random walk

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pages: 369 words: 128,349

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing by Vijay Singal

3Com Palm IPO, Andrei Shleifer, AOL-Time Warner, asset allocation, book value, buy and hold, capital asset pricing model, correlation coefficient, cross-subsidies, currency risk, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, fixed income, index arbitrage, index fund, information asymmetry, information security, junk bonds, liberal capitalism, locking in a profit, Long Term Capital Management, loss aversion, low interest rates, margin call, market friction, market microstructure, mental accounting, merger arbitrage, Myron Scholes, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, short squeeze, survivorship bias, Tax Reform Act of 1986, transaction costs, uptick rule, Vanguard fund

A trading strategy set up to exploit these price patterns can earn an abnormal return of 1.25 percent to 3 percent after transaction costs, that is, 15–36 percent annually. 57 58 Beyond the Random Walk Evidence The evidence on the short-term price drift begins with a large price change as an indicator of a strong signal of new information. A large price change (stock return) can be defined in two different ways: as an absolute return or as a relative return. A 10 percent absolute return or a change in price from $50 to $45 (or to $55) obviously seems like a large price change. However, a 10 percent return in one day may be relatively common for riskier stocks, such as technology stocks.

Since an individual stock’s volume, like its return, depends on market volume, it is appropriate to adjust the stock’s trading volume by market volume. The results in panel A of Table 4.2 reveal two return patterns. First, price changes that are accompanied by high volume have price continuations. Price increases with high volume are followed by subsequent increases of 0.20 percent and 0.95 percent over five-day 61 62 Beyond the Random Walk Table 4.2 Returns Following Large Price Changes, High Volume, and News Price Change High Volume Public News Sample Size Abnormal Return: Days 1–5 (%) Abnormal Return: Days 1–20 (%) Panel A: Large price changes with and without high volume 1. 2. 3. 4. Increase Increase Decrease Decrease Yes No Yes No — — — — 1,477 1,442 1,142 812 0.20 –0.71 –0.66 0.49 0.95 –0.67 –0.65 –0.13 Panel B: Large price changes, high volume, and public news 5. 6. 7. 8.

Column 2 = Column 1 + the price on the day before the event is greater than $10. c Column 3 = Column 2 + volume in the top 6 out of previous 60 days. d Column 4 = Column 3 + return in the top three among the last year. e Column 5 = Column 4 but excluding continuations of earlier events. The original data set is from Quotes-Plus. b Beyond the Random Walk Table 4.4 Short-Term Price Drift satisfy these criteria each day. The total number of events selected for April 2002 is 114, including 55 price increases and 59 price declines. The average price increase is 12.9 percent on the event day, while the price decrease is –16.9 percent.


pages: 209 words: 13,138

Empirical Market Microstructure: The Institutions, Economics and Econometrics of Securities Trading by Joel Hasbrouck

Alvin Roth, barriers to entry, business cycle, conceptual framework, correlation coefficient, discrete time, disintermediation, distributed generation, experimental economics, financial intermediation, index arbitrage, information asymmetry, interest rate swap, inventory management, market clearing, market design, market friction, market microstructure, martingale, payment for order flow, power law, price discovery process, price discrimination, quantitative trading / quantitative finance, random walk, Richard Thaler, second-price auction, selection bias, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, two-sided market, ultimatum game, zero-sum game

This chapter develops the Roll model by first presenting the randomwalk model, which describes the evolution of the fundamental security value. The discussion then turns to bid and ask quotes, order arrivals, and the resulting transaction price process. 3.2 The Random-Walk Model of Security Prices Before financial economists began to concentrate on the trading process, the standard statistical model for a security price was the random walk. THE ROLL MODEL OF TRADE PRICES The random-walk model is no longer considered to be a complete and valid description of short-term price dynamics, but it nevertheless retains an important role as a model for the fundamental security value. Furthermore, some of the lessons learned from early statistical tests of the randomwalk hypothesis have ongoing relevance in modeling market data.

Here we ask what can be inferred starting from a moving average representation of price changes that is not of order one but is instead of arbitrary order: pt = θ(L)εt . The only economic structure we impose on the prices is pt = mt + st where mt follows a random walk (mt = mt−1 + wt ) and st is (as in the preceding section) a tracking error that may be serially correlated and partially or completely correlated with wt . The observable variable, pt , is thus decomposed into a random-walk and a covariance-stationary error. Random-walk decompositions were originally developed and applied in macroeconomic settings, where the random-walk component is considered the long-term trend and the stationary component impounds transient business cycle effects.

In macroeconomics applications, random-walk decompositions are usually called permanent/transitory. The random-walk terminology is used here to stress the financial economics connection to the random-walk efficient prices. The permanent/transitory distinction is in some respects more descriptive, however, of the attributions that we’re actually making. From a microstructure perspective, the key results expand on those demonstrated for the generalized Roll model: The moving average representation for the price changes suffices to identify the variance of the 2 , the projection of the efficient price on past price implicit efficient price σw changes, and a lower bound on the variance of the difference between the transaction price and the efficient price.


pages: 345 words: 86,394

Frequently Asked Questions in Quantitative Finance by Paul Wilmott

Abraham Wald, Albert Einstein, asset allocation, beat the dealer, Black-Scholes formula, Brownian motion, butterfly effect, buy and hold, capital asset pricing model, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discrete time, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, iterative process, lateral thinking, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, power law, quantitative trading / quantitative finance, random walk, regulatory arbitrage, risk free rate, risk/return, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, transaction costs, urban planning, value at risk, volatility arbitrage, volatility smile, Wiener process, yield curve, zero-coupon bond

Its simplicity allows calculations and analysis that would not be possible with other processes. For example, in option pricing it results in simple closed-form formulæ for the prices of vanilla options. It can be used as a building block for random walks with characteristics beyond those of BM itself. For example, it is used in the modelling of interest rates via mean-reverting random walks. Higher-dimensional versions of BM can be used to represent multi-factor random walks, such as stock prices under stochastic volatility. One of the unfortunate features of BM is that it gives returns distributions with tails that are unrealistically shallow.

Where possible I give dates, name names and refer to the original sources.1 1827 Brown The Scottish botanist, Robert Brown, gave his name to the random motion of small particles in a liquid. This idea of the random walk has permeated many scientific fields and is commonly used as the model mechanism behind a variety of unpredictable continuous-time processes. The lognormal random walk based on Brownian motion is the classical paradigm for the stock market. See Brown (1827). 1900 Bachelier Louis Bachelier was the first to quantify the concept of Brownian motion. He developed a mathematical theory for random walks, a theory rediscovered later by Einstein. He proposed a model for equity prices, a simple normal distribution, and built on it a model for pricing the almost unheard of options.

It is difficult to overstate the importance of Itô’s lemma in quantitative finance. It is used in many of the derivations of the Black-Scholes option pricing model and the equivalent models in the fixed-income and credit worlds. If we have a random walk model for a stock price S and an option on that stock, with value V(S, t), then Itô’s lemma tells us how the option price changes with changes in the stock price. From this follows the idea of hedging, by matching random fluctuations in S with those in V . This is important both in the theory of derivatives pricing and in the practical management of market risk. Even if you don’t know how to prove Itô’s lemma you must be able to quote it and use the result.


pages: 543 words: 153,550

Model Thinker: What You Need to Know to Make Data Work for You by Scott E. Page

Airbnb, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Alvin Roth, assortative mating, behavioural economics, Bernie Madoff, bitcoin, Black Swan, blockchain, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Checklist Manifesto, computer age, corporate governance, correlation does not imply causation, cuban missile crisis, data science, deep learning, deliberate practice, discrete time, distributed ledger, Easter island, en.wikipedia.org, Estimating the Reproducibility of Psychological Science, Everything should be made as simple as possible, experimental economics, first-price auction, Flash crash, Ford Model T, Geoffrey West, Santa Fe Institute, germ theory of disease, Gini coefficient, Higgs boson, High speed trading, impulse control, income inequality, Isaac Newton, John von Neumann, Kenneth Rogoff, knowledge economy, knowledge worker, Long Term Capital Management, loss aversion, low skilled workers, Mark Zuckerberg, market design, meta-analysis, money market fund, multi-armed bandit, Nash equilibrium, natural language processing, Network effects, opioid epidemic / opioid crisis, p-value, Pareto efficiency, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, Phillips curve, power law, pre–internet, prisoner's dilemma, race to the bottom, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Solow, school choice, scientific management, sealed-bid auction, second-price auction, selection bias, six sigma, social graph, spectrum auction, statistical model, Stephen Hawking, Supply of New York City Cabdrivers, systems thinking, tacit knowledge, The Bell Curve by Richard Herrnstein and Charles Murray, The Great Moderation, the long tail, The Rise and Fall of American Growth, the rule of 72, the scientific method, The Spirit Level, the strength of weak ties, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, Tragedy of the Commons, urban sprawl, value at risk, web application, winner-take-all economy, zero-sum game

Though unnecessary for small networks such as these, this method becomes useful on larger networks, like the World Wide Web or large email networks. Random Walks and Efficient Markets Stock prices prove to be nearly normal random walks with a positive drift to capture gains in the market. Many individual stock prices also are approximately random. Figure 13.3 shows the daily stock price data for Facebook for the year following its initial public offering on May 18, 2012. Facebook was offered at $42 per share. By June 1, 2012, the price had fallen to $28.89. One year later the price had fallen to $24.63. The figure also shows a random walk calibrated to have similar variation. Figure 13.3: Facebook Daily Stock Price June 2012–June 2013 vs. a Random Walk We can apply statistical tests to the sequence of Facebook share prices to see if it satisfies the assumptions of a normal random walk.

Figure 13.3: Facebook Daily Stock Price June 2012–June 2013 vs. a Random Walk We can apply statistical tests to the sequence of Facebook share prices to see if it satisfies the assumptions of a normal random walk. First, the price should go up and down with equal probability. In the 249 trading days covered, Facebook’s stock price went down on 127 days, or 51% of the time. Second, in a random walk, the probability of an increase should be independent of an increase that occurred in the previous period. Facebook’s stock price moved in the same direction on consecutive days 54% of the time. Finally, the expected longest streak of moves in the same direction should be eight days.

Others extended this thinking to create the efficient market hypothesis, which states that at any moment in time the price of a stock captures all relevant information, and future prices must follow a random walk. The efficient market hypothesis rests on paradoxical logic.18 Determining an accurate price requires time and effort. A financial analyst must gather data and construct models. If prices followed a random walk, those activities would have no expected return. However, if no one expends effort to estimate prices, then prices will become inaccurate and the sidewalk will be covered in hundred-dollar bills. In brief, the Grossman and Stiglitz paradox states that if investors believe in the efficient market hypothesis, they stop analyzing, making markets inefficient.


pages: 461 words: 128,421

The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street by Justin Fox

"Friedman doctrine" OR "shareholder theory", Abraham Wald, activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, AOL-Time Warner, asset allocation, asset-backed security, bank run, beat the dealer, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, card file, Carl Icahn, Cass Sunstein, collateralized debt obligation, compensation consultant, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, democratizing finance, Dennis Tito, discovery of the americas, diversification, diversified portfolio, Dr. Strangelove, Edward Glaeser, Edward Thorp, endowment effect, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Glass-Steagall Act, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Bogle, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, market design, Michael Milken, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, power law, prediction markets, proprietary trading, prudent man rule, pushing on a string, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, road to serfdom, Robert Bork, Robert Shiller, rolodex, Ronald Reagan, seminal paper, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, Skinner box, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, stocks for the long run, tech worker, The Chicago School, The Myth of the Rational Market, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, Thorstein Veblen, Tobin tax, transaction costs, tulip mania, Two Sigma, Tyler Cowen, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra

Over the forty-two years of data he examined, Working found that the speculators had, as a group, lost money.32 Moving on, Working began to study the movements of futures prices. He found a few interesting patterns. “Wheat prices tend strongly to rise during a season following three of low average price and to decline during a season following three of high average price,” he reported in 1931. “The relation is attributed partly to a tendency for price judgments of wheat traders to be unduly influenced by memory of prices in recent years.”33 Much of what Working saw in price movements, though, seemed random. The phrase “random walk” appears to have been coined in 1905, in an exchange in the letters pages of the English journal Nature concerning the mathematical description of the meanderings of a hypothetical drunkard.34 Most early studies of economic data had been a search not for drunken meanderings but for recognizable patterns and, not surprisingly, many were found.

Moore finished his dissertation in 1962, and it was published as “Some Characteristics of Changes in Common Stock Prices,” in The Random Character of Stock Prices, Paul Cootner, ed. (Cambridge, Mass.: MIT Press, 1964), 139–61. 21. Robert A. Levy, “Random Walks: Reality or Myth,” Financial Analysts Journal (Nov.–Dec. 1967): 69–77. 22. Michael C. Jensen, “Random Walks: Reality or Myth—Comment,” Financial Analysts Journal (Nov.–Dec. 1967): 84. 23. Jensen, “Random Walks,” 81. 24. Eugene F. Fama, Lawrence Fisher, Michael C. Jensen, Richard Roll, “The Adjustment of Stock Prices to New Information,” International Economic Review (Feb. 1969): 1–21.

One reason why not had been noted by Fred Macaulay at that statisticians’ dinner in 1925. If stock price changes followed a true random walk, prices could become negative, a fate that the entire limited-liability structure of the modern corporation is designed to prevent. Another problem was that if price movements were truly random, the price of a pea might follow the same trajectory as that of a share of IBM stock. Instead, stock prices trended upward with the growth of a company and of the economy as a whole. The price of a pea did not. So it wasn’t that “the mathematical expectation of the speculator was zero,” as Bachelier had posited.


pages: 482 words: 121,672

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition) by Burton G. Malkiel

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, beat the dealer, Bernie Madoff, bitcoin, book value, butter production in bangladesh, buttonwood tree, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Detroit bankruptcy, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, equity risk premium, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial engineering, financial innovation, financial repression, fixed income, framing effect, George Santayana, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Japanese asset price bubble, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, margin call, market bubble, Mary Meeker, money market fund, mortgage tax deduction, new economy, Own Your Own Home, PalmPilot, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Salesforce, short selling, Silicon Valley, South Sea Bubble, stock buybacks, stocks for the long run, sugar pill, survivorship bias, Teledyne, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond, zero-sum game

There is some momentum in stock prices. When good news arises, investors often only partially adjust their estimates of the appropriate price of the stock. Slow adjustment can make stock prices rise steadily for a period, imparting a degree of momentum. The failure of stock prices to measure up perfectly to the definition of a random walk led the financial economists Andrew Lo and A. Craig MacKinlay to publish a book entitled A Non-Random Walk Down Wall Street. In addition to some evidence of short-term momentum, there has been a long-run uptrend in most averages of stock prices in line with the long-run growth of earnings and dividends.

The life-cycle investment guide described in Part Four gives individuals of all age groups specific portfolio recommendations for meeting their financial goals, including advice on how to invest in retirement. WHAT IS A RANDOM WALK? A random walk is one in which future steps or directions cannot be predicted on the basis of past history. When the term is applied to the stock market, it means that short-run changes in stock prices are unpredictable. Investment advisory services, earnings forecasts, and complicated chart patterns are useless. On Wall Street, the term “random walk” is an obscenity. It is an epithet coined by the academic world and hurled insultingly at the professional soothsayers.

The earliest empirical work on the behavior of stock prices, going back to the early 1900s, found that a sequence of random numbers had the same appearance as a time series of stock prices. But even though the earliest studies supported a general finding of randomness, more recent work indicated that the random-walk model does not strictly hold. Some patterns appear to exist in the development of stock prices. Over short holding periods, there is some evidence of momentum in the stock market. Increases in stock prices are slightly more likely to be followed by further increases than by price declines. For longer holding periods, reversion to the mean appears to be present.


pages: 338 words: 106,936

The Physics of Wall Street: A Brief History of Predicting the Unpredictable by James Owen Weatherall

Alan Greenspan, Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Apollo 11, Asian financial crisis, bank run, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, butterfly effect, buy and hold, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, coastline paradox / Richardson effect, collateralized debt obligation, collective bargaining, currency risk, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, Financial Modelers Manifesto, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, Jim Simons, John Nash: game theory, junk bonds, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Market Wizards by Jack D. Schwager, martingale, Michael Milken, military-industrial complex, Myron Scholes, Neil Armstrong, new economy, Nixon triggered the end of the Bretton Woods system, Paul Lévy, Paul Samuelson, power law, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk-adjusted returns, Robert Gordon, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, Stuart Kauffman, The Chicago School, The Myth of the Rational Market, tulip mania, Vilfredo Pareto, volatility smile

By the time the Cootner book was published in 1964, the idea that market prices follow a random walk was well entrenched, and many economists recognized that Bachelier was responsible for it. But the random walk model wasn’t the punch line of Bachelier’s thesis. He thought of it as preliminary work in the service of his real goal, which was developing a model for pricing options. An option is a kind of derivative that gives the person who owns the option the right to buy (or sometimes sell) a specific security, such as a stock or bond, at a predetermined price (called the strike price), at some future time (the expiration date).

How much you gain or lose is easy to work out, since it’s just the difference between the strike price on the option and the market price for the underlying security. But with the random walk model in hand, Bachelier also knew how to calculate the probabilities that a given stock would exceed (or fail to exceed) the strike price in a given time window. Putting these two elements together, Bachelier showed just how to calculate the fair price of an option. Problem solved. There’s an important point to emphasize here. One often hears that markets are unpredictable because they are random. There is a sense in which this is right, and Bachelier knew it. Bachelier’s random walk model indicates that you can’t predict whether a given stock is going to go up or down, or whether your portfolio will profit.

Black had read Cootner’s collection of essays on the randomness of markets, and so he was familiar with Bachelier’s and Osborne’s work on the random walk hypothesis. This gave him a way to model how the underlying stock prices changed over time — which in turn gave him a way to understand how options prices must change over time, given the link he had discovered between options prices and stock prices. Once Black had found this fundamental relationship between the price of a stock, the price of an option on that stock, and the risk-free interest rate, it was just a few steps of algebra for him to derive an equation for the value of the option, by relating the risk premium on the stock to the risk premium on the option.


pages: 416 words: 118,592

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing by Burton G. Malkiel

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, book value, BRICs, butter production in bangladesh, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial engineering, financial innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Japanese asset price bubble, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, margin call, market bubble, Mary Meeker, money market fund, mortgage tax deduction, new economy, Own Your Own Home, PalmPilot, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stock buybacks, stocks for the long run, sugar pill, survivorship bias, The Myth of the Rational Market, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

There is some momentum in stock prices. When good news arises, investors often only partially adjust their estimates of the appropriate price of the stock. Slow adjustment can make stock prices rise steadily for a period, imparting a degree of momentum. The failure of stock prices to measure up perfectly to the definition of a random walk led the financial economists Andrew Lo and A. Craig MacKinlay to publish a book entitled A Non-Random Walk Down Wall Street. In addition to some evidence of short-term momentum, there has been a long-run uptrend in most averages of stock prices in line with the long-run growth of earnings and dividends.

The life-cycle investment guide described in Part Four gives individuals of all age groups specific portfolio recommendations for meeting their financial goals, including advice on how to invest in retirement. WHAT IS A RANDOM WALK? A random walk is one in which future steps or directions cannot be predicted on the basis of past history. When the term is applied to the stock market, it means that short-run changes in stock prices are unpredictable. Investment advisory services, earnings forecasts, and complicated chart patterns are useless. On Wall Street, the term “random walk” is an obscenity. It is an epithet coined by the academic world and hurled insultingly at the professional soothsayers.

He believed that every investor should post the following Latin maxim above his desk: Res tantum valet quantum vendi potest. (A thing is worth only what someone else will pay for it.) HOW THE RANDOM WALK IS TO BE CONDUCTED With this introduction out of the way, come join me for a random walk through the investment woods, with an ultimate stroll down Wall Street. My first task will be to acquaint you with the historical patterns of pricing and how they bear on the two theories of pricing investments. It was Santayana who warned that if we did not learn the lessons of the past we would be doomed to repeat the same errors. Therefore, in the pages to come I will describe some spectacular crazes—both long past and recently past.


pages: 733 words: 179,391

Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

Alan Greenspan, Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, bitcoin, Bob Litterman, Bonfire of the Vanities, bonus culture, break the buck, Brexit referendum, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, carbon tax, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, confounding variable, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, democratizing finance, Diane Coyle, diversification, diversified portfolio, do well by doing good, double helix, easy for humans, difficult for computers, equity risk premium, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, information security, interest rate derivative, invention of the telegraph, Isaac Newton, it's over 9,000, James Watt: steam engine, Jeff Hawkins, Jim Simons, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, language acquisition, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, megaproject, merger arbitrage, meta-analysis, Milgram experiment, mirror neurons, money market fund, moral hazard, Myron Scholes, Neil Armstrong, Nick Leeson, old-boy network, One Laptop per Child (OLPC), out of africa, p-value, PalmPilot, paper trading, passive investing, Paul Lévy, Paul Samuelson, Paul Volcker talking about ATMs, Phillips curve, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Solow, Sam Peltzman, Savings and loan crisis, seminal paper, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, subprime mortgage crisis, survivorship bias, systematic bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, uptick rule, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

Changes in the weather also affected the price of wheat from day to day. However, in 1953 the economist Maurice Kendall showed that wheat prices appeared to move randomly, according to his statistical tests.19 Samuelson spotted a paradox: if the weather influenced the price of grain, how could the price of grain follow a random walk?20 Samuelson knew that weather patterns, while complicated, did not behave randomly, and certainly the seasons didn’t follow each other randomly either. It seemed to Samuelson that Bachelier’s Random Walk actually proved too much. Samuelson resolved this difficulty in a very quick and elegant way, characteristic of his personal style in economics.

More technically speaking, if you measure the variance of a random walk over a ten-day window, where “variance” is a mathematically precise way of measuring these fluctuations, it will have ten times the variance of a one-day window. What does this mean financially? Since Harry Markowitz’s development of portfolio theory in 1952, most investors equate the variance of a stock’s price with its risk. In other words, the more disorderly a stock’s staggerings in the market, the riskier it is. But the drunkard’s walk of stock prices leads to a sobering conclusion for long-term investors. If stock prices follow a random walk, the mathematics of the variance means that investment risk will increase in lockstep with the length of the investment period.4 Our test of the Random Walk Hypothesis was to check this relationship: was the variance of two-week stock returns exactly two times the variance of weekly returns as it should have been?

Despite our best efforts, however, we were unable to explain away the evidence against the Random Walk Hypothesis. At first, we thought our results might be due to the fact that we used weekly returns, since prior studies that supported the Random Walk Hypothesis used daily returns. But we soon discovered that the case against the random walk was equally persuasive with daily returns. We looked into possible sources of bias in the market data itself, such as subtle errors introduced by incorrectly assuming that all closing prices occur at the same time of day. (An active stock like Apple will trade until the closing bell, 4:00 p.m.


pages: 442 words: 39,064

Why Stock Markets Crash: Critical Events in Complex Financial Systems by Didier Sornette

Alan Greenspan, Asian financial crisis, asset allocation, behavioural economics, Berlin Wall, Black Monday: stock market crash in 1987, Bretton Woods, Brownian motion, business cycle, buy and hold, buy the rumour, sell the news, capital asset pricing model, capital controls, continuous double auction, currency peg, Deng Xiaoping, discrete time, diversified portfolio, Elliott wave, Erdős number, experimental economics, financial engineering, financial innovation, floating exchange rates, frictionless, frictionless market, full employment, global village, implied volatility, index fund, information asymmetry, intangible asset, invisible hand, John von Neumann, joint-stock company, law of one price, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, market design, market fundamentalism, mental accounting, moral hazard, Network effects, new economy, oil shock, open economy, pattern recognition, Paul Erdős, Paul Samuelson, power law, quantitative trading / quantitative finance, random walk, risk/return, Ronald Reagan, Schrödinger's Cat, selection bias, short selling, Silicon Valley, South Sea Bubble, statistical model, stochastic process, stocks for the long run, Tacoma Narrows Bridge, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, Tobin tax, total factor productivity, transaction costs, tulip mania, VA Linux, Y2K, yield curve

Top panel: Realization of a bubble price Bt as a function of time constructed from the “singular inverse random walk.” This corresponds to a specific realization of the random numbers used in generating the random walks W t represented in the second panel. The top panel is obtained by taking a power of the inverse of a constant Wc , here taken equal to 1 minus the random walk shown in the second panel. In this case, when the random walk approaches 1, the bubble diverges. Notice the similarity between the trajectories shown in the top (Bt) and second (W t) panels as long as the random walk W t does not approach the value Wc = 1 too much.

In other words, the liquidity and efficiency of markets control the degree of correlation that is compatible with a near absence of arbitrage opportunity. THE EFFICIENT MARKET HYPOTHESIS AND THE RANDOM WALK Such observations have been made for a long time. A pillar of modern finance is the 1900 Ph.D. thesis dissertation of Louis Bachelier, in Paris, and his subsequent work, especially in 1906 and 1913 [25]. To account for the apparent erratic motion of stock market prices, he proposed that price trajectories are identical to random walks. The Random Walk The concept of a random walk is simple but rich for its many applications, not only in finance but also in physics and the description of natural phenomena.

for investment targets: if the price variations are really like tossing coins at random, it seems impossible to know what the direction of the price will be between today and tomorrow, or between any two other times. A qualifying scaling property of random walks. To get a more quantitative feeling for how well the random walk model can constitute a good model of stock market prices, consider Figures 2.3, 2.4, and 2.5 of return time series at three very different time scales (minute, day, and month). The most important prediction of the random walk model is that the square of the fluctuations of its position should increase in proportion to the time scale. This is equivalent to saying that the typical amplitude of its position is proportional to the square root of the time scale.


The Armchair Economist: Economics and Everyday Life by Steven E. Landsburg

Albert Einstein, Arthur Eddington, business cycle, diversified portfolio, Dutch auction, first-price auction, German hyperinflation, Golden Gate Park, information asymmetry, invisible hand, junk bonds, Kenneth Arrow, low interest rates, means of production, price discrimination, profit maximization, Ralph Nader, random walk, Ronald Coase, Sam Peltzman, Savings and loan crisis, sealed-bid auction, second-price auction, second-price sealed-bid, statistical model, the scientific method, Unsafe at Any Speed

Each day the wheel spins, and the little ball's destination determines not today's price, but the difference between yesterday's price and today's. If the current price is $10 and the ball lands on -2, then the price falls to $8; if instead it lands on 5, then the price rises to $15.* *An even more accurate image is that the roulette wheel determines not the actual price change but the percentage price change; when the ball lands on -2 the stock price falls 2%, and when the ball lands on 5 the stock price rises 5%. The image I've adopted in the text is slightly easier to think about and close enough to true that nothing interesting will be lost in the discussion. 188 Random Walks and Stock Market Prices 189 With a random walk every change is permanent.

I had misinterpreted the word random to mean "unrelated to anything else in the world," which is why I thought that the random walk theory denied that IBM's behavior could affect its stock price. But one random event can be perfectly correlated with another. Great corporate blunders arrive randomly, and the corresponding stock price changes arrive along with them. Economists believe that stock market prices behave a lot like random walks most of the time. That is, we believe that price changes (not prices) usually have the same statistical characteristics as the series of numbers generated by a roulette wheel. If prices were random, as I once erroneously believed, then today's price would be useless as a predictor of tomorrow's.

Send me a note. I'll be in my well-worn armchair, thinking about things. CHAPTER 20 RANDOM WALKS AND STOCK MARKET PRICES A Primer for Investors When I was young and first heard that stock market prices follow random walks, I was incredulous. Did this mean that IBM might as well replace its corporate officers with underprivileged eight-year-olds? My question was born of naivete', and of considerable ignorance. I've learned a lot in the interim. One thing I've learned is that a random walk is not a theory of prices; it is a theory of price changes. In that distinction lies a world of difference. My original (entirely wrong) conception invoked a roulette wheel as its central image.


Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals by David Aronson

Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, book value, butter production in bangladesh, buy and hold, capital asset pricing model, cognitive dissonance, compound rate of return, computerized trading, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, equity risk premium, feminist movement, Great Leap Forward, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, managed futures, mental accounting, meta-analysis, p-value, pattern recognition, Paul Samuelson, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk free rate, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, sugar pill, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

Illusory Trends and Patterns in Financial Data Statistician Harry Roberts said that technical analysts fall victim to the illusion of patterns and trends for two possible reasons. First, “the usual method of graphing stock prices gives a picture of successive (price) levels rather than of price changes and levels can give an artificial appearance of pattern or trend. Second, chance behavior itself produces patterns that invite spurious interpretations.”128 Roberts showed that the same chart patterns to which TA attaches importance129 appear with great regularity in random walks. A random walk is, by definition, devoid of authentic trends, patterns, or exploitable order of any kind. However, Roberts’ random-walk charts displayed headand-shoulder tops and bottoms, triangle tops and bottoms, triple tops and bottoms, trend channels, and so forth.

Fortunately, theories developed in the field of behavioral finance and elsewhere are beginning to offer the theoretical support TA needs. THE ENEMY’S POSITION: EFFICIENT MARKETS AND RANDOM WALKS Before discussing theories that explain why nonrandom price movements should exist, we need to consider the enemy’s position, the EMH. Recently, some have argued that EMH does not necessarily imply that prices follow unpredictable random walks,8 and that efficient markets and price predictability can coexist. However, the pioneers of EMH asserted that random walks were a necessary consequence of efficient markets. This section states their case and examines its weaknesses. What Is an Efficient Market?

It occurs when an observer has no prior belief about whether the process generating the data is random or nonrandom The clustering illusion is the misperception of order (nonrandomness) in data that is actually a random walk. Again, imagine someone observing the outcomes of a process that is truly a random walk trying to determine if the process is random or nonrandom (orderly, systematic).71 Recall that small samples of random walks often appear more trended (clustered) than common sense would lead us to expect (the hot hand in basketball). As a result of the clustering illusion, a sequence of positive price or earnings changes is wrongly interpreted as a legitimate trend, when it is nothing more than an ordinary streak in a random walk. Social Factors: Imitative Behavior, Herding, and Information Cascades72 We have just seen how investor behavior viewed at the level of the individual investor can explain several types of systematic price movement.


Commodity Trading Advisors: Risk, Performance Analysis, and Selection by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

Asian financial crisis, asset allocation, backtesting, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, financial engineering, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, managed futures, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Performance of Mutual Funds in the Period, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, tail risk, technology bubble, transaction costs, value at risk, zero-sum game

However, the robustness of the ADF test is increased when lags are used. If a series is found to be nonstationary by the ADF test, it does not necessarily imply that it behaves like a random walk, because random walks are but one example of nonstationary time series. Fortunately, the ADF test also can be used to test specifically for random walks. No CTA strategy that relies solely on historical prices can be continuously profitable if markets are efficient and the random walk hypothesis holds true. In this case, future percent changes in NAVs would be entirely unrelated by the historical performance (Pindyck and Rubinfeld 1998).

The effectiveness of CTAs in enhancing risk-return characteristics of portfolios could be compromised when pure random walk behavior is identified. T INTRODUCTION This chapter investigates whether monthly percent changes in net asset values (NAVs) of commodity trading advisor (CTA) classifications follow random walks. Previous econometric studies of financial time series have employed unit root tests, such as the Augmented Dickey-Fuller test (ADF), to identify random walk behavior in stock prices and market indices, for example. The characteristics of CTAs are such that investment into this alternative investment class can enhance portfolio returns, but these characteristics are likely to be mitigated if pure random walk behavior is present because that would imply a lack of evidence of value added to the portfolio (differential manager skill).

Thus, the performance of CTAs depends not only on price movements, but also on the managers’ ability to identify them. One possible explanation for random walk behavior during the examination period is due to the fact that traditional CTAs make large profits during extreme market movements, themselves random events. Their correlations may be more accurate and stable if they are used as a hedge against short volatility exposure. The discretionary, currency, and European traders trade in periods of high liquidity, which has been the case since 1995. We found that only one class, diversified, did not behave as a random walk, likely since trends in a diversified portfolio are stable, although they may not produce sufficient profits to satisfy the expectations of all investors.


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The Misbehavior of Markets: A Fractal View of Financial Turbulence by Benoit Mandelbrot, Richard L. Hudson

Alan Greenspan, Albert Einstein, asset allocation, Augustin-Louis Cauchy, behavioural economics, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black-Scholes formula, British Empire, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, carbon-based life, discounted cash flows, diversification, double helix, Edward Lorenz: Chaos theory, electricity market, Elliott wave, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, Fellow of the Royal Society, financial engineering, full employment, Georg Cantor, Henri Poincaré, implied volatility, index fund, informal economy, invisible hand, John Meriwether, John von Neumann, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market microstructure, Myron Scholes, new economy, paper trading, passive investing, Paul Lévy, Paul Samuelson, plutocrats, power law, price mechanism, quantitative trading / quantitative finance, Ralph Nelson Elliott, RAND corporation, random walk, risk free rate, risk tolerance, Robert Shiller, short selling, statistical arbitrage, statistical model, Steve Ballmer, stochastic volatility, transfer pricing, value at risk, Vilfredo Pareto, volatility smile

Wars start, peace returns, economies expand, firms fail—all these come and go, affecting prices. But the fundamental process by which prices react to news does not change. A mathematician would say market processes are “stationary.” This contradicts some would-be reformers of the random-walk model who explain the way volatility clusters by asserting that the market is in some way changing, that volatility varies because the pricing mechanism varies. Wrong. A striking example: My analysis of cotton prices over the past century shows the same broad pattern of price variability at the turn of the last century when prices were unregulated, as there was in the 1930s when prices were regulated as part of the New Deal.

But evaluating risk is another matter entirely. Step back a moment. The classic Random Walk model makes three essential claims. First is the so-called martingale condition: that your best guess of tomorrow’s price is today’s price. Second is a declaration of independence: that tomorrow’s price is independent of past prices. Third is a statement of normality: that all the price changes taken together, from small to large, vary in accordance with the mild, bell-curve distribution. In my view, that is two claims too many. The first, though not proven by the data, is at least not (much) contradicted by it; and it certainly helps, in an intuitive way, to explain why we so often guess the market wrong.

Englewood Cliffs, NJ: Prentice-Hall International Inc. Alexander, Sidney S. 1961. Price movements in speculative markets: Trends or random walks? Industrial Management Review 2 (2): 7-26. Alexander, Sidney S. 1964. Price movements in speculative markets: Trends or random walks, number 2. Industrial Management Review 5 (2): 25-46. Alligood, Kathleen T., Tim D. Sauer, and James A. Yorke. 1996. Chaos: An Introduction to Dynamical Systems. New York: Springer-Verlag. Alvarez-Ramirez, Jose, Myrian Cisneros, Carlos Ibarra-Valdez, and Angel Soriano. 2002. Multifractal Hurst analysis of crude oil prices. Physica A 313: 651-670. Babeau, André and Teresa Sbano. 2002.


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Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street by William Poundstone

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Albert Einstein, anti-communist, asset allocation, Bear Stearns, beat the dealer, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bletchley Park, Brownian motion, buy and hold, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, Edward Thorp, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, Henry Singleton, high net worth, index fund, interest rate swap, Isaac Newton, Johann Wolfgang von Goethe, John Meriwether, John von Neumann, junk bonds, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Michael Milken, Myron Scholes, New Journalism, Norbert Wiener, offshore financial centre, Paul Samuelson, publish or perish, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ronald Reagan, Rubik’s Cube, short selling, speech recognition, statistical arbitrage, Teledyne, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, value at risk, zero-coupon bond, zero-sum game

The butterfly whose flapping causes a hurricane could lead to the sinking of a yacht full of Sperry executives, pummeling the stock’s price. How can anyone predict such contingencies systematically? Then Thorp thought of the random walk model. Assume that there is no possible way of predicting the events that move stock prices. Then buying a stock option is placing a bet on a random walk. Thorp knew that there were already precise methods for calculating the probability distributions of random walks. They depend on the average size of the random motions—in this case, how much a stock’s price changes, up or down, per day. Thorp did some computations. He found that most warrants were priced like carnival games.

The mathematical treatment of Brownian motion that Einstein published in 1905 was similar to, but less advanced than, the one that Bachelier had already derived for stock prices. Einstein, like practically everyone else, had never heard of Bachelier. The Random Walk Cosa Nostra SAMUELSON ADOPTED Bachelier’s ideas into his own thinking. Characteristically, he did everything he could to acquaint people with Bachelier’s genius. Just as characteristically, Samuelson called Bachelier’s views “ridiculous.” Huh? Samuelson spotted a mistake in Bachelier’s work. Bachelier’s model had failed to consider that stock prices cannot fall below zero. Were stock price changes described by a conventional random walk, it would be possible for prices to wander below zero, ending up negative.

This spoiled Bachelier’s neat model. Samuelson found a simple fix. He suggested that each day, a stock’s price is multiplied by a random factor (like 98 or 105 percent) rather than increased or decreased by a random amount. A stock might, for instance, be just as likely to double in price as to halve in price over a certain time frame. This model, called a log-normal or geometric random walk, prevents stocks from taking on negative values. To Samuelson, the random walk suggested that the stock market was a glorified casino. If the daily movements of stock prices are as unpredictable as the daily lotto numbers, then maybe people who make fortunes in the market are like people who win lotteries.


The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk by William J. Bernstein

asset allocation, backtesting, book value, buy and hold, capital asset pricing model, commoditize, computer age, correlation coefficient, currency risk, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, index arbitrage, index fund, intangible asset, John Bogle, junk bonds, Long Term Capital Management, p-value, passive investing, prediction markets, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, South Sea Bubble, stocks for the long run, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Wayback Machine, Yogi Berra, zero-coupon bond

Eventually long-term mean reversion occurs to correct these excesses. Over 2 decades ago, Eugene Fama made a powerful case that security price changes could not be predicted, and Burton Malkiel introduced the words “random walk” into the popular investing lexicon. Unfortunately, in a truly random-walk world, there is no advantage to portfolio rebalancing. If you rebalance, you profit only when the frogs in your portfolio turn into princes, and vice versa. In the real world, fortunately, there are subtle departures in random-walk behavior that the asset allocator-investor can exploit. Writer and money manager Ken Fisher calls this change in asset desirability, and the resultant short-term momentum and long-term mean reversion, the “Wall Street Waltz.”

As we’ve already seen, stock price movements are essentially an unpredictable “random walk.” Interestingly, it turns out that earnings growth also exhibits random-walk behavior; a company with good earnings growth this year is quite likely to have poor earnings growth next year (and vice versa). In other words, this year’s growth stock is quite likely to become next year’s value stock, at great cost to its shareholders. Contrariwise, a value stock with poor earnings growth will frequently surprise the market with strong earnings growth, with an agreeable change in P/E and price. This typically happens to only a few stocks in a value portfolio in a given year, but the effects on total portfolio performance are still dramatic.

.): correlation coefficients, 71–74 international diversification with small stocks, 74–75 risk tolerance and, 79–80, 143 three-step approach to, 75–83 Out of sample, 87 Overbalancing, 138 Overconfidence, 139–140 P/B ratio (See Book value) P/E ratio: data on ranges of, 113, 114 earnings yield as reverse of, 119 in new era of investing, 124 in value investing, 112, 119–120 Pacific Rim stocks, 19, 20, 21, 25, 55–59, 147, 156 Pension funds, 103 (See also Institutional investors) Perfectly reasonable price (PRP), 127–128 Performance measurement: alpha in, 89–90, 98 three-factor model in, 123–124 (See also Benchmarking) Perold, Andre, 141 Persistence of performance, 85–88 Peters, Tom, 118 Piscataqua Research, 103 Policy allocation, 59 Portfolio insurance, 141 Portfolio Selection (Markowitz), 177–178 Precious metals stocks, 19–20, 21, 48, 55, 57, 59 Price, Michael, 162 Professional investors (See Institutional investors) Prudent man test, 60 Random Walk Down Wall Street, A (Malkiel), 101–102, 175 Random walk theory, 106–108, 119 positive autocorrelation and, 106–108 204 Index Random walk theory (Cont.): random walk defined, 106 rebalancing and, 109 Raskob, John J., 16–17 Real estate investment trusts (REITs), 38, 40, 100, 145 defined, 19 index fund, 148 returns on, 19, 21, 25 Real return, 26, 80, 168, 170 Rebalancing: frequency of, 108–109 importance of, 32–33, 35–36, 59, 63, 174 and mean-variance optimizer (MVO), 65 overbalancing in, 138 random walk theory and, 109 rebalancing bonus, 74, 159–160 of tax-sheltered accounts, 159–160 of taxable accounts, 160–161 Recency effects, 47–48, 52, 53, 58–59, 140–141 Regression analysis, 89–90 Reinvestment risk, 23 Representativeness, 118 Research expenses, 92, 95 Residual return, 98 Retirement, 165–172 asset allocation for, 153–154 duration risk and, 165–167 shortfall risk and, 167–172 (See also Tax-sheltered accounts) Return: annualized, 2–3, 5 average, 2–3 coin toss and, 1–5 company size and, 116–117 correlation between risk and, 21 dividend discount method, 23–24, 26, 127–132 efficient frontier and, 55–58 expected investment, 26 historical, problems with, 21–27 Return (Cont.): impact of diversification on, 31–36, 63 market, 168 real, 26, 80, 168, 170 return and risk plot, 31–36, 41–45 risk and high, 18 uncorrelated, 29–31 variation in, 116–117 Risk: common stock, 1–5 correlation between return and, 21 currency, 132–137 duration, 165–167 efficient frontier and, 55–58 excess, 12–13 high returns and, 18 impact of diversification on, 31–36, 63 nonsystematic, 12–13 reinvestment, 23 return and risk plot, 31–36, 41–45 shortfall, 167–172 sovereign, 72 systematic, 13 (See also Standard deviation) Risk aversion myopia, 141–142 Risk dilution, 45–46 Risk-free investments, 10, 15, 152 Risk-free rate, 121 Risk time horizon, 130, 131, 143–144, 167 Risk tolerance, 79–80, 143 Roth IRA, 172 Rukeyser, Lou, 174 Rule of 72, 27 Sanborn, Robert, 88–90 Securities Act of 1933, 92–93 Security Analysis (Graham and Dodd), 93, 118, 125, 176 Selling forward, 132–133 Semivariance, 7 Sharpe, William, 141 Shortfall risk, 167–172 Siegel, Jeremy, 19, 136 Index Simple portfolios, 31–36 Sinquefield, Rex, 148 Small-cap premium, 53, 121, 122 Small-company stocks, 13–16, 25 correlation with large-company stocks, 53–55 efficient frontier and, 55–59 indexing, 101, 102, 148–149 international diversification with, 74–75 January effect and, 92–94 large-company stocks versus, 53–55, 75 “lottery ticket” premium and, 127 tracking error of, 75 Small investors, institutional investors versus, 59–61 Solnik, Bruno, 72 Sovereign risk, 72 S&P 500, 13, 38, 39, 55 as benchmark, 60, 78, 79, 80, 86, 88–89, 145 efficient frontier, 56–57 Spiders (SPDRS), 149 Spot rate, 135 Spread, 91, 92, 93, 96 Standard deviation, 5–8 defined, 6, 63 limitations of, 7 of manager returns, 96 in mean-variance analysis, 65 Standard error (SE), 87 Standard normal cumulative distribution function, 7 Stocks, Bonds, Bills, and Inflation (Ibbotson Associates), 9–10, 41–42, 178 Stocks for the Long Run (Siegel), 19, 136 Strategic asset allocation, 58–59 Survivorship bias, 101–102 Systematic risk, 13 t distribution function, 87 Tax-sheltered accounts: asset allocation for, 153–154 rebalancing, 108–109, 159–160 (See also Retirement accounts) 205 Taxable accounts: asset allocation for, 153–154 rebalancing, 160–161 Taxes: in asset allocation strategy, 145 capital gains capture, 102, 108 foreign tax credits, 161 market efficiency and, 102–103 Technological change: historical, impact of, 125 in new era of investing, 125 Templeton, John, 164 Thaler, Richard, 131, 142 Three-factor model (Fama and French), 120–124 Time horizon, 130, 131, 143–144, 167 Tracking error: defined, 75 determining tolerance for, 83, 145 of small-company stocks, 75 of various equity mixes, 79 Treasury bills: 1926–1998, 10–11 returns on, 25–26 as risk-free investments, 10, 15, 152 Treasury bonds: 1926–1998, 11–13, 42–45 ladders, 152 Treasury Inflation Protected Security (TIPS), 80, 131–132, 172 Treasury notes, 11 Turnover, 95, 102, 130–131, 145 Tweedy, Browne, 148–149, 162, 176 Utility functions, 7 Value averaging, 155–159 Value Averaging (Edleson), 176 Value index funds, 145 Value investing, 77, 111–124 defined, 118 growth investing versus, 117, 118–120 measures used in, 112–114 studies on, 115–118 three-factor model of, 120–124 Value premium, 121–123 206 Index VanEck Gold Fund, 21 Vanguard Group, 97–100, 146–148, 149, 150, 152, 156, 161–163 Variance, 7, 108–109 mean-variance analysis, 44–45, 64–71, 181–182 minimum-variance portfolios, 65–69 Variance drag, 69 Walz, Daniel T., 169 Websites, 178–180 Wilkinson, David, 56, 57, 181–182 Williams, John Burr, 127 Wilshire Associates, 120, 147, 162 World Equity Benchmark Securities (WEBS), 149–151 z values, 87 Zero correlation, 31 About the Author William Bernstein, Ph.D, M.D., is a practicing neurologist in Oregon.


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A Mathematician Plays the Stock Market by John Allen Paulos

Alan Greenspan, AOL-Time Warner, Benoit Mandelbrot, Black-Scholes formula, book value, Brownian motion, business climate, business cycle, butter production in bangladesh, butterfly effect, capital asset pricing model, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, dogs of the Dow, Donald Trump, double entry bookkeeping, Elliott wave, endowment effect, equity risk premium, Erdős number, Eugene Fama: efficient market hypothesis, four colour theorem, George Gilder, global village, greed is good, index fund, intangible asset, invisible hand, Isaac Newton, it's over 9,000, John Bogle, John Nash: game theory, Larry Ellison, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Myron Scholes, Nash equilibrium, Network effects, passive investing, Paul Erdős, Paul Samuelson, Plato's cave, Ponzi scheme, power law, price anchoring, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, Richard Thaler, risk free rate, Robert Shiller, short selling, six sigma, Stephen Hawking, stocks for the long run, survivorship bias, transaction costs, two and twenty, ultimatum game, UUNET, Vanguard fund, Yogi Berra

By starting with the basic up-down-up and down-up-down patterns of a stock’s possible movements, continually replacing each of these patterns’ three segments with smaller versions of one of the basic patterns chosen at random, and then altering the spikiness of the patterns to reflect changes in the stock’s volatility, Mandelbrot has constructed what he calls multifractal “forgeries.” The forgeries are patterns of price movement whose general look is indistinguishable from that of real stock price movements. In contrast, more conventional assumptions about price movements, say those of a strict random-walk theorist, lead to patterns that are noticeably different from real price movements. These multifractal patterns are so far merely descriptive, not predictive of specific price changes. In their modesty, as well as in their mathematical sophistication, they differ from the Elliott waves mentioned in chapter 3.

Weeding out some of the details, let’s assume for the sake of the argument (although Lo and MacKinlay don’t) that the thesis of Burton Malkiel’s classic book, A Random Walk Down Wall Street, is true and that the movement of the market as a whole is entirely random. Let’s also assume that each stock, when its fluctuations are examined in isolation, moves randomly. Given these assumptions it would nevertheless still be possible that the price movements of, say, 5 percent of stocks accurately predict the price movements of a different 5 percent of stocks one week later. The predictability comes from cross-correlations over time between stocks.

More concretely, let’s say stock X, when looked at in isolation, fluctuates randomly from week to week, as does stock Y. Yet if X’s price this week often predicts Y’s next week, this would be an exploitable opportunity and the strict random-walk hypothesis would be wrong. Unless we delved deeply into such possible cross-correlations among stocks, all we would see would be a randomly fluctuating market populated by randomly fluctuating stocks. Of course, I’ve employed the typical mathematical gambit of considering an extreme case, but the example does suggest that there may be relatively simple elements of order in a market that appears to fluctuate randomly. There are other sorts of stock price anomalies that can lead to exploitable opportunities.


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The Rise of the Quants: Marschak, Sharpe, Black, Scholes and Merton by Colin Read

Abraham Wald, Albert Einstein, Bayesian statistics, Bear Stearns, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, discovery of penicillin, discrete time, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, floating exchange rates, full employment, Henri Poincaré, implied volatility, index fund, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Long Term Capital Management, Louis Bachelier, margin call, market clearing, martingale, means of production, moral hazard, Myron Scholes, Paul Samuelson, price stability, principal–agent problem, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk tolerance, risk/return, Robert Solow, Ronald Reagan, shareholder value, Sharpe ratio, short selling, stochastic process, Thales and the olive presses, Thales of Miletus, The Chicago School, the scientific method, too big to fail, transaction costs, tulip mania, Works Progress Administration, yield curve

His statement that stock prices could be modeled as a random walk according to a Weiner process was amenable to empirical verification. Alfred Cowles, who would found the Cowles Commission, and Herbert Jones explored and subsequently vindicated this notion that there is no memory effect in the price of stocks in a 1937 paper together.8 While the notion of the random walk has since been replaced with the less restrictive concept of a martingale process, much of finance pricing theory still retains the random walk because of its simple first and second moment characterization of price movements. The random walk of absolute prices Bachelier constructed a theory of absolute rather than relative price movements.

In this revolutionary thesis, Bachelier was the first to apply the mathematical model of Brownian motion to the movement of security prices. He did so five years before Albert Einstein applied the same model to the movement of small particles. Einstein and Bachelier both noted that, beyond a common drift element, the movement of a particle or a stock from one period to the next is uncorrelated. We now know this phenomenon as the random walk. We return to Bachelier’s model later in our discussion of options pricing theory, and more fully in the next volume of our series on Applications 33 the random walk and the efficient market hypothesis. Without fully anticipating the profound impact, he nonetheless created a wave of scientific innovation in finance.

First, they assumed that the number of shares outstanding does not change before the settlement date. If so, this would dilute the price of the stock and affect the option price. Similarly, they assumed that no dividends are paid and that the stock evolution follows a log-normal random walk with a constant drift and volatility. Finally, individuals can borrow and lend without restriction at the risk-free rate of return. The log-normal drift of returns implies that the stock price x(t) follows the following process as a function of its drift rate and standard deviation: dS(t) = μS(t)dt + σS(t)dB(t) where µ is the drift rate and ␴ is the standard deviation of the Brownian motion defined by B(t).


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Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else by Jordan Ellenberg

Albert Einstein, AlphaGo, Andrew Wiles, autonomous vehicles, British Empire, Brownian motion, Charles Babbage, Claude Shannon: information theory, computer age, coronavirus, COVID-19, deep learning, DeepMind, Donald Knuth, Donald Trump, double entry bookkeeping, East Village, Edmond Halley, Edward Jenner, Elliott wave, Erdős number, facts on the ground, Fellow of the Royal Society, Geoffrey Hinton, germ theory of disease, global pandemic, government statistician, GPT-3, greed is good, Henri Poincaré, index card, index fund, Isaac Newton, Johannes Kepler, John Conway, John Nash: game theory, John Snow's cholera map, Louis Bachelier, machine translation, Mercator projection, Mercator projection distort size, especially Greenland and Africa, Milgram experiment, multi-armed bandit, Nate Silver, OpenAI, Paul Erdős, pets.com, pez dispenser, probability theory / Blaise Pascal / Pierre de Fermat, Ralph Nelson Elliott, random walk, Rubik’s Cube, self-driving car, side hustle, Snapchat, social distancing, social graph, transcontinental railway, urban renewal

His work was just too far away from the mainstream—or so it seemed, before the random-walk revolution began. Bachelier did end up getting a job as a professor at Besançon, and lived until 1946, long enough to see the originality of his work appreciated by other mathematicians, but not to see the random walk become a standard tool in mathematical finance. Word has even diffused to the general public: Burton Malkiel’s book on investing, A Random Walk Down Wall Street, has sold over a million copies. Malkiel’s message is a sobering one. The constant up-and-down wandering of a stock price looks like events are driving it, but it might well be as random as the mosquito’s endless flitting.

A few years afterward, the stock market went haywire and threw the world into depression, so Elliott had a lot of free time, and a lot of motivation to restore some order to a financial world that no longer fit into neat double-entry bookkeeping. Elliott surely didn’t know about Louis Bachelier’s work on stock prices as a random walk, but if he had, he wouldn’t have given it a minute. He didn’t want to believe stock prices were randomly jittering like dust suspended in fluid. He wanted something more like the comforting physical laws that kept the planets safely in their orbits. Elliott compared himself to Edmond Halley, who figured out in the seventeenth century that the apparently random comings and goings of comets actually obeyed a rigid timetable.

Yes, you’d lose those rare but thrilling moments where a team like the 2004 Red Sox comes back from a 3–0 deficit to win the American League Championship Series; but that hardly ever happens. And would that be too high a price to pay for all the Game 8s and winner-take-all Game 9s we’d have between closely matched teams? THE SPACE OF STRATEGIES Back to Go. We’ve seen that the outcome of a random walk can give you clues about where your starting position was; it’s reasonable to guess that a position from which Akbar is likely to accidentally win is also one where he’s well set up to win if he actually tries.


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Capital Ideas: The Improbable Origins of Modern Wall Street by Peter L. Bernstein

Albert Einstein, asset allocation, backtesting, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, corporate raider, debt deflation, diversified portfolio, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, full employment, Glass-Steagall Act, Great Leap Forward, guns versus butter model, implied volatility, index arbitrage, index fund, interest rate swap, invisible hand, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, law of one price, linear programming, Louis Bachelier, mandelbrot fractal, martingale, means of production, Michael Milken, money market fund, Myron Scholes, new economy, New Journalism, Paul Samuelson, Performance of Mutual Funds in the Period, profit maximization, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk free rate, risk/return, Robert Shiller, Robert Solow, Ronald Reagan, stochastic process, Thales and the olive presses, the market place, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, transaction costs, transfer pricing, zero-coupon bond, zero-sum game

See also Capital Asset Pricing Model; Random price fluctuations; specific types of securities arbitrage Black/Scholes formula of: see Black/Scholes formula earnings ratio efficient markets and future of growth stocks information and interest rates and intrinsic value and manipulation risk and security analysis and shadow transfer trends value differentiation zero downside limit on “Price Movements in Speculative Markets: Trends or Random Walks” (Alexander) “Pricing of Options and Corporate Liabilities, The” (Black/Scholes) Probability theory Procter & Gamble Profit maximization Program trading Prospective yield “Proposal for a Smog Tax, A” (Sharpe) Puts: see Options Railroads RAND Random Character of Stock Prices, The (Cootner) “Random Difference Series for Use in the Analysis of Time Series, A” (Working) Random price fluctuations/random walks selection of securities and “Random Walks in Stock Market Prices” (Fama) Rational Expectations Hypothesis “Rational Theory of Warrant Pricing” (Samuelson) Regulation of markets Return analysis: see Risk/return ratios Review of Economics and Statistics Review of Economic Studies, The “RHM Warrant and Low-Price Stock Survey, The” Risk arbitrage calculations diversification and dominant expected return and minimalization portfolio premium return ratios Rosenberg’s model stock prices and of stocks vs. bonds systematic (beta) trade-offs valuation of assets and “Risk and the Evaluation of Pension Fund Performance” (Fama) Risk-free assets Rosenberg Institutional Equity Management (RIEM) “Safety First and the Holding of Assets” (Roy) Samsonite Savings rates Scott Paper Securities analysis Securities and Exchange Commission Security Analysis (Graham/Dodd) Security selection Separation Theorem Shadow prices “Simplified Model for Portfolio Analysis, A” (Sharpe) Singer Manufacturing Company Single-index model Sloan School of Management Standard & Poor’s 500 index “State of the Art in Our Profession, The” (Vertin) Stock(s) cash ratios common expected return on growth income international legal restrictions on market value variance volatility Stock market (general discussion) Black Monday (October, 1987, crash) “Stock Market ‘Patterns’ and Financial Analysis” (Roberts) Supply and demand theory Swaps Tactical asset allocation theory Tampax Taxes.

Fama first reviews the research on the random behavior of stock prices, including the work of Kendall and Alexander. He then throws down the gauntlet to the chartists and technical analysts who believe that the past pattern of stock prices makes future prices predictable: The chartist must admit that the evidence in favor of the random walk model is both consistent and voluminous, whereas there is precious little published in discussion of rigorous empirical tests of the various technical theories. If the chartist rejects the evidence of the random walk model, his position is weak if his own theories have not been subjected to equally rigorous tests.

Kirby(1979). 3. Schwert (1990a). 4. Markowitz(1991). Notes “PC&I” refers to personal correspondence and interviews. Bibliography and Other Sources Alexander, Sidney S. 1961. “Price Movements in Speculative Markets: Trends or Random Walks.” Industrial Management Review, Vol. 2, No. 2 (May), pp. 7–26. Also in Cootner (1964). Alexander, Sidney S. 1964. “Price Movements in Speculative Markets: Trends or Random Walks, No. 2.” Industrial Management Review, Vol. 5, No. 2 (Spring). Also in Cootner (1964). Aristotle. Politics. Book I, Chapter 11. Bachelier, Louis. 1900. Theory of Speculation. Paris: Gauthier-Villars.


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Irrational Exuberance: With a New Preface by the Author by Robert J. Shiller

Alan Greenspan, Andrei Shleifer, asset allocation, banking crisis, benefit corporation, Benoit Mandelbrot, book value, business cycle, buy and hold, computer age, correlation does not imply causation, Daniel Kahneman / Amos Tversky, demographic transition, diversification, diversified portfolio, equity premium, Everybody Ought to Be Rich, experimental subject, hindsight bias, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Joseph Schumpeter, Long Term Capital Management, loss aversion, Mahbub ul Haq, mandelbrot fractal, market bubble, market design, market fundamentalism, Mexican peso crisis / tequila crisis, Milgram experiment, money market fund, moral hazard, new economy, open economy, pattern recognition, Phillips curve, Ponzi scheme, price anchoring, random walk, Richard Thaler, risk tolerance, Robert Shiller, Ronald Reagan, Small Order Execution System, spice trade, statistical model, stocks for the long run, Suez crisis 1956, survivorship bias, the market place, Tobin tax, transaction costs, tulip mania, uptick rule, urban decay, Y2K

., Scottsdale, Arizona, and Roosevelt, New Jersey The paper used in this publication meets the requirements of ANSI/NISO Z39.48-1992 (R1997) (Permanence of Paper) http://pup.princeton.edu Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 To Ben and Derek This page intentionally left blank Contents List of Figures and Tables Preface Acknowledgments One ix xi xix The Stock Market Level in Historical Perspective 3 Part One Structural Factors Two Three Precipitating Factors: The Internet, the Baby Boom, and Other Events Amplification Mechanisms: Naturally Occurring Ponzi Processes 17 44 Part Two Cultural Factors Four Five Six The News Media New Era Economic Thinking New Eras and Bubbles around the World vii 71 96 118 viii C ONT ENTS Part Three Psychological Factors Seven Eight Psychological Anchors for the Market Herd Behavior and Epidemics 135 148 Part Four Attempts to Rationalize Exuberance Nine Ten Efficient Markets, Random Walks, and Bubbles Investor Learning—and Unlearning 171 191 Part Five A Call to Action Eleven Speculative Volatility in a Free Society Notes References Index 235 269 283 203 Figures and Tables Figures 1.1 1.2 1.3 9.1 Stock Prices and Earnings, 1871–2000 Price-Earnings Ratio, 1881–2000 Price-Earnings Ratio as Predictor of Ten-Year Returns Stock Price and Dividend Present Value, 1871–2000 6 8 11 186 Tables 6.1 6.2 6.3 6.4 Largest Recent One-Year Real Stock Price Index Increases Largest Recent One-Year Real Stock Price Index Decreases Largest Recent Five-Year Real Stock Price Index Increases Largest Recent Five-Year Real Stock Price Index Decreases ix 119 120 121 122 This page intentionally left blank Preface T his book is a broad study, drawing on a wide range of published research and historical evidence, of the enormous recent stock market boom.

One method for judging whether there is evidence in support of the basic validity of the efficient markets theory, which I published in an article in the American Economic Review in 1981 (at the same time as a similar paper by Stephen LeRoy and Richard Porter appeared), is to see whether the very volatility of speculative prices, such as stock prices, can be justified by the variability of dividends over long intervals of time. If the stock price move- E F F ICIE N T MARKE TS , RANDOM WALKS, AND BUBB LES 185 ments are to be justified in terms of the future dividends that firms pay out, as the basic version of the efficient markets theory would imply, then under efficient markets we cannot have volatile prices without subsequently volatile dividends.23 In fact, my article concluded, no movement of U.S. aggregate stock prices beyond the trend growth of prices has ever been subsequently justified by dividend movements, as the dividend present value has shown an extraordinarily smooth growth path.

Kenneth Froot and Maurice Obstfeld, reacting to the same appearance of co-movement between prices and dividends, postulated an “intrinsic bubble” model in which prices respond in an apparently exaggerated fashion, but in fact rationally, to dividend movements. In their theory, stock prices overreact, in a certain sense, to dividends, but yet there are no profit opportunities to trading to take advantage of this overreaction.19 I think that these authors overstate their case for co-movements between dividends and prices. The wiggles in stock prices do not E F F ICIE N T MARKE TS , RANDOM WALKS, AND BUBB LES 183 in fact correspond very closely to wiggles in dividends.


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Python for Data Analysis by Wes McKinney

Alignment Problem, backtesting, Bear Stearns, cognitive dissonance, crowdsourcing, data science, Debian, duck typing, Firefox, functional programming, Google Chrome, Guido van Rossum, index card, machine readable, random walk, recommendation engine, revision control, sentiment analysis, Sharpe ratio, side project, sorting algorithm, statistical model, type inference

I put a couple of S&P 500 future contracts and expiry dates in a Series: from datetime import datetime expiry = {'ESU2': datetime(2012, 9, 21), 'ESZ2': datetime(2012, 12, 21)} expiry = Series(expiry).order() expiry then looks like: In [131]: expiry Out[131]: ESU2 2012-09-21 00:00:00 ESZ2 2012-12-21 00:00:00 Then, I use the Yahoo! Finance prices along with a random walk and some noise to simulate the two contracts into the future: np.random.seed(12347) N = 200 walk = (np.random.randint(0, 200, size=N) - 100) * 0.25 perturb = (np.random.randint(0, 20, size=N) - 10) * 0.25 walk = walk.cumsum() rng = pd.date_range(px.index[0], periods=len(px) + N, freq='B') near = np.concatenate([px.values, px.values[-1] + walk]) far = np.concatenate([px.values, px.values[-1] + walk + perturb]) prices = DataFrame({'ESU2': near, 'ESZ2': far}, index=rng) prices then has two time series for the contracts that differ from each other by a random amount: In [133]: prices.tail() Out[133]: ESU2 ESZ2 2013-04-16 1416.05 1417.80 2013-04-17 1402.30 1404.55 2013-04-18 1410.30 1412.05 2013-04-19 1426.80 1426.05 2013-04-22 1406.80 1404.55 One way to splice time series together into a single continuous series is to construct a weighting matrix.

Partial list of numpy.random functions FunctionDescription seed Seed the random number generator permutation Return a random permutation of a sequence, or return a permuted range shuffle Randomly permute a sequence in place rand Draw samples from a uniform distribution randint Draw random integers from a given low-to-high range randn Draw samples from a normal distribution with mean 0 and standard deviation 1 (MATLAB-like interface) binomial Draw samples a binomial distribution normal Draw samples from a normal (Gaussian) distribution beta Draw samples from a beta distribution chisquare Draw samples from a chi-square distribution gamma Draw samples from a gamma distribution uniform Draw samples from a uniform [0, 1) distribution Example: Random Walks An illustrative application of utilizing array operations is in the simulation of random walks. Let’s first consider a simple random walk starting at 0 with steps of 1 and -1 occurring with equal probability. A pure Python way to implement a single random walk with 1,000 steps using the built-in random module: import random position = 0 walk = [position] steps = 1000 for i in xrange(steps): step = 1 if random.randint(0, 1) else -1 position += step walk.append(position) See Figure 4-4 for an example plot of the first 100 values on one of these random walks. Figure 4-4.

Turns out this can be computed using argmax, which returns the first index of the maximum value in the boolean array (True is the maximum value): In [221]: (np.abs(walk) >= 10).argmax() Out[221]: 37 Note that using argmax here is not always efficient because it always makes a full scan of the array. In this special case once a True is observed we know it to be the maximum value. Simulating Many Random Walks at Once If your goal was to simulate many random walks, say 5,000 of them, you can generate all of the random walks with minor modifications to the above code. The numpy.random functions if passed a 2-tuple will generate a 2D array of draws, and we can compute the cumulative sum across the rows to compute all 5,000 random walks in one shot: In [222]: nwalks = 5000 In [223]: nsteps = 1000 In [224]: draws = np.random.randint(0, 2, size=(nwalks, nsteps)) # 0 or 1 In [225]: steps = np.where(draws > 0, 1, -1) In [226]: walks = steps.cumsum(1) In [227]: walks Out[227]: array([[ 1, 0, 1, ..., 8, 7, 8], [ 1, 0, -1, ..., 34, 33, 32], [ 1, 0, -1, ..., 4, 5, 4], ..., [ 1, 2, 1, ..., 24, 25, 26], [ 1, 2, 3, ..., 14, 13, 14], [ -1, -2, -3, ..., -24, -23, -22]]) Now, we can compute the maximum and minimum values obtained over all of the walks: In [228]: walks.max() In [229]: walks.min() Out[228]: 138 Out[229]: -133 Out of these walks, let’s compute the minimum crossing time to 30 or -30.


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Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies by Jeremy Siegel

Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, backtesting, banking crisis, Bear Stearns, behavioural economics, Black Monday: stock market crash in 1987, Black-Scholes formula, book value, break the buck, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, carried interest, central bank independence, cognitive dissonance, compound rate of return, computer age, computerized trading, corporate governance, correlation coefficient, Credit Default Swap, currency risk, Daniel Kahneman / Amos Tversky, Deng Xiaoping, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Financial Instability Hypothesis, fixed income, Flash crash, forward guidance, fundamental attribution error, Glass-Steagall Act, housing crisis, Hyman Minsky, implied volatility, income inequality, index arbitrage, index fund, indoor plumbing, inflation targeting, invention of the printing press, Isaac Newton, it's over 9,000, John Bogle, joint-stock company, London Interbank Offered Rate, Long Term Capital Management, loss aversion, machine readable, market bubble, mental accounting, Minsky moment, Money creation, money market fund, mortgage debt, Myron Scholes, new economy, Northern Rock, oil shock, passive investing, Paul Samuelson, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price stability, proprietary trading, purchasing power parity, quantitative easing, random walk, Richard Thaler, risk free rate, risk tolerance, risk/return, Robert Gordon, Robert Shiller, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, stocks for the long run, survivorship bias, technology bubble, The Great Moderation, the payments system, The Wisdom of Crowds, transaction costs, tulip mania, Tyler Cowen, Tyler Cowen: Great Stagnation, uptick rule, Vanguard fund

This is because modern portfolio theory was established when the vast majority of the academic profession supported the random walk theory of security prices. As noted earlier, when prices are a random walk, the risk over any holding period is a simple function of the risk over a single period, so that the relative risk of different asset classes does not depend on the holding period. In that case the efficient frontier is invariant to the time period, and asset allocation does not depend on the investment horizon of the investor. When security markets do not obey random walks, that conclusion cannot be maintained.6 CONCLUSION No one denies that, in the short run, stocks are riskier than fixed-income assets.

Since unanticipated information is as likely to be better than expected as it is to be worse than expected, the resulting movement in stock prices is random. Price charts will therefore look like a random walk and cannot be predicted.5 SIMULATIONS OF RANDOM STOCK PRICES If stock prices are indeed random, their movements should not be distinguishable from simulations generated randomly by a computer. Figure 20-1 extends the experiment conceived by Professor Roberts 60 years ago. Instead of generating only closing prices, I programmed the computer to generate intraday prices, creating the popular high-low-close bar graphs that are found in most newspapers and chart publications.

., 312 Private vs. public capital, 206 Procter & Gamble, 205, 297 Producer price index (PPI), 264–265 Productivity growth, 69–71 Profit margins, 168–169 Profits. See Earnings Programmed trading, 274, 279 Prospect theory, 347–349 Psychology of investing. See Behavioral finance Public vs. private capital, 206 Purchasing managers index (PMI), 263–264 Purchasing power, 5, 93, 103 Purchasing power parity, 202 Puts, 284–287 Q theory, 168 QQQ ticker symbols, 273 Quantitative easing, 41 Rail Average, 106 Railroads, 92 Ramaswamy, Krishna, 180 A Random Walk Down Wall Street , 323 Random walk hypothesis, 98, 313–314 Randomness of stock prices, 312–314 Rao, Prime Minister Narasimha, 64 Raskob, John J., 3–5 Ratings, 25–27 Ratios.


Analysis of Financial Time Series by Ruey S. Tsay

Asian financial crisis, asset allocation, backpropagation, Bayesian statistics, Black-Scholes formula, Brownian motion, business cycle, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, financial engineering, frictionless, frictionless market, implied volatility, index arbitrage, inverted yield curve, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk free rate, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve

The speed by which r̂h () approaches µ determines the speed of mean reverting. 2.7 UNIT-ROOT NONSTATIONARITY So far we have focused on return series that are stationary. In some studies, interest rates, foreign exchange rates, or the price series of an asset are of interest. These series tend to be nonstationary. For a price series, the nonstationarity is mainly due to the fact that there is no fixed level for the price. In the time series literature, such a nonstationary series is called unit-root nonstationary time series. The best known example of unit-root nonstationary time series is the random-walk model. 2.7.1 Random Walk A time series { pt } is a random walk if it satisfies pt = pt−1 + at , (2.32) where p0 is a real number denoting the starting value of the process and {at } is a white noise series.

The best known example of unit-root nonstationary time series is the random-walk model. 2.7.1 Random Walk A time series { pt } is a random walk if it satisfies pt = pt−1 + at , (2.32) where p0 is a real number denoting the starting value of the process and {at } is a white noise series. If pt is the log price of a particular stock at date t, then p0 could be the log price of the stock at its initial public offering (i.e., the logged IPO price). If at has a symmetric distribution around zero, then conditional on pt−1 , pt has a 50–50 chance to go up or down, implying that pt would go up or down at random. If we treat the random-walk model as a special AR(1) model, then the coefficient of pt−1 is unity, which does not satisfy the weak stationarity condition of an AR(1) model. A random-walk series is, therefore, not weakly stationary, and we call it a unit-root nonstationary time series.

Theoretically, this means that pt can assume any real value for a sufficiently large t. For the log price pt of an individual stock, this is plausible. Yet for market indexes, negative log price is very rare if it happens at all. In this sense, the adequacy of a random-walk model for market indexes is questionable. Third, from the representation, ψi = 1 for all i. Thus, the impact of any past shock at−i on pt does not decay over time. Consequently, the series has a strong memory as it remembers all of the past shocks. In economics, the shocks are said to have a permanent effect on the series. 2.7.2 Random Walk with a Drift As shown by empirical examples considered so far, the log return series of a market index tends to have a small and positive mean.


pages: 542 words: 145,022

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest by Andrew W. Lo, Stephen R. Foerster

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, backtesting, behavioural economics, Benoit Mandelbrot, Black Monday: stock market crash in 1987, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, Charles Babbage, Charles Lindbergh, compound rate of return, corporate governance, COVID-19, credit crunch, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, Edward Glaeser, equity premium, equity risk premium, estate planning, Eugene Fama: efficient market hypothesis, fake news, family office, fear index, fiat currency, financial engineering, financial innovation, financial intermediation, fixed income, hiring and firing, Hyman Minsky, implied volatility, index fund, interest rate swap, Internet Archive, invention of the wheel, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John von Neumann, joint-stock company, junk bonds, Kenneth Arrow, linear programming, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, managed futures, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, money market fund, money: store of value / unit of account / medium of exchange, Myron Scholes, new economy, New Journalism, Own Your Own Home, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, prediction markets, price stability, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, selection bias, seminal paper, shareholder value, Sharpe ratio, short selling, South Sea Bubble, stochastic process, stocks for the long run, survivorship bias, tail risk, Thales and the olive presses, Thales of Miletus, The Myth of the Rational Market, The Wisdom of Crowds, Thomas Bayes, time value of money, transaction costs, transfer pricing, tulip mania, Vanguard fund, yield curve, zero-coupon bond, zero-sum game

Competition in markets turns out to have subtle consequences in a variety of ways, including the behavior of stocks around news events and the surprising difficulty that asset managers have in delivering better performance than indices, an acid test of efficiency. The first prediction of the EMH is that stock prices should follow random walks. The successive changes of a random walk are unpredictable, and stock price changes in an efficient market should be unpredictable; otherwise, people could make easy money. It should not matter today what yesterday’s price change was, since only new and relevant information should move stock prices. (To be fully precise, the stock price should follow a random walk after adjustment for dividends and a risk premium.) The notion of random walks can be traced back to 1827, when botanist Robert Brown used a microscope to examine dust grains floating in water and noticed their erratic behavior, later memorialized as Brownian motion.

See also specific topics “Portfolio Operations” (Ellis), 262 portfolio risk formula, Markowitz’s discovery of, 25–26 “Portfolio Selection” (Markowitz), 27–34 portfolio selection: importance of Markowitz’s contribution to, 44–45; Markowitz’s book on, 39–41; as Markowitz’s dissertation topic, 27–34; work preceding Markowitz’s work on, 35–39 Portfolio Selection: Efficient Diversification of Investments (Markowitz), 39–41, 54 portfolio theory, Marschak and, 35 Portfolio Theory and Capital Markets (Sharpe), 73 Pre-Pottery Neolithic period trade, 1–2 Prescott, Edward, 230, 288–89 prestito, 7 price-earnings (P/E) ratio, 246, 291; cyclically adjusted (see cyclically adjusted price-to-earnings (CAPE) ratio); equity premium and, 291; lower-risk premiums and, 238; Shiller on, 303; Siegel on, 297–98, 299, 302, 303, 305, 306, 307; speculative return and, 133–34; target, in 1719 France, 11 price-to-earnings (P/E) multiple, of Mississippi Company, 11 “The Pricing of Options and Corporate Liabilities” (Black and Scholes), 157 Primecap Fund, 135 Primerica, 346n5 principles of constructing a Perfect Portfolio, 323–26 process of constructing a Perfect Portfolio, 323, 326–31 prospect theory, 42–43 pure yield pickup swaps, 211 Qin Shi Huangdi, 5 Rainwater, James, as Nobel Prize winner, 174 Ramo, Simon, 264–65, 362n34 RAND Corporation, 39, 340n77 The Random Character of Stock Market Prices (Cootner), 83 A Random Walk Down Wall Street (Malkiel), 276 random walks, 82–83, 88–89, 93–94 “Random Walks in Stock-Market Prices” (Fama), 93–94 Rashomon effect, 308 rate anticipation swaps, 211 rational expectations, 344n28; Shiller on, 229–30, 232–34 “Rational Expectations and the Structure of Interest Rates” (Shiller), 230 rationality, critics of, 83–84 Reagan, Ronald, 236 rebalancing, Bogle on, 135–36 “Religion and Science” (Einstein), 227 reputation, importance of, 3–4 retirement, baby boomer, Siegel on effect on investor portfolios, 299–300 retirement planning: asset-liability management and, 214–15; inflation and, 224; Merton’s work in, 192–93, 194–97; Sharpe’s interest in, 75–76; target date funds for, 224 risk: adaptive markets hypothesis and, 320; capital asset pricing model and (see capital asset pricing model (CAPM)); differing approaches to, 321; dragon, 221; Ellis on, 268; endowment model and, 220–21; Leibowitz on, 216–18, 220–21, 224, 316–17; MacroShares and, 251; Markowitz on, 25–26, 310; Merton on, 192–93; portfolio risk formula and, 25–26 (see also modern portfolio theory (MPT)); Scholes on, 167, 171–72, 313–14; Sharpe’s expansion on Markowitz’s approach to, 56; Siegel on, 294–95.

That was the beginning of the story.”18 Investors and analysts who tried to detect geometric patterns in past stock prices that would point to a trend (a discipline known as technical analysis, and also charting) contended that there was important information in past prices. These analysts assumed that history repeated itself. For example, if past prices formed a head-and-shoulders pattern, then chartists predicted that the stock would continue to fall below the shoulder level, just as had happened to other stocks that had price charts resembling the head-and-shoulders pattern. In contrast, the random walk hypothesis suggested that it’s no easier to predict stock prices than it is to pick the correct lottery numbers: just because certain numbers turned up in the past, they were no more and no less likely to turn up next time.


pages: 374 words: 114,600

The Quants by Scott Patterson

Alan Greenspan, Albert Einstein, AOL-Time Warner, asset allocation, automated trading system, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, book value, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Carl Icahn, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Dr. Strangelove, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Glass-Steagall Act, global macro, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, Jim Simons, job automation, John Meriwether, John Nash: game theory, junk bonds, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, Mark Spitznagel, merger arbitrage, Michael Milken, military-industrial complex, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Savings and loan crisis, Sergey Aleynikov, short selling, short squeeze, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Bachelier’s formula describing this phenomenon showed that the future course of the market is essentially a coin flip—a bond is as likely to rise as it is to fall, just as a coin is as likely to land on heads as tails, or a grain of pollen quivering in a mass of liquid is as likely to zig left as right. With bond prices, that’s because the current price is “the true price: if the market judged otherwise, it would quote not this price, but another price higher or lower,” Bachelier wrote. This discovery came to be called the random walk. It’s also called the drunkard’s walk. Imagine it’s late at night, and you’re walking home through a thick fog—let’s say a 1900 Parisian fog. You notice a drunk leaning against a lamppost in the bohemian quarter of Montmartre—perhaps some unknown artist celebrating a breakthrough.

It would also give birth to its own destructive forces and pave the way to a series of financial catastrophes, culminating in an earthshaking collapse that erupted in August 2007. Like Thorp’s methodology for pricing warrants, an essential component of the Black-Scholes formula was the assumption that stocks moved in a random walk. Stocks, in other words, are assumed to move in antlike zigzag patterns just like the pollen particles observed by Brown in 1827. In their 1973 paper, Black and Scholes wrote that they assumed that the “stock price follows a random walk in continuous time.” Just as Thorp had already discovered, this allowed investors to determine the relevant probabilities for volatility—how high or low a stock or option would move in a certain time frame.

If he thought the market followed a random walk, that meant everyone had to get on board or have a damn good reason not to. Most agreed, including one of Samuelson’s star students, Robert Merton, one of the co-creators of the Black-Scholes option-pricing formula. Another acolyte was Burton Malkiel, who went on to write A Random Walk Down Wall Street. It was Fama, however, who connected all of the dots and put the efficient-market hypothesis on the map as a central feature of modern portfolio theory. The idea that the market is an efficient, randomly churning price-processing machine has many odd consequences.


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Black Swan, Bob Litterman, bond market vigilante , book value, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, carbon credits, Carmen Reinhart, central bank independence, classic study, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, deal flow, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global macro, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, inverted yield curve, invisible hand, John Bogle, junk bonds, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, pension time bomb, performance metric, Phillips curve, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, savings glut, search costs, selection bias, seminal paper, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stock buybacks, stocks for the long run, survivorship bias, systematic trading, tail risk, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

Inflation persistence rose with inflation level until 1980. During the gold standard, prices could go persistently up or down but the best long-term forecasts used to be for no change. That is, the price level followed a random walk and recent inflation had no ability to predict future inflation. Between the 1950s and the 1970s, as inflation’s persistence gradually rose, the best time series forecast of future inflation shifted from zero to the most recent inflation rate. That is, instead of the price level following a random walk, the inflation rate did so (the statistical persistence parameter rose from 0 to 1, before reversing after 1980).

Antti Ilmanen Bad Homburg, November 2010 Abbreviations and acronyms AM Arithmetic Mean ATM At The Money (option) AUM Assets Under Management BEI Break-Even Inflation BF Behavioral Finance B/P Book/Price, book-to-market ratio BRP Bond Risk Premium, term premium B-S Black–Scholes C-P BRP Cochrane–Piazzesi Bond Risk Premium CAPM Capital Asset Pricing Model CAY Consumption wealth ratio CB Central Bank CCW Covered Call Writing CDO Collateralized Debt Obligation CDS Credit Default Swap CF Cash Flow CFNAI Chicago Fed National Activity Index CFO Chief Financial Officer CMD Commodity (futures) CPIyoy Consumer Price Inflation year on year CRB Commodity Research Bureau CRP Credit Risk Premium (over Treasury bond) CRRA Constant Relative Risk Aversion CTA Commodity Trading Advisor DDM Dividend Discount Model DJ CS Dow Jones Credit Suisse DMS Dimson–Marsh–Staunton D/P Dividend/Price (ratio), dividend yield DR Diversification Return E( ) Expected (conditional expectation) EMH Efficient Markets Hypothesis E/P Earnings/Price ratio, earnings yield EPS Earnings Per Share ERP Equity Risk Premium ERPB Equity Risk Premium over Bond (Treasury) ERPC Equity Risk Premium over Cash (Treasury bill) F Forward price or futures price FF Fama–French FI Fixed Income FoF Fund of Funds FX Foreign eXchange G Growth rate GARCH Generalized AutoRegressive Conditional Heteroskedasticity GC General Collateral repo rate (money market interest rate) GDP Gross Domestic Product GM Geometric Mean, also compound annual return GP General Partner GSCI Goldman Sachs Commodity Index H Holding-period return HF Hedge Fund HFR Hedge Fund Research HML High Minus Low, a value measure, also VMG HNWI High Net Worth Individual HPA House Price Appreciation (rate) HY High Yield, speculative-rated debt IG Investment Grade (rated debt) ILLIQ Measure of a stock’s illiquidity: average absolute daily return over a month divided by dollar volume IPO Initial Public Offering IR Information Ratio IRP Inflation Risk Premium ISM Business confidence index ITM In The Money (option) JGB Japanese Government Bond K-W BRP Kim–Wright Bond Risk Premium LIBOR London InterBank Offered Rate, a popular bank deposit rate LP Limited Partner LSV Lakonishok–Shleifer–Vishny LtA Limits to Arbitrage LTCM Long-Term Capital Management MA Moving Average MBS (fixed rate, residential) Mortgage-Backed Securities MIT-CRE MIT Center for Real Estate MOM Equity MOMentum proxy MSCI Morgan Stanley Capital International MU Marginal Utility NBER National Bureau of Economic Research NCREIF National Council of Real Estate Investment Fiduciaries OAS Option-Adjusted (credit) Spread OTM Out of The Money (option) P Price P/B Price/Book (valuation ratio) P/E Price/Earnings (valuation ratio) PE Private Equity PEH Pure Expectations Hypothesis PT Prospect Theory r Excess return R Real (rate) RE Real Estate REITs Real Estate Investment Trusts RWH Random Walk Hypothesis S Spot price, spot rate SBRP Survey-based Bond Risk Premium SDF Stochastic Discount Factor SMB Small Minus Big, size premium proxy SR Sharpe Ratio SWF Sovereign Wealth Fund TED Treasury–Eurodollar (deposit) rate spread in money markets TIPS Treasury Inflation-Protected Securities, real bonds UIP Uncovered Interest Parity (hypothesis) VaR Value at Risk VC Venture Capital VIX A popular measure of the implied volatility of S&P 500 index options VMG Value Minus Growth, equity value premium proxy WDRA Wealth-Dependent Risk Aversion X Cash flow Y Yield YC Yield Curve (steepness), term spread YTM Yield To Maturity YTW Yield To Worst Disclaimer Antti Ilmanen is a Senior Portfolio Manager at Brevan Howard, one of Europe’s largest hedge fund managers.

Roll return measures expected excess return only if the spot price (and the term structure of constant maturity futures prices) follow a random walk and thus are expected to remain unchanged. This empirical assumption may work well on average (see Figure 11.8 and Chapter 22 for broader evidence), but sometimes it is too extreme. What drives the shape of commodity term structure and expected commodity returns? The classic commodity-pricing literature focuses on the former and only indirectly addresses the latter (expected risk premia). The oldest idea, attributable to John Maynard Keynes, is that the term structure of commodity prices is normally inverted (backwardated: S > F) because producers create more hedging pressure than consumers.


pages: 320 words: 33,385

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, implied volatility, interest rate swap, low interest rates, market friction, market microstructure, p-value, performance metric, power law, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, seminal paper, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic bias, Thomas Bayes, transaction costs, two and twenty, value at risk, volatility smile, Wiener process, yield curve, zero-sum game

In Section II.5.3.7 we prove that the discrete time version of (I.3.142) is a stationary AR(1) model. I.3.7.3 Stochastic Models for Asset Prices and Returns Time series of asset prices behave quite differently from time series of returns. In efficient markets a time series of prices or log prices will follow a random walk. More generally, even in the presence of market frictions and inefficiencies, prices and log prices of tradable assets are integrated stochastic processes. These are fundamentally different from the associated returns, which are generated by stationary stochastic processes. Figures I.3.28 and I.3.29 illustrate the fact that prices and returns are generated by very different types of stochastic process.

This is introduced below, but its application to option pricing is not discussed until Chapter III.3. The first two subsections define what is meant by a stationary or ‘mean-reverting’ stochastic process in discrete and continuous time. We contrast this with a particular type of nonstationary process which is called a ‘random walk’. Then Section I.3.7.3 focuses on some standard discrete and continuous time models for the evolution of financial asset prices and returns. The most basic assumption in both types of models is that the prices of traded assets follow a random walk, and from this it follows that their returns follow a stationary process.

Probability and Statistics 139 Application of Itô’s lemma with f = ln S shows that a continuous time representation of geometric Brownian motion that is equivalent to the geometric Brownian motion (I.3.143) but is translated into a process for log prices is the arithmetic Brownian motion, d ln St = − 21 2 dt + dWt (I.3.145) We already know what a discretization of (I.3.145) looks like. The change in the log price is the log return, so using the standard discrete time notation Pt for a price at time t we have d ln St → ln Pt Hence the discrete time equivalent of (I.3.145) is ln Pt = + $t where = prices, i.e. − 1 2 2 $t ∼ NID 0 2 (I.3.146) . This is equivalent to a discrete time random walk model for the log ln Pt = + ln Pt−1 + $t $t ∼ NID 0 2 (I.3.147) To summarize, the assumption of geometric Brownian motion for prices in continuous time is equivalent to the assumption of a random walk for log prices in discrete time.


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Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever by Robin Wigglesworth

Albert Einstein, algorithmic trading, asset allocation, Bear Stearns, behavioural economics, Benoit Mandelbrot, Big Tech, Black Monday: stock market crash in 1987, Blitzscaling, Brownian motion, buy and hold, California gold rush, capital asset pricing model, Carl Icahn, cloud computing, commoditize, coronavirus, corporate governance, corporate raider, COVID-19, data science, diversification, diversified portfolio, Donald Trump, Elon Musk, Eugene Fama: efficient market hypothesis, fear index, financial engineering, fixed income, Glass-Steagall Act, Henri Poincaré, index fund, industrial robot, invention of the wheel, Japanese asset price bubble, Jeff Bezos, Johannes Kepler, John Bogle, John von Neumann, Kenneth Arrow, lockdown, Louis Bachelier, machine readable, money market fund, Myron Scholes, New Journalism, passive investing, Paul Samuelson, Paul Volcker talking about ATMs, Performance of Mutual Funds in the Period, Peter Thiel, pre–internet, RAND corporation, random walk, risk-adjusted returns, road to serfdom, Robert Shiller, rolodex, seminal paper, Sharpe ratio, short selling, Silicon Valley, sovereign wealth fund, subprime mortgage crisis, the scientific method, transaction costs, uptick rule, Upton Sinclair, Vanguard fund

., 51 Optimized Portfolios As Listed Securities (OPALS), 195–96, 247 Orrick, Herrington & Sutcliffe, 177 Pacific Commodities Exchange, 170 Pacific Vegetable Oil, 169 Paine Webber Jackson & Curtis, 111 Parsons, James, 199–200, 201 passive investing brief overview of, 7–8 Buffett-Seides wager, 1–2, 3–4, 6, 9–11, 15–17 index inclusion effect, 254–62 origin story of, 69–77, 164–65 problems with, 266–84, 287–90, 294–99 Samuelson on, 106–7 Singer on, 18–19, 287–88, 290 Wallace’s tale, 265–66 Peanuts (comic), 38 Pedersen, Lasse Heje, 276–77 Penn Central Transportation Company, 172–73 pension funds, 79–80, 83–85, 274, 292 AG Becker and, 142–43 Buffett on, 4–5, 7, 8, 83 DFA and, 144–45, 151, 159, 162, 164 Wells Fargo and, 75–78, 80, 185, 186, 188, 190–91 Pensions & Investments, 68 Peru, 257 Peter Cooper Village, 220 Peterson, Pete, 210, 211, 213–14 Petry, Johannes, 256–57 Petty, Tom, 248n Philadelphia National Bank, 92 Philadelphia Stock Exchange (PSE), 172–73 Phillips, Noah, 295–96 Pilcher, Simon, 274–75 Pimco, 261 Pimco Total Return Fund, 124 PNC Bank, 214, 217, 219 Poincaré, Henri, 22–23 political donations, 66 “portfolio,” 40 portfolio insurance, 171–72, 178–79, 189 “Portfolio Selection” (Markowitz), 41 Pratchett, Terry, 95 PRIMECAP Management, 123 Princeton University, 53–54, 87, 90–93, 108 “probability law,” 24 “Problem of Twelve, The” (Coates), 297–98 “program trading,” 78n Project Amethyst, 201 ProShares, 248 Protégé House, 16 Protégé Partners, 2, 3, 9 Buffett-Seides wager, 1–2, 3–4, 6, 9–11, 15–17 proxy advisors, 288–90 Purcell, Philip, 216 Purdey shotgun, 239 Qatar Investment Authority, 223–24 Qontigo, 242n “Quantifiers,” 53, 55–68 Quotron, 149–50 RAND Corporation, 41–42, 43–44 Random Character of Stock Prices, The (Cootner), 36–37 Random Walk Down Wall Street, A (Malkiel), 54, 115 random walk hypothesis, 25, 36–37, 49, 50, 51, 82–83 “Random Walks in Stock Market Prices” (Fama), 50 Ranieri, Lewis S., 207 Renshaw, Edward, 34, 45, 86 Reserve Officers’ Training Corps (ROTC), 42, 59–60 “retail money,” 162 Revenue Act of 1978, 119 Reynolds Securities, 111 Riefenstahl, Leni, 163 Riepe, Jim, xi Bogle-Brennan schism, 133 Bogle dinners, 127 founding of Vanguard, 88, 104–5, 104n, 109, 110 setting up first FIIT (“Bogle’s Folly”), 110–11, 112, 114 at T.

Finally, Lorie discussed a controversial but increasingly popular theory that was spreading out of the confines of academia, that stocks took a “random walk” and thus cannot actually be accurately and consistently predicted—the idea that Bachelier had first proposed in 1900 but had only recently been rediscovered thanks to the efforts of the likes of Savage and Samuelson. In 1964, Paul Cootner, a colleague of Samuelson at MIT, published a five-hundred-page tome titled The Random Character of Stock Prices, which contained much of the academic work done by himself, Cowles, and others in the field. One of the more colorful descriptions of the apparent path of stock prices came from Maurice Kendall, a famous British statistician who in 1953 had published a study of fluctuations in UK stocks, Chicago wheat prices, and New York cotton.

Moreover, Fama’s thesis—titled “The Behavior of Stock-Market Prices”—corroborated earlier work by the likes of Mandelbrot and Samuelson which argued that markets are close to random, and therefore impossible to predict. As the young economist wrote in the introduction, “The series of price changes has no memory, that is, the past cannot be used to predict the future in any meaningful way.”19 But why? Fama’s big contribution was to present an overarching hypothesis explaining this conclusion. Although his doctoral thesis makes no explicit mention of it, the term “efficient markets” made its first appearance in “Random Walks in Stock Market Prices,” a paper he published in the Financial Analysts Journal in 1965, which was reprinted in a simpler form by Institutional Investor, the money management industry’s leading magazine, later that year.


Capital Ideas Evolving by Peter L. Bernstein

Albert Einstein, algorithmic trading, Andrei Shleifer, asset allocation, behavioural economics, Black Monday: stock market crash in 1987, Bob Litterman, book value, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, computerized trading, creative destruction, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, diversification, diversified portfolio, endowment effect, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, high net worth, hiring and firing, index fund, invisible hand, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, mental accounting, money market fund, Myron Scholes, paper trading, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, price anchoring, price stability, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, seminal paper, Sharpe ratio, short selling, short squeeze, Silicon Valley, South Sea Bubble, statistical model, survivorship bias, systematic trading, tail risk, technology bubble, The Wealth of Nations by Adam Smith, transaction costs, yield curve, Yogi Berra, zero-sum game

Lo studies with an unusual intensity and a hunger to learn. The possibility that the capital markets are not a random walk came to him quite by accident—in fact, it came to him as he was working on the opposite hypothesis that markets are a random walk. When the evidence fell short of supporting the random walk hypothesis, Lo just looked harder in search of an explanation. bern_c05.qxd 62 3/23/07 9:02 AM Page 62 THE THEORETICIANS After six years, he relates, “I finally decided that markets don’t really follow random walks. The notion is a great idealization but not the real thing. And this work got me tenure at MIT!”

So long as a minute minority of investors, possessed of considerable assets, can seek gain by trading against willful uninformed bettors, then Limited Efficiency of Markets will be empirically observable. The temporary appearance of aberrant price profiles coaxes action from alert traders who act gleefully to wipe out the aberration.” In more colorful language, he has made the same point this way: “My pitch on this occasion is not exclusively or even primarily aimed at practical men. The less of them who become sophisticated, the better for us happy few.”5 bern_c03.qxd 3/23/07 9:01 AM Page 41 Paul A. Samuelson 41 The consequence of all this market activity is a more complex state of affairs than we would find in a truly random walk.* As Samuelson points out, “After numerous people carefully weigh new information arriving about the future, all that is pragmatically knowable is already in current pricing patterns.

If we can actually identify when stock prices are too high or too low, what the market is going to do becomes predictable! Shiller and his frequent coauthor John Campbell put it this way in a paper published in 1998: “Although one might have thought that it is easier to forecast into the near future than into the distant future . . . the data contradict such intuition.”4 But just because something is predictable, we are not guaranteed the ability to predict it correctly. As Andrew Lo and his coauthor, A. Craig MacKinlay of Wharton, wrote in A Non-Random Walk Down Wall Street: “Forecasts of stock returns . . . may be subject to considerable forecast errors, so that ‘excess’ profit opportunities and market inefficiencies are not necessarily consequences of forecastability” (p. 115).


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Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, behavioural economics, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, Higgs boson, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk free rate, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

This is useful in that bid-ask spreads are a function of liquidity, hence Roll's model can be seen as an early attempt to measure the liquidity of a security. Consider a mid-price series {mt}, where prices follow a Random Walk with no drift, hence price changes Δmt = mt − mt − 1 are independently and identically drawn from a Normal distribution These assumptions are, of course, against all empirical observations, which suggest that financial time series have a drift, they are heteroscedastic, exhibit serial dependency, and their returns distribution is non-Normal. But with a proper sampling procedure, as we saw in Chapter 2, these assumptions may not be too unrealistic. The observed prices, {pt}, are the result of sequential trading against the bid-ask spread: where c is half the bid-ask spread, and bt ∈ { − 1, 1} is the aggressor side.

In this context, bubbles are not limited to price rallies, but they also include sell-offs. Tests that allow for multiple bubbles are more robust in the sense that a cycle of bubble-burst-bubble will make the series appear to be stationary to single-bubble tests. Maddala and Kim [1998], and Breitung [2014] offer good overviews of the literature. 17.4.1 Chow-Type Dickey-Fuller Test A family of explosiveness tests was inspired by the work of Gregory Chow, starting with Chow [1960]. Consider the first order autoregressive process where ϵt is white noise. The null hypothesis is that yt follows a random walk, H0: ρ = 1, and the alternative hypothesis is that yt starts as a random walk but changes at time τ*T, where τ* ∈ (0, 1), into an explosive process: At time T we can test for a switch (from random walk to explosive process) having taken place at time τ*T (break date).

The test statistic for an unknown τ* is the maximum of all T(1 − 2τ0) values of . Another drawback of Chow's approach is that it assumes that there is only one break date τ*T, and that the bubble runs up to the end of the sample (there is no switch back to a random walk). For situations where three or more regimes (random walk → bubble → random walk …) exist, we need to discuss the Supremum Augmented Dickey-Fuler (SADF) test. 17.4.2 Supremum Augmented Dickey-Fuller In the words of Phillips, Wu and Yu [2011], “standard unit root and cointegration tests are inappropriate tools for detecting bubble behavior because they cannot effectively distinguish between a stationary process and a periodically collapsing bubble model.


Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies by Jeremy J. Siegel

addicted to oil, Alan Greenspan, asset allocation, backtesting, behavioural economics, Black-Scholes formula, book value, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, cognitive dissonance, compound rate of return, correlation coefficient, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, Everybody Ought to Be Rich, fixed income, German hyperinflation, implied volatility, index arbitrage, index fund, Isaac Newton, it's over 9,000, John Bogle, joint-stock company, Long Term Capital Management, loss aversion, machine readable, market bubble, mental accounting, Money creation, Myron Scholes, new economy, oil shock, passive investing, Paul Samuelson, popular capitalism, prediction markets, price anchoring, price stability, proprietary trading, purchasing power parity, random walk, Richard Thaler, risk free rate, risk tolerance, risk/return, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, stock buybacks, stocks for the long run, subprime mortgage crisis, survivorship bias, technology bubble, The Great Moderation, The Wisdom of Crowds, transaction costs, tulip mania, uptick rule, Vanguard fund, vertical integration

Since unanticipated information is as likely to be good as it is to be bad, the resulting movement in stock prices is random. Price charts will look like a random walk since the probability that stocks go up or down is completely random and cannot be predicted.5 SIMULATIONS OF RANDOM STOCK PRICES If stock prices are indeed random, their movements should not be distinguishable from counterfeits generated randomly by a computer. Figure 17-1 extends the experiment conceived by Professor Roberts 50 years ago. Instead of generating only closing prices, I programmed the computer to generate intraday prices, creating the popular high-low-close bar graphs that are found in most newspapers and chart publications.

TABLE 2–2 Portfolio Allocation: Percentage of Portfolio Recommended in Stocks Based on All Historical Data Risk Tolerance 1 Year Holding Period 5 Years 10 Years 30 Years Ultraconservative (Minimum Risk) 9.0% 22.0% 39.3% 71.4% Conservative 25.0% 38.7% 59.6% 89.5% Moderate 50.0% 61.6% 88.0% 116.2% Aggressive Risk Taker 75.0% 78.5% 110.1% 139.1% CHAPTER 2 Risk, Return, and Portfolio Allocation 35 because modern portfolio theory was established when the academic profession believed in the random walk theory of security prices. As noted earlier, under a random walk, the relative risk of various securities does not change for different holding periods, so portfolio allocations do not depend on how long one holds the asset. The holding period becomes a crucial issue in portfolio theory when the data reveal the mean reversion of stock returns.8 INFLATION-INDEXED BONDS Until the last decade, there was no U.S. government bond whose return was guaranteed against changes in the price level. But in January 1997, the U.S. Treasury issued the first government-guaranteed inflation-indexed bond.

CHAPTER 17 Technical Analysis and Investing with the Trend 291 and-sell signals are purely subjective and cannot be determined by precise numerical rules. THE RANDOMNESS OF STOCK PRICES Although the Dow theory might not be as popular as it once was, technical analysis is still alive and well. The idea that you can identify the major trends in the market, riding bull markets while avoiding bear markets, is still a fundamental pursuit of technical analysts. Yet most economists still attack the fundamental tenet of the chartists—that stock prices follow predictable patterns. To these academic researchers, the movements of prices in the market more closely conform to a pattern called a random walk than to trends that forecast future returns.


Monte Carlo Simulation and Finance by Don L. McLeish

algorithmic bias, Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, discrete time, distributed generation, finite state, frictionless, frictionless market, implied volatility, incomplete markets, invention of the printing press, martingale, p-value, random walk, risk free rate, Sharpe ratio, short selling, stochastic process, stochastic volatility, survivorship bias, the market place, transaction costs, value at risk, Wiener process, zero-coupon bond, zero-sum game

Equivalently, we generate X = V1 /V2 where Vi ∼ U [−1, 1] conditional on V12 + V22 · 1 to produce a standard Cauchy variate X. Example: Stable random walk. A stable random walk may be used to model a stock price but the closest analogy to the Black Scholes model would be a logstable process St under which the distribution of ln(St ) has a symmetric stable distribution. Unfortunately, this specification renders impotent many of our tools of analysis, since except in 158 CHAPTER 3. BASIC MONTE CARLO METHODS the case α = 2 or the case β = −1, such a stock price process St has no finite moments at all. Nevertheless, we may attempt to fit stable laws to the distribution of ln(St ) for a variety of stocks and except in the extreme tails, symmetric stable laws with index α ' 1.7 often provide a reasonably good fit.

It has been an important part of the literature in Physics, Probability and Finance at least since the papers of Bachelier and Einstein, about 100 years ago. A Brownian motion process also has some interesting and remarkable theoretical properties; it is continuous with probability one but the probability that the process has finite 10 68 CHAPTER 2. SOME BASIC THEORY OF FINANCE Random Walk 4 3 2 Sn 1 0 -1 -2 -3 0 2 4 6 8 10 n 12 14 16 18 Figure 2.7: A sample path of a Random Walk variation in any interval is 0. With probability one it is nowhere differentiable. Of course one might ask how a process with such apparently bizarre properties can be used to approximate real-world phenomena, where we expect functions to be built either from continuous and differentiable segments or jumps in the process.

The answer is that a very wide class of functions constructed from those that are quite well-behaved (e.g. step functions) and that have independent increments converge as the scale on which they move is refined either to a Brownian motion process or to a process defined as an integral with respect to a Brownian motion process and so this is a useful approximation to a broad range of continuous time processes. For example, consider a random walk process Pn Sn = i=1 Xi where the random variables Xi are independent identically distributed with expected value E(Xi ) = 0 and var(Xi ) = 1. Suppose we plot the graph of this random walk (n, Sn ) as below. Notice that we have linearly interpolated the graph so that the function is defined for all n, whether integer or not. [FIGURE 2.7 ABOUT HERE] 20 MODELS IN CONTINUOUS TIME 69 Now if we increase the sample size and decrease the scale appropriately on both axes, the result is, in the limit, a Brownian motion process.


pages: 1,829 words: 135,521

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython by Wes McKinney

Bear Stearns, business process, data science, Debian, duck typing, Firefox, general-purpose programming language, Google Chrome, Guido van Rossum, index card, p-value, quantitative trading / quantitative finance, random walk, recommendation engine, sentiment analysis, side project, sorting algorithm, statistical model, Two Sigma, type inference

Partial list of numpy.random functionsFunctionDescription seed Seed the random number generator permutation Return a random permutation of a sequence, or return a permuted range shuffle Randomly permute a sequence in-place rand Draw samples from a uniform distribution randint Draw random integers from a given low-to-high range randn Draw samples from a normal distribution with mean 0 and standard deviation 1 (MATLAB-like interface) binomial Draw samples from a binomial distribution normal Draw samples from a normal (Gaussian) distribution beta Draw samples from a beta distribution chisquare Draw samples from a chi-square distribution gamma Draw samples from a gamma distribution uniform Draw samples from a uniform [0, 1) distribution 4.7 Example: Random Walks The simulation of random walks provides an illustrative application of utilizing array operations. Let’s first consider a simple random walk starting at 0 with steps of 1 and –1 occurring with equal probability. Here is a pure Python way to implement a single random walk with 1,000 steps using the built-in random module: In [247]: import random .....: position = 0 .....: walk = [position] .....: steps = 1000 .....: for i in range(steps): .....: step = 1 if random.randint(0, 1) else -1 .....: position += step .....: walk.append(position) .....: See Figure 4-4 for an example plot of the first 100 values on one of these random walks: In [249]: plt.plot(walk[:100]) Figure 4-4.

Turns out, we can compute this using argmax, which returns the first index of the maximum value in the boolean array (True is the maximum value): In [257]: (np.abs(walk) >= 10).argmax() Out[257]: 37 Note that using argmax here is not always efficient because it always makes a full scan of the array. In this special case, once a True is observed we know it to be the maximum value. Simulating Many Random Walks at Once If your goal was to simulate many random walks, say 5,000 of them, you can generate all of the random walks with minor modifications to the preceding code. If passed a 2-tuple, the numpy.random functions will generate a two-dimensional array of draws, and we can compute the cumulative sum across the rows to compute all 5,000 random walks in one shot: In [258]: nwalks = 5000 In [259]: nsteps = 1000 In [260]: draws = np.random.randint(0, 2, size=(nwalks, nsteps)) # 0 or 1 In [261]: steps = np.where(draws > 0, 1, -1) In [262]: walks = steps.cumsum(1) In [263]: walks Out[263]: array([[ 1, 0, 1, ..., 8, 7, 8], [ 1, 0, -1, ..., 34, 33, 32], [ 1, 0, -1, ..., 4, 5, 4], ..., [ 1, 2, 1, ..., 24, 25, 26], [ 1, 2, 3, ..., 14, 13, 14], [ -1, -2, -3, ..., -24, -23, -22]]) Now, we can compute the maximum and minimum values obtained over all of the walks: In [264]: walks.max() Out[264]: 138 In [265]: walks.min() Out[265]: -133 Out of these walks, let’s compute the minimum crossing time to 30 or –30.

, Introspection-Introspection %quickref magic function, About Magic Commands quicksort method, Alternative Sort Algorithms quotation marks in strings, Strings R r character prefacing quotes, Strings R language, pandas, statsmodels, Handling Missing Data radd method, Arithmetic methods with fill values rand function, Pseudorandom Number Generation randint function, Pseudorandom Number Generation randn function, Boolean Indexing, Pseudorandom Number Generation random module, Pseudorandom Number Generation-Simulating Many Random Walks at Once random number generation, Pseudorandom Number Generation-Pseudorandom Number Generation random sampling and permutation, Example: Random Sampling and Permutation random walks example, Example: Random Walks-Simulating Many Random Walks at Once RandomState class, Pseudorandom Number Generation range function, range, Creating ndarrays rank method, Sorting and Ranking ranking data in pandas library, Sorting and Ranking-Sorting and Ranking ravel method, Reshaping Arrays rc method, matplotlib Configuration rdiv method, Arithmetic methods with fill values re module, Functions Are Objects, Regular Expressions read method, Files and the Operating System-Files and the Operating System read-and-write mode for files, Files and the Operating System read-only mode for files, Files and the Operating System reading datain Microsoft Excel files, Reading Microsoft Excel Files-Reading Microsoft Excel Files in text format, Reading and Writing Data in Text Format-Reading Text Files in Pieces readline functionality, Searching and Reusing the Command History readlines method, Files and the Operating System read_clipboard function, Reading and Writing Data in Text Format read_csv function, Files and the Operating System, Reading and Writing Data in Text Format, Reading and Writing Data in Text Format, Bar Plots, Column-Wise and Multiple Function Application read_excel function, Reading and Writing Data in Text Format, Reading Microsoft Excel Files read_feather function, Reading and Writing Data in Text Format read_fwf function, Reading and Writing Data in Text Format read_hdf function, Reading and Writing Data in Text Format, Using HDF5 Format read_html function, Reading and Writing Data in Text Format, XML and HTML: Web Scraping-Parsing XML with lxml.objectify read_json function, Reading and Writing Data in Text Format, JSON Data read_msgpack function, Reading and Writing Data in Text Format read_pickle function, Reading and Writing Data in Text Format, Binary Data Formats read_sas function, Reading and Writing Data in Text Format read_sql function, Reading and Writing Data in Text Format, Interacting with Databases read_stata function, Reading and Writing Data in Text Format read_table function, Reading and Writing Data in Text Format, Reading and Writing Data in Text Format, Working with Delimited Formats reduce method, ufunc Instance Methods reduceat method, ufunc Instance Methods reductions (aggregations), Mathematical and Statistical Methods references in Python, Variables and argument passing-Dynamic references, strong types regplot method, Scatter or Point Plots regress function, Example: Group-Wise Linear Regression regular expressionspasses as delimiters, Reading and Writing Data in Text Format string manipulation and, Regular Expressions-Regular Expressions reindex method, Reindexing-Reindexing, Selection with loc and iloc, Axis Indexes with Duplicate Labels, Upsampling and Interpolation reload function, Reloading Module Dependencies remove method, Adding and removing elements, set remove_categories method, Categorical Methods remove_unused_categories method, Categorical Methods rename method, Renaming Axis Indexes rename_categories method, Categorical Methods reorder_categories method, Categorical Methods repeat function, Repeating Elements: tile and repeat repeat method, Vectorized String Functions in pandas replace method, Replacing Values, String Object Methods-String Object Methods, Vectorized String Functions in pandas requests package, Interacting with Web APIs resample method, Date Ranges, Frequencies, and Shifting, Resampling and Frequency Conversion-Open-High-Low-Close (OHLC) resampling, Grouped Time Resampling resamplingdefined, Resampling and Frequency Conversion downsampling and, Resampling and Frequency Conversion-Open-High-Low-Close (OHLC) resampling OHLC, Open-High-Low-Close (OHLC) resampling upsampling and, Resampling and Frequency Conversion, Upsampling and Interpolation with periods, Resampling with Periods %reset magic function, About Magic Commands, Input and Output Variables reset_index method, Pivoting “Wide” to “Long” Format, Returning Aggregated Data Without Row Indexes reshape method, Fancy Indexing, Reshaping Arrays *rest syntax, Unpacking tuples return statement, Functions reusing command history, Searching and Reusing the Command History reversed function, reversed rfind method, String Object Methods rfloordiv method, Arithmetic methods with fill values right join type, Database-Style DataFrame Joins rint function, Universal Functions: Fast Element-Wise Array Functions rjust method, String Object Methods rmul method, Arithmetic methods with fill values rollback method, Shifting dates with offsets rollforward method, Shifting dates with offsets rolling function, Moving Window Functions, Moving Window Functions rolling_corr function, Binary Moving Window Functions row major order (C order), C Versus Fortran Order, The Importance of Contiguous Memory row_stack function, Concatenating and Splitting Arrays rpow method, Arithmetic methods with fill values rstrip method, String Object Methods, Vectorized String Functions in pandas rsub method, Arithmetic methods with fill values %run magic functionabout, About Magic Commands exceptions and, Exceptions in IPython interactive debugger and, Interactive Debugger, Other ways to make use of the debugger IPython and, The Python Interpreter, The %run Command-Interrupting running code reusing command history with, Searching and Reusing the Command History r_ object, Stacking helpers: r_ and c_ S %S datetime format, Dates and times, Converting Between String and Datetime s(tep) debugger command, Interactive Debugger sample method, Permutation and Random Sampling, Example: Random Sampling and Permutation save function, File Input and Output with Arrays, Advanced Array Input and Output savefig method, Saving Plots to File savez function, File Input and Output with Arrays savez_compressed function, File Input and Output with Arrays scalar types in Python, Scalar Types-Dates and times, Arithmetic with NumPy Arrays scatter plot matrix, Scatter or Point Plots scatter plots, Scatter or Point Plots scikit-learn library, scikit-learn, Introduction to scikit-learn-Introduction to scikit-learn SciPy library, SciPy scope of functions, Namespaces, Scope, and Local Functions scripting languages, Why Python for Data Analysis?


pages: 389 words: 98,487

The Undercover Economist: Exposing Why the Rich Are Rich, the Poor Are Poor, and Why You Can Never Buy a Decent Used Car by Tim Harford

Alan Greenspan, Albert Einstein, barriers to entry, Berlin Wall, business cycle, collective bargaining, congestion charging, Corn Laws, David Ricardo: comparative advantage, decarbonisation, Deng Xiaoping, Fall of the Berlin Wall, George Akerlof, Great Leap Forward, household responsibility system, information asymmetry, invention of movable type, John Nash: game theory, John von Neumann, Kenneth Arrow, Kickstarter, market design, Martin Wolf, moral hazard, new economy, Pearl River Delta, price discrimination, Productivity paradox, race to the bottom, random walk, rent-seeking, Robert Gordon, Robert Shiller, Ronald Reagan, sealed-bid auction, second-price auction, second-price sealed-bid, Shenzhen special economic zone , Shenzhen was a fishing village, special economic zone, spectrum auction, The Market for Lemons, Thomas Malthus, trade liberalization, Vickrey auction

The truth is that busy, smart, agile, and experienced shoppers are a bit better at calling the fastest lines and can probably average a quicker time than the rest of us. But not by much. Value and price— beyond the random walk Assuming that what is true of supermarket lines is also true of stock-market prices, economists should be able to throw some light on the market, but not very much. Many economists do work for investment funds. They are as wrong nearly as often as they are right, but not quite. Our modified random walk theory tells us that this is what we should expect. So, what do these economists do to provide investment funds with such tiny edges over the market?

In 2002, the company was getting good write-ups in the financial press— but shares were still valued at less than that initial offering of $18. Yet, since then they have recovered to $40 a share. Which price was the mistake: $100, or $8? Or both? The answer would be useful, not least because Amazon’s roller-coaster performance is common. So can the Undercover Economist say anything about why share prices acted the way they did, and how they might behave in the future? A random walk Economists face a serious problem in trying to say anything sensible about stock prices. Economists work by studying rational behavior, but the more rational the behavior of stock-market investors, the more erratic the behavior of the stock market becomes.

See radio supermarkets spectrum rights and bargain shopping, 43–45 television licenses, 165 checkout lines, 139–40, 153 Telewest, 174 location, 39–40, 40, 40–42 theaters, 48–49 organic foods, 42–43 Theory of Games and Economic Behavior and price leaks, 49–53 (Von Neumann and pricing strategies, 36–38, 39–43, Morgenstern), 158 45–47 3G cell-phone service, 163 and scarcity, 48–49 3G-UK, 171 supply, 65, 67 Three Men in a Boat (Jerome), 109 SUVs, 92 Times (London), 144 sweatshops, 222–24, 229, 251–52 Timor, 228 Swing’s, 7 TIW, 173 Switzerland, 120 Tokugawa clan, 227 Tokyo, Japan, 6 Taiwan, 180–81, 247–49 tourism, 31, 112, 177–78 Taiyuan, China, 99 township enterprises, 244 Tanzania, 65–66, 73 trade barriers tariffs, 200, 225 and agriculture, 219 taxation and comparative advantage, 206–8, and corruption, 185, 187–88 209–10 economic impact, 108 and globalization, 212 and externality charges, 84–85, persistence of, 225–26 87–91 and pollution, 217, 220–21 and fairness, 71–72 and self-sufficiency, 199–200 and fuel prices, 76–78 trade unions, 25–26, 224–25 and head start theorem, 74 traffic. See also transportation and inefficiency, 185 and alternative forms of transport, and information, 61 97 luxury taxes, 92 in Cameroon, 179, 186–89 in nonmarket economies, 68 commuting, 5, 18, 85–87 and prices, 70, 82 “driving game,” 156–57 and public projects, 64 economist’s view of, 2–3 vehicle taxes, 82–83 and externalities, 84–85, 92, 96–97 • 274 • I N D E X and government involvement, 107 trade barriers, 226 and public transportation, 18 wealth inequality, 89 and random walk theory, 139–40 universal health care, 120, 126 training, 67 unskilled labor, 28, 29 trains, 51, 56, 232 Uruguay round of trade negotiations, transparency in auctions, 164–65, 172 225 Transparency International, 178 US Congress, 227 transportation, 18, 56, 96–98, 219– US Department of Transportation, 20.


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Networks, Crowds, and Markets: Reasoning About a Highly Connected World by David Easley, Jon Kleinberg

Albert Einstein, AltaVista, AOL-Time Warner, Apollo 13, classic study, clean water, conceptual framework, Daniel Kahneman / Amos Tversky, Douglas Hofstadter, Dutch auction, Erdős number, experimental subject, first-price auction, fudge factor, Garrett Hardin, George Akerlof, Gerard Salton, Gerard Salton, Gödel, Escher, Bach, incomplete markets, information asymmetry, information retrieval, John Nash: game theory, Kenneth Arrow, longitudinal study, market clearing, market microstructure, moral hazard, Nash equilibrium, Network effects, Pareto efficiency, Paul Erdős, planetary scale, power law, prediction markets, price anchoring, price mechanism, prisoner's dilemma, random walk, recommendation engine, Richard Thaler, Ronald Coase, sealed-bid auction, search engine result page, second-price auction, second-price sealed-bid, seminal paper, Simon Singh, slashdot, social contagion, social web, Steve Jobs, Steve Jurvetson, stochastic process, Ted Nelson, the long tail, The Market for Lemons, the strength of weak ties, The Wisdom of Crowds, trade route, Tragedy of the Commons, transaction costs, two and twenty, ultimatum game, Vannevar Bush, Vickrey auction, Vilfredo Pareto, Yogi Berra, zero-sum game

LINK ANALYSIS AND WEB SEARCH Given that the two formulations of PageRank — based on repeated improvement and random walks respectively — are equivalent, we do not strictly speaking gain anything at a formal level by having this new definition. But the analysis in terms of random walks provides some additional intuition for PageRank as a measure of importance: the PageRank of a page X is the limiting probability that a random walk across hyperlinks will end up at X, as we run the walk for larger and larger numbers of steps. This equivalent definition using random walks also provides a new and sometimes useful perspective for thinking about some of the issues that came up earlier in the section.

. . . . . . . . . . . . . . . . 299 10.6 Advanced Material: A Proof of the Matching Theorem . . . . . . . . . . . . 300 10.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 310 11 Network Models of Markets with Intermediaries 319 11.1 Price-Setting in Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 319 11.2 A Model of Trade on Networks . . . . . . . . . . . . . . . . . . . . . . . . . 323 11.3 Equilibria in Trading Networks . . . . . . . . . . . . . . . . . . . . . . . . . 330 11.4 Further Equilibrium Phenomena: Auctions and Ripple Effects . . . . . . . . 334 11.5 Social Welfare in Trading Networks . . . . . . . . . . . . . . . . . . . . . . . 338 11.6 Trader Profits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 339 11.7 Reflections on Trade with Intermediaries . . . . . . . . . . . . . . . . . . . . 342 11.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 342 12 Bargaining and Power in Networks 347 12.1 Power in Social Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347 12.2 Experimental Studies of Power and Exchange . . . . . . . . . . . . . . . . . 350 12.3 Results of Network Exchange Experiments . . . . . . . . . . . . . . . . . . . 352 12.4 A Connection to Buyer-Seller Networks . . . . . . . . . . . . . . . . . . . . . 356 12.5 Modeling Two-Person Interaction: The Nash Bargaining Solution . . . . . . 357 12.6 Modeling Two-Person Interaction: The Ultimatum Game . . . . . . . . . . . 360 12.7 Modeling Network Exchange: Stable Outcomes . . . . . . . . . . . . . . . . 362 12.8 Modeling Network Exchange: Balanced Outcomes . . . . . . . . . . . . . . . 366 12.9 Advanced Material: A Game-Theoretic Approach to Bargaining . . . . . . . 369 12.10Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 376 IV Information Networks and the World Wide Web 381 13 The Structure of the Web 383 13.1 The World Wide Web . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 384 13.2 Information Networks, Hypertext, and Associative Memory . . . . . . . . . . 386 13.3 The Web as a Directed Graph . . . . . . . . . . . . . . . . . . . . . . . . . . 394 13.4 The Bow-Tie Structure of the Web . . . . . . . . . . . . . . . . . . . . . . . 397 13.5 The Emergence of Web 2.0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . 400 13.6 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 403 6 CONTENTS 14 Link Analysis and Web Search 405 14.1 Searching the Web: The Problem of Ranking . . . . . . . . . . . . . . . . . . 405 14.2 Link Analysis using Hubs and Authorities . . . . . . . . . . . . . . . . . . . 407 14.3 PageRank . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 414 14.4 Applying Link Analysis in Modern Web Search . . . . . . . . . . . . . . . . 420 14.5 Applications beyond the Web . . . . . . . . . . . . . . . . . . . . . . . . . . 423 14.6 Advanced Material: Spectral Analysis, Random Walks, and Web Search . . . 425 14.7 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437 15 Sponsored Search Markets 445 15.1 Advertising Tied to Search Behavior . . . . . . . . . . . . . . . . . . . . . . 445 15.2 Advertising as a Matching Market . . . . . . . . . . . . . . . . . . . . . . . . 448 15.3 Encouraging Truthful Bidding in Matching Markets: The VCG Principle . . 452 15.4 Analyzing the VCG Procedure: Truth-Telling as a Dominant Strategy . . . . 457 15.5 The Generalized Second Price Auction . . . . . . . . . . . . . . . . . . . . . 460 15.6 Equilibria of the Generalized Second Price Auction . . . . . . . . . . . . . . 464 15.7 Ad Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 467 15.8 Complex Queries and Interactions Among Keywords . . . . . . . . . . . . . 469 15.9 Advanced Material: VCG Prices and the Market-Clearing Property . . . . . 470 15.10Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486 V Network Dynamics: Population Models 489 16 Information Cascades 491 16.1 Following the Crowd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 491 16.2 A Simple Herding Experiment . . . . . . . . . . . . . . . . . . . . . . . . . . 493 16.3 Bayes’ Rule: A Model of Decision-Making Under Uncertainty . . . . . . . . . 497 16.4 Bayes’ Rule in the Herding Experiment . . . . . . . . . . . . . . . . . . . . . 502 16.5 A Simple, General Cascade Model . . . . . . . . . . . . . . . . . . . . . . . . 504 16.6 Sequential Decision-Making and Cascades . . . . . . . . . . . . . . . . . . . 508 16.7 Lessons from Cascades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 511 16.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 513 17 Network Effects 517 17.1 The Economy Without Network Effects . . . . . . . . . . . . . . . . . . . . . 518 17.2 The Economy with Network Effects . . . . . . . . . . . . . . . . . . . . . . . 522 17.3 Stability, Instability, and Tipping Points . . . . . . . . . . . . . . . . . . . . 525 17.4 A Dynamic View of the Market . . . . . . . . . . . . . . . . . . . . . . . . . 527 17.5 Industries with Network Goods . . . . . . . . . . . . . . . . . . . . . . . . . 534 17.6 Mixing Individual Effects with Population-Level Effects . . . . . . . . . . . . 536 17.7 Advanced Material: Negative Externalities and The El Farol Bar Problem . 541 17.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 549 CONTENTS 7 18 Power Laws and Rich-Get-Richer Phenomena 553 18.1 Popularity as a Network Phenomenon . . . . . . . . . . . . . . . . . . . . . . 553 18.2 Power Laws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555 18.3 Rich-Get-Richer Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 557 18.4 The Unpredictability of Rich-Get-Richer Effects . . . . . . . . . . . . . . . . 559 18.5 The Long Tail . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 561 18.6 The Effect of Search Tools and Recommendation Systems . . . . . . . . . . . 564 18.7 Advanced Material: Analysis of Rich-Get-Richer Processes . . . . . . . . . . 565 18.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 569 VI Network Dynamics: Structural Models 571 19 Cascading Behavior in Networks 573 19.1 Diffusion in Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 573 19.2 Modeling Diffusion through a Network . . . . . . . . . . . . . . . . . . . . . 575 19.3 Cascades and Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583 19.4 Diffusion, Thresholds, and the Role of Weak Ties . . . . . . . . . . . . . . . 588 19.5 Extensions of the Basic Cascade Model . . . . . . . . . . . . . . . . . . . . . 590 19.6 Knowledge, Thresholds, and Collective Action . . . . . . . . . . . . . . . . . 593 19.7 Advanced Material: The Cascade Capacity . . . . . . . . . . . . . . . . . . . 597 19.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 20 The Small-World Phenomenon 621 20.1 Six Degrees of Separation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 621 20.2 Structure and Randomness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 622 20.3 Decentralized Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626 20.4 Modeling the Process of Decentralized Search . . . . . . . . . . . . . . . . . 629 20.5 Empirical Analysis and Generalized Models . . . . . . . . . . . . . . . . . . 632 20.6 Core-Periphery Structures and Difficulties in Decentralized Search . . . . . . 638 20.7 Advanced Material: Analysis of Decentralized Search . . . . . . . . . . . . . 640 20.8 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652 21 Epidemics 655 21.1 Diseases and the Networks that Transmit Them . . . . . . . . . . . . . . . . 655 21.2 Branching Processes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657 21.3 The SIR Epidemic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 660 21.4 The SIS Epidemic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666 21.5 Synchronization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 669 21.6 Transient Contacts and the Dangers of Concurrency . . . . . . . . . . . . . . 672 21.7 Genealogy, Genetic Inheritance, and Mitochondrial Eve . . . . . . . . . . . . 676 21.8 Advanced Material: Analysis of Branching and Coalescent Processes . . . . . 682 21.9 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695 8 CONTENTS VII Institutions and Aggregate Behavior 699 22 Markets and Information 701 22.1 Markets with Exogenous Events . . . . . . . . . . . . . . . . . . . . . . . . . 702 22.2 Horse Races, Betting, and Beliefs . . . . . . . . . . . . . . . . . . . . . . . . 704 22.3 Aggregate Beliefs and the “Wisdom of Crowds” . . . . . . . . . . . . . . . . 710 22.4 Prediction Markets and Stock Markets . . . . . . . . . . . . . . . . . . . . . 714 22.5 Markets with Endogenous Events . . . . . . . . . . . . . . . . . . . . . . . . 717 22.6 The Market for Lemons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 719 22.7 Asymmetric Information in Other Markets . . . . . . . . . . . . . . . . . . . 724 22.8 Signaling Quality . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 728 22.9 Quality Uncertainty On-Line: Reputation Systems and Other Mechanisms . 729 22.10Advanced Material: Wealth Dynamics in Markets . . . . . . . . . . . . . . . 732 22.11Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 740 23 Voting 745 23.1 Voting for Group Decision-Making . . . . . . . . . . . . . . . . . . . . . . . 745 23.2 Individual Preferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 747 23.3 Voting Systems: Majority Rule . . . . . . . . . . . . . . . . . . . . . . . . . 750 23.4 Voting Systems: Positional Voting . . . . . . . . . . . . . . . . . . . . . . . . 755 23.5 Arrow’s Impossibility Theorem . . . . . . . . . . . . . . . . . . . . . . . . . 758 23.6 Single-Peaked Preferences and the Median Voter Theorem . . . . . . . . . . 760 23.7 Voting as a Form of Information Aggregation . . . . . . . . . . . . . . . . . . 766 23.8 Insincere Voting for Information Aggregation . . . . . . . . . . . . . . . . . . 768 23.9 Jury Decisions and the Unanimity Rule . . . . . . . . . . . . . . . . . . . . . 771 23.10Sequential Voting and the Relation to Information Cascades . . . . . . . . . 776 23.11Advanced Material: A Proof of Arrow’s Impossibility Theorem . . . . . . . . 777 23.12Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 782 24 Property Rights 785 24.1 Externalities and the Coase Theorem . . . . . . . . . . . . . . . . . . . . . . 785 24.2 The Tragedy of the Commons . . . . . . . . . . . . . . . . . . . . . . . . . . 790 24.3 Intellectual Property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 793 24.4 Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 796 Chapter 1 Overview Over the past decade there has been a growing public fascination with the complex “connectedness” of modern society.

(If their current page has no out-going links, they just stay where they are.) Such an exploration of nodes performed by randomly following links is called a random walk on the network. We should stress that this is not meant to be an accurate model of an actual person exploring the Web; rather, it is a thought experiment that leads to a particular definition. In Section 14.6, we analyze this random walk and show the following fact: Claim: The probability of being at a page X after k steps of this random walk is precisely the PageRank of X after k applications of the Basic PageRank Update Rule. 2As an aside about our earlier motivating example, one can check that using a value of s in this range doesn’t completely fix the problem with Figure 14.8: nodes F and G still get most (though no longer all) of the PageRank under the scaled update rule with such values of s.


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Millionaire Teacher: The Nine Rules of Wealth You Should Have Learned in School by Andrew Hallam

Albert Einstein, asset allocation, Bernie Madoff, buy and hold, diversified portfolio, financial independence, George Gilder, index fund, John Bogle, junk bonds, Long Term Capital Management, low interest rates, Mary Meeker, new economy, passive investing, Paul Samuelson, Ponzi scheme, pre–internet, price stability, random walk, risk tolerance, Silicon Valley, South China Sea, stocks for the long run, survivorship bias, transaction costs, Vanguard fund, yield curve

Interestingly, more than 98 percent of invested mutual fund money gets pushed into Morningstar’s top-rated funds25 But choosing which actively managed mutual fund will perform well in the future is, in Burton Malkiel’s words: “. . . like an obstacle course through hell’s kitchen.”26 Malkiel, a professor of economics at Princeton University and the bestselling author of A Random Walk Guide to Investing, adds: There is no way to choose the best [actively managed mutual fund] managers in advance. I have calculated the results of employing strategies of buying the funds with the best recent-year performance, best recent two-year performance, best five-year and ten-year performance, and not one of these strategies produced above average returns.

Bogle, Common Sense on Mutual Funds, 376. 22. Ibid. 23. Dilbert Comics, Reprinted with permission, Order Receipt #1591582. 24. John C. Bogle, The Little Book of Common Sense Investing, 90. 25. John C. Bogle, Don’t Count On It! (Hoboken, New Jersey: John Wiley & Sons, 2011), 382. 26. Burton Malkiel, The Random Walk Guide to Investing (New York: Norton, 2003), 130. 27. Ibid. 28. Bruce Kelly, “Raymond James Unit Gives Bonuses to Big Producers,” Investment News—The Leading Source for Financial Advisors, June 18, 2007. 29. Carole Gould, “Mutual Funds Report; A Seven-Year Lesson in Investing: Expect the Unexpected, and More,” The New York Times, July 9, 2000, accessed April 15, 2011, http://www.nytimes.com/2000/07/09/business/mutual-funds-report-seven-year-lesson-investing-expect-unexpected-more.html?.

Investing $10,000 in a few of the new millennium’s most popular stocks during 2000 would have resulted in some devastating losses for investors. Table 4.4 How Investors were Punished Source: Morningstar and Burton Malkiel, A Random Walk Down Wall Street, 200312 Formerly Hot Stocks $10,000 Invested at the Market High in 2000 Value of the Same $10,000 at the Low of 2001–2002 Amazon.com $10,000 $700 Cisco Systems $10,000 $990 Corning Inc. $10,000 $100 JDS Uniphase $10,000 $50 Lucent Technologies $10,000 $70 Nortel Networks $10,000 $30 Priceline.com $10,000 $60 Yahoo! $10,000 $360 The stories of wealth enticed individual investors and fund management firms alike before the eventual collapse of the dot-com bubble.


pages: 404 words: 107,356

The Future of Fusion Energy by Jason Parisi, Justin Ball

Albert Einstein, Arthur Eddington, Boeing 747, carbon footprint, carbon tax, Colonization of Mars, cuban missile crisis, decarbonisation, electricity market, energy security, energy transition, heat death of the universe, Intergovernmental Panel on Climate Change (IPCC), invention of the steam engine, ITER tokamak, Kickstarter, Large Hadron Collider, megaproject, Mikhail Gorbachev, mutually assured destruction, nuclear winter, performance metric, profit motive, random walk, Richard Feynman, Ronald Reagan, Stuxnet, the scientific method, time dilation, uranium enrichment

Because gyration allows particles to stray from their field line by a gyroradius, a collision can cause a particle to jump to a new magnetic surface that is a gyroradius away (see Figure 4.21 (left)). However, since the direction of the jump is random, sometimes this would move particles further into the device. Still, this process does cause the transport of particles (and their energy) through what is called random walk diffusion (see the following Tech Box). TECH BOX: Random walk diffusion To understand random walk diffusion, imagine that you’re at a swimming pool, standing in the middle of a diving board. Now flip a coin. If it’s heads take a step forward and if it’s tails take a step backwards. Then flip the coin again and take another step. Repeat again and again.

Note that both trajectories are projected onto the poloidal plane and the neoclassical one is averaged over the particle gyration. If we start with a dense clump of particles in the center of our tokamak, collisions and random walk diffusion will cause them to travel outwards. Eventually, after many collisions, particles will start to reach the outermost magnetic surface and escape the device. Using the mathematics of random walks, we can estimate how long this will take in order to determine the confinement time (as is done in the above Tech Box). What we find is a confinement time of a few minutes, much longer than our goal of 1 second!

When the mathematics of this situation are analyzed, we find that, after a time t, there is a 62% chance that you’ll be more than a distance away from your starting location. Here x is the total net distance traveled, t is the total elapsed time, Δx is the distance traveled per step, and Δt is the time between steps. We see that, as the time t increases, a particle becomes more likely to be increasingly far away. Hence, a random walk process will transport particles out of the device, but it does so much slower than the usual directed motion. We can use Equation (4.1) to estimate the confinement time in a tokamak. If we set x to be the distance from the center to the edge of the plasma, then t will be a typical time it takes for a particle to escape.


pages: 354 words: 26,550

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems by Irene Aldridge

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial engineering, financial intermediation, fixed income, global macro, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, Jim Simons, law of one price, Long Term Capital Management, Louis Bachelier, machine readable, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, Performance of Mutual Funds in the Period, pneumatic tube, profit motive, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, Small Order Execution System, statistical arbitrage, statistical model, stochastic process, stochastic volatility, systematic trading, tail risk, trade route, transaction costs, value at risk, yield curve, zero-sum game

These tests help traders evaluate the state of the markets and reallocate trading capital to the markets with the most inefficiencies—that is, the most opportunities for reaping profits. When price changes are random, they are said to follow a “random walk.” Formally, a random walk process is specified as follows: ln Pt = ln Pt−1 + εt (7.2) where ln Pt is the logarithm of the price of the financial security of interest at time t, ln Pt-1 is the logarithm of the price of the security one time TABLE 7.4 Non-Parametric Runs Test Applied to Data on Various Securities and Frequencies Recorded on June 8, 2009. Ticker Data Frequency N1 N2 u Z Result SPY SPY T T USD/JPY USD/JPY XAU/USD XAU/USD 1 10 1 10 1 10 1 10 179 21 142 18 558 68 685 75 183 17 138 17 581 64 631 66 193 20 187 20 775 82 778 76 1.11 −0.10 5.45 0.35 12.11 2.55 6.61 0.73 Random Random Predictable Random Predictable Predictable Predictable Random minute minutes minute minutes minute minutes minute minutes Market Inefficiency and Profit Opportunities at Different Frequencies 83 interval removed at a predefined frequency (minute, hour, etc.), and εt is the error term with mean 0.

Ticker Data Frequency N1 N2 u Z Result SPY SPY T T USD/JPY USD/JPY XAU/USD XAU/USD 1 10 1 10 1 10 1 10 179 21 142 18 558 68 685 75 183 17 138 17 581 64 631 66 193 20 187 20 775 82 778 76 1.11 −0.10 5.45 0.35 12.11 2.55 6.61 0.73 Random Random Predictable Random Predictable Predictable Predictable Random minute minutes minute minutes minute minutes minute minutes Market Inefficiency and Profit Opportunities at Different Frequencies 83 interval removed at a predefined frequency (minute, hour, etc.), and εt is the error term with mean 0. From equation (7.2), log price changes ln Pt are obtained as follows: ln Pt = ln Pt − ln Pt−1 = εt At any given time, the change in log price is equally likely to be positive and negative. The logarithmic price specification ensures that the model does not allow prices to become negative (logarithm of a negative number does not exist). The random walk process can drift, and be specified as shown in equation (7.3): ln Pt = µ + ln Pt−1 + εt (7.3) In this case, the average change in prices equals the drift rather than 0, since ln Pt = ln Pt − ln Pt−1 = µ + εt .

The drift can be due to a variety of factors; persistent inflation, for example, would uniformly lower the value of the U.S. dollar, inflicting a small positive drift on prices of all U.S. equities. At very high frequencies, however, drifts are seldom noticeable. Lo and MacKinlay (1988) developed a popular test for whether or not a given price follows a random walk. The test can be applied to processes with or without drift. The test procedure is built around the following principle: if price changes measured at a given frequency (e.g., one hour) are random, then price changes measured at a lower frequency (e.g., two hours) should also be random. Furthermore, the variances of the 1-hour and 2-hour changes should be deterministically related.


pages: 695 words: 194,693

Money Changes Everything: How Finance Made Civilization Possible by William N. Goetzmann

Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, book value, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, Cass Sunstein, classic study, collective bargaining, colonial exploitation, compound rate of return, conceptual framework, Cornelius Vanderbilt, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, delayed gratification, Detroit bankruptcy, disintermediation, diversified portfolio, double entry bookkeeping, Edmond Halley, en.wikipedia.org, equity premium, equity risk premium, financial engineering, financial independence, financial innovation, financial intermediation, fixed income, frictionless, frictionless market, full employment, high net worth, income inequality, index fund, invention of the steam engine, invention of writing, invisible hand, James Watt: steam engine, joint-stock company, joint-stock limited liability company, laissez-faire capitalism, land bank, Louis Bachelier, low interest rates, mandelbrot fractal, market bubble, means of production, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, new economy, passive investing, Paul Lévy, Ponzi scheme, price stability, principal–agent problem, profit maximization, profit motive, public intellectual, quantitative trading / quantitative finance, random walk, Richard Thaler, Robert Shiller, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, spice trade, stochastic process, subprime mortgage crisis, Suez canal 1869, Suez crisis 1956, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, time value of money, tontine, too big to fail, trade liberalization, trade route, transatlantic slave trade, tulip mania, wage slave

Evidently none knew of Bachelier, and thus they had to retrace the mathematical logic of fair prices and random walks when they began work on the problem of option pricing in the late 1960s. Like Bachelier, they relied on a model of variation in prices—Brownian motion—although unlike Bachelier, they chose one that did not allow prices to become negative—a limitation of Bachelier’s work. The Black-Scholes formula, as it is now referred to, was mathematically sophisticated, but at its heart it contained a novel economic—as opposed to mathematical—insight. They discovered that the invisible hand setting option prices was risk-neutral. Option payoffs could be replicated risklessly, provided one could trade in an ideal, frictionless market in which stocks behaved according to Brownian motion.

Not only did he emphasize that market sentiment could push prices away from fundamental values, he also did not believe in the statistical foundation of Jules Regnault’s random walk theory. Keynes’s most academic book is A Treatise on Probability, written when he returned to Cambridge after the Paris negotiations. The book argues that the statistician should not automatically assume that there is a central tendency in the data. Recall from Chapter 16 Regnault’s example of many people looking at the same security and the resulting efficient market price. Keynes cautions that such a mechanism might not apply in markets.

The option pricing model is based on the principle of forecasting the range of future outcomes of the stock price by assuming it will follow a random walk that conforms to Regnault’s square-root of time insight. However, the Black-Scholes formula gives a solution to the option price today by mathematically rolling time backward. It reverses entropy. In this, it echoes the most basic trait of finance—it uses mathematics to transcend time. THERMODYNAMICS The Black-Scholes formula was published in 1973, just around the time that the Chicago Board Option Exchange began to trade standardized option contracts. Like Bachelier’s thesis, the path-breaking paper was not at first well received. The Journal of Political Economy, where it ultimately was published, needed serious urging from Chicago Professor Merton Miller to be convinced of its contribution.


pages: 1,164 words: 309,327

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

active measures, Andrei Shleifer, AOL-Time Warner, asset allocation, automated trading system, barriers to entry, Bernie Madoff, Bob Litterman, book value, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, equity risk premium, fault tolerance, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, information security, interest rate swap, invention of the telegraph, job automation, junk bonds, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, National best bid and offer, Nick Leeson, open economy, passive investing, pattern recognition, payment for order flow, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, proprietary trading, race to the bottom, random walk, Reminiscences of a Stock Operator, rent-seeking, risk free rate, risk tolerance, risk-adjusted returns, search costs, selection bias, shareholder value, short selling, short squeeze, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, vertical integration, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game

Fundamental value changes therefore must be unpredictable. Since prices are very close to fundamental values in efficient markets, price changes in efficient markets are quite unpredictable. When traders cannot predict future price changes, statisticians say that prices follow a random walk. Plots of random walks through time look like paths that wander up or down at random because random walks are completely unpredictable. * * * ▶ Fischer Black on Noise Fischer Black was a mathematician who made many seminal contributions to the development of financial theory. Perhaps most notably, he helped develop option-pricing theory, for which Myron Scholes and Robert Merton received the 1997 Nobel Prize in economic science.

Markets are weak-form efficient if prices reflect all information in past prices so that no one can predict future price changes from knowing only past prices. In weak-form efficient markets, price charts and statistical analyses of past prices are useless. Prices simply appear to follow a random walk. Most published empirical studies have determined that markets are weak-form efficient. (Of course, if researchers found otherwise, they might trade on their results rather than publish them!) Markets are semistrong-form efficient if prices reflect all publicly available information so that no one can predict future price changes using only public information.

Statisticians call this process a random walk because it describes the path a walker would take if after every step he flipped a coin to decide whether to next step forward or backward. Fully informative prices seem to follow random walks because no one can predict future price changes from past information when prices fully reflect that information. When price changes are unpredictable, they appear random. ◀ * * * Speculative arbitrages involve nonstationary hedge portfolios that arbitrageurs believe have a strong tendency toward short-term mean reversion. The nonstationariness is due to instrument-specific valuation factors that cause prices to follow a random walk in the long run. The mean reversion may come from inconsistent pricing of the common factors among the instruments in the hedge portfolio or from mispricing of one or more specific factors.


pages: 119 words: 10,356

Topics in Market Microstructure by Ilija I. Zovko

Brownian motion, computerized trading, continuous double auction, correlation coefficient, financial intermediation, Gini coefficient, information asymmetry, market design, market friction, market microstructure, Murray Gell-Mann, p-value, power law, quantitative trading / quantitative finance, random walk, stochastic process, stochastic volatility, transaction costs

One of the predictions of the model, that to our knowledge has not been hypothesized elsewhere in the literature, is that the order size σ is an important determinant of the spread. Another prediction of the model concerns the price diffusion rate, which drives the volatility of prices and is the primary determinant of financial risk. If we assume that prices make a random walk, then the diffusion rate measures the size and frequency of its increments. The variance V of a random walk grows as V (t) = Dt, where D is the diffusion rate and t is time. This is the main free parameter in the Bachelier model of prices (Bachelier, 1964). While its value is essential for risk estimation and derivative pricing there is very little fundamental understanding of what actually determines it.

These show that while daily variations in W do give additional predictability for the spread, other aspects of the model are substantially responsible for these results. Measuring the price diffusion rate The measurement of the price diffusion rate requires some discussion. We measure the intraday price diffusion by computing the mid-point price variance V (τ ) = Var{m(t + τ ) − m(t)}, for different time scales τ . The averaging over t includes all events that change the mid-point price. The plot of V (τ ) against τ is called a diffusion curve and for an IID random walk is a straight line with slope D, the diffusion coefficient. 46 CHAPTER 3. THE PREDICTIVE POWER OF ZERO INTELLIGENCE IN FINANCIAL MARKETS 0.00008 0.00006 0.00004 Vodafone, August 4 1998 D=1.498e!

THE PREDICTIVE POWER OF ZERO INTELLIGENCE IN FINANCIAL MARKETS ship σ 2 (τ ) = Dτ 2H to a good approximation for all values of τ , where σ 2 (τ ) is the variance of price changes or returns computed on timescale τ . The Hurst exponent H is close to and typically a little greater than 0.5. In contrast, under Poisson order flow, as already discussed above, due to the dynamics of the double continuous auction price formation process, prices make a strongly anti-correlated random walk. This means that the function σ 2 (τ ) is nonlinear. Asymptotically H = 0.5, but for shorter times H < 0.5. Alternatively, one can characterize this in terms of a timescale-dependent diffusion rate D(τ ), so that the variance of prices increases as σ 2 (τ ) = D(τ )τ .


pages: 354 words: 105,322

The Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis by James Rickards

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Bayesian statistics, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Benoit Mandelbrot, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black Swan, blockchain, Boeing 747, Bonfire of the Vanities, Bretton Woods, Brexit referendum, British Empire, business cycle, butterfly effect, buy and hold, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, cellular automata, cognitive bias, cognitive dissonance, complexity theory, Corn Laws, corporate governance, creative destruction, Credit Default Swap, cuban missile crisis, currency manipulation / currency intervention, currency peg, currency risk, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, disintermediation, distributed ledger, diversification, diversified portfolio, driverless car, Edward Lorenz: Chaos theory, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, fiat currency, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, Fractional reserve banking, G4S, George Akerlof, Glass-Steagall Act, global macro, global reserve currency, high net worth, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Isaac Newton, jitney, John Meriwether, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Rogoff, labor-force participation, large denomination, liquidity trap, Long Term Capital Management, low interest rates, machine readable, mandelbrot fractal, margin call, market bubble, Mexican peso crisis / tequila crisis, Minsky moment, Money creation, money market fund, mutually assured destruction, Myron Scholes, Naomi Klein, nuclear winter, obamacare, offshore financial centre, operational security, Paul Samuelson, Peace of Westphalia, Phillips curve, Pierre-Simon Laplace, plutocrats, prediction markets, price anchoring, price stability, proprietary trading, public intellectual, quantitative easing, RAND corporation, random walk, reserve currency, RFID, risk free rate, risk-adjusted returns, Robert Solow, Ronald Reagan, Savings and loan crisis, Silicon Valley, sovereign wealth fund, special drawing rights, stock buybacks, stocks for the long run, tech billionaire, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transfer pricing, value at risk, Washington Consensus, We are all Keynesians now, Westphalian system

Their individual behavior is random. Do they cause markets as a whole to be random? Or do they cause bulls to become bears, and vice versa, producing nonrandom persistence? Research conducted by physicists Neil Johnson, Pak Ming Hui, and Paul Jefferies using financial market data shows the price movement pattern in markets does not correspond to the so-called random walk model that is the foundation of modern financial economics. Instead, behavior corresponds to predictions of complexity theorists using principles of feedback and adaptive behavior. Behavior in financial markets can be broken down into binary choices, expressed as “either/or” or “yes/no” answers to a series of questions.

If t = 9 (the total steps taken), and ta ≈ 3 (total positions moved as shown by the random walk output), then a ≈ 0.5. The total movement in the 9-step random walk is 3 = 90.5. In a highly ordered walk a = 1.0. In a random or disordered walk a = 0.5. What type of walk do actual markets take? Stated formally, what is the value of a based on actual market price movements? One characteristic of complex systems is they are neither highly ordered nor random. Complex systems oscillate between order and disorder. This oscillation comes from agents’ deciding to quit the crowd and join the anticrowd or vice versa. A complex system that begins with random behavior can become ordered through feedback and adaptive behavior.

These strings can be computer coded and analyzed for patterns in large data sets and long time series. The answers are highly revealing about how markets actually work. The random walk model, associated with Princeton professor Burton G. Malkiel, says these decisions resemble a drunk walking down the street. Each step is uncertain. It could be forward or backward. The drunk doesn’t know himself. Each step is random, unaffected by the one before. There is no memory, there is no feedback. The random walk model and the crowd-anticrowd model should produce completely different patterns of 1s and 0s because the random walk has no memory and the crowd does. Patterns produced by each model are quantified, and the model projections compared to experimental data.


The Smartest Investment Book You'll Ever Read: The Simple, Stress-Free Way to Reach Your Investment Goals by Daniel R. Solin

Alan Greenspan, asset allocation, buy and hold, corporate governance, diversification, diversified portfolio, index fund, John Bogle, market fundamentalism, money market fund, Myron Scholes, PalmPilot, passive investing, prediction markets, prudent man rule, random walk, risk tolerance, risk-adjusted returns, risk/return, transaction costs, Vanguard fund, zero-sum game

While a number of books have been written about the virtues of being a Smart Investor, few have achieved commercial 90 Your Broker or Advisor Is Keeping You from Being a Smart Investor success. One exception is A Random Walk Down Wall Street, a superb book by Bunon Malkiel, now in its eighth edition. Malkiel, a professor of econom ics at Princeton University, was one of the first to show that the history of the price of a stock cannot be used to predict how it will move in the future, and therefore that stock price movement is, in the language of economists. "random." In other words, he completely debunked the belief that anyone can consistently predict [he future prices of stocks (which is the core belief of Hyperactive Investors!). Most of the books and anicles written on the merits of being a Smarr Investor are, unfortunately, dense and difficult to understand-thus seemingly validating the myth that being a Smart Investor is somehow elitist, complex and beyond the ability of the ordinary investor.

by John Bogle. in Scott Simon's book Inda Mutual Funds: Profiting from an In mtmmt Rrvolution; see also the article by Edward S. O'Neal, discussed in Chapter 13. and a study by Dalbar, Inc .• a well~ respected research firm. Reported at http://www.dalbarinc.com/ con ten tIshowpage.asp ?page=200 1062 100. Burton Malkiel summarizes these studies in A Random Walk Down Wall Strut, p. 187. In Mark Hebner's book, Index Funds: Tht i2-Sup Program for Actiw InvtstorJ (pp. 47-53), he sets forth the studies showing the lack of consistency of mutual fund performance and the daunting odds of picking an actively managed fund that will outperform its benchmark index.

It is summarized at: http://socialize.morningstar.com/New Socialize/asp/FullConv.asp?forumId=F 1000000 15&lastConv Seq=41356 . Clements is the rare exception to those financial journalists who routinely peddle "financial pornography." Too Good 10 Be True? 153 Here is what Burton Malkid has to say about charting (wh ich he likens to "alchemy") in his sem inal book, A Random Walk Down Wall Strut, (p. 165): "There has been a rematkable unjformity in the conclusions of studies done on all forms of technical ana1ysis. Not one has consistently outperformed the placebo of a buy-and-hold strategy. Technical methods cannot be used to make useful investment strategies ... Ma1kiel believes that chartistS simply provide cover fo r hyperactive brokers to encourage more trading-generating more fees-by their unsuspecting clients.


pages: 335 words: 94,657

The Bogleheads' Guide to Investing by Taylor Larimore, Michael Leboeuf, Mel Lindauer

asset allocation, behavioural economics, book value, buy and hold, buy low sell high, corporate governance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, endowment effect, estate planning, financial engineering, financial independence, financial innovation, high net worth, index fund, John Bogle, junk bonds, late fees, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, margin call, market bubble, mental accounting, money market fund, passive investing, Paul Samuelson, random walk, risk tolerance, risk/return, Sharpe ratio, statistical model, stocks for the long run, survivorship bias, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

We think it deserves a place on the bookshelf of every serious investor. Professor Malkiel describes a random walk this way: "One in which future steps or directions cannot be predicted on the basis of past action. When the term is applicable to the stock market, it means that short-run changes in stock prices cannot be predicted. " Another, more vivid, description of a random walk: A drunk standing in the middle of the road whose future movements can only be guessed. " Few academics argue that the stock market is totally efficient. Nevertheless, they agree that stocks and bonds are so efficiently priced that the majority of investors, including full-time professional fund managers, will not outperform an unmanaged index fund after transaction costs.

The study again found "no evidence of ability to predict successfully the direction of the stock market. " In the 1960s, a University of Chicago Professor, Eugene E Fama, performed a detailed analysis of the ever-increasing volume of stock price data. He concluded that stock prices are very efficient and that it's extremely difficult to pick winning stocks-especially after factoring in the costs of transaction fees. In 1973, Princeton professor Burton Malkiel, after extensive research, came to the same conclusion as Bachelier, Cowles, and Fama. Professor Malkiel published a book with the catchy title Random Walk Down Wall Street. The book is now an investment classic, and updated revisions are published on a regular basis.

By rebalancing, you're selling a portion of your winning asset classes before they revert to the mean (drop in price) and you're buying more of your underperforming asset classes when their prices are lower, before they revert to the mean (increase in value). So, you're selling high and buying low. If you believe in RTM, rebalancing could increase your returns. Jack Bogle believes in RTM, and we do, too. Even if you don't believe that RTM will occur in the future, but rather, believe that the market is a random walk and that each market move is independent of previous moves, remember that you'll still benefit from rebalancing because you're controlling the level of risk in your portfolio.


Mathematical Finance: Core Theory, Problems and Statistical Algorithms by Nikolai Dokuchaev

Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, discrete time, electricity market, fixed income, implied volatility, incomplete markets, martingale, random walk, risk free rate, short selling, stochastic process, stochastic volatility, transaction costs, volatility smile, Wiener process, zero-coupon bond

Proposition 2.24 Under the assumptions and notations of Definition 2.23, for all measurable deterministic functions F such that the corresponding random variables are integrable. Problem 2.25 Prove that a discrete time random walk is a Markov process. Vector processes Let ξ(t)=(ξ1(t),…, ξn(t)) be a vector process such that all its components are random processes. Then ξ is said to be an n-dimensional (vector) random process. All definitions given above can be extended for these vector processes. Sometimes, we can convert a process that is not a Markov process to a Markov process of higher dimension. Example 2.26 Let ηt be a random walk, t=0, 1, 2,…, and let Then ψt is not a Markov process, but the vector process (ηt,ψt) is a Markov process. 2.5 Problems Problem 2.27 Let ζ be a random variable, and let 0≤a<b≤1.

For simplicity, we shall use below stationary processes and white noise in the sense of Definitions 8.1–8.3, but all results are valid for wide-sense stationary processes and for the white noise defined as a wide-sense stationary process with no correlation and zero mean. 8.2 Simplest regression and autoregression The first-order regression model can be described by a one-dimensional equation © 2007 Nikolai Dokuchaev Review of Statistical Estimation 141 yt=β0+βxt+εt, t=1, 2,…. (8.1) Here yt and xt represent observable discrete time processes; yt is called the regressand, or dependent variable, xt is called the regressor, or explanatory variable, εt is an unobserved and are parameters that are usually unknown. error term, The standard assumption is that (8.2) Special case: autoregression (AR) Let us describe the first-order autoregressive process, AR(1), as yt=β0+βyt−1+εt, (8.3) where εt is a white noise process, are parameters. The AR(1) model is a special case of the simplest regression (8.1), where xt= yt−1. It can be shown that εt is uncorrelated with {ys}s<t. If −1<β<1, then there exists a stationary process such that as t→+∞. If β=1 and β0=0 in (8.3), then yt is a random walk (see Definition 2.6). A random walk is non-stationary and it does not converge to any stationary process. In fact, if |β|≥1, then Var yt→+∞ as t→+∞. This implies that many standard tools for forecasting and testing coefficients etc. are invalid. To avoid this, we can try to study changes in yt instead: for example, the differences zt=yt−yt−1 may converge to a stationary process.

Definition 2.5 Let ξt, t=0, 1, 2,…, be a discrete time random process such that ξt are mutually independent and have the same distribution, and Eξt≡0. Then the process ξt is said to be a discrete time white noise. Definition 2.6 Let ξt be a discrete time white noise, and let t=0, 1, 2,…. Then the process ηt is said to be a random walk. The theory of stochastic processes studies their pathwise properties (or properties of trajectories ξ(t, ω) for given ω, as well as the evolution of the probability distributions. © 2007 Nikolai Dokuchaev Mathematical Finance 18 Definition 2.7 A continuous time process ξ(t)=ξ(t, ω) is said to be continuous (or pathwise continuous), if trajectories ξ(t, ω) are continuous in t a.s.


pages: 545 words: 137,789

How Markets Fail: The Logic of Economic Calamities by John Cassidy

Abraham Wald, Alan Greenspan, Albert Einstein, An Inconvenient Truth, Andrei Shleifer, anti-communist, AOL-Time Warner, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, Black-Scholes formula, Blythe Masters, book value, Bretton Woods, British Empire, business cycle, capital asset pricing model, carbon tax, Carl Icahn, centralized clearinghouse, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, corporate raider, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, Daniel Kahneman / Amos Tversky, debt deflation, different worldview, diversification, Elliott wave, Eugene Fama: efficient market hypothesis, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, Garrett Hardin, George Akerlof, Glass-Steagall Act, global supply chain, Gunnar Myrdal, Haight Ashbury, hiring and firing, Hyman Minsky, income per capita, incomplete markets, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Nash: game theory, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kickstarter, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Nixon triggered the end of the Bretton Woods system, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, Ponzi scheme, precautionary principle, price discrimination, price stability, principal–agent problem, profit maximization, proprietary trading, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, RAND corporation, random walk, Renaissance Technologies, rent control, Richard Thaler, risk tolerance, risk-adjusted returns, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, subprime mortgage crisis, tail risk, Tax Reform Act of 1986, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, Tragedy of the Commons, transaction costs, Two Sigma, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game

The coin-tossing model was resurrected: by the early 1960s, Samuelson and a number of other economists were publishing papers claiming that stock prices followed a random walk. One of these authors was Eugene Fama, an Italian American from Boston who was still in his early twenties. After paying his way through Tufts, Fama went to the University of Chicago, where he did his Ph.D. thesis on the behavior of stock prices, using the school’s spiffy new IBM mainframe to analyze data covering the period from 1926 to 1960. After providing a critical survey of previous research that had purported to find some predictability in stock returns, Fama reported details of his own statistical tests, which supported the random walk model. What made Fama’s paper especially distinctive was the criticism it contained of “fundamental analysis”—the type of stock research that many Wall Street professionals relied on, which involved deconstructing companies’ earnings reports, visiting factories, and so on.

Fama joined another firm that manages index funds, Dimensional Fund Advisors.) The rise of efficient market theory also signaled the beginning of quantitative finance. In addition to the random walk model of stock prices, the period between 1950 and 1970 saw the development of the mean-variance approach to portfolio diversification, which Harry Markowitz, another Chicago economist, pioneered; the capital asset pricing model, which a number of different scholars developed independently of one another; and the Black-Scholes option pricing formula, which Fischer Black, an applied mathematician from Harvard, and Myron Scholes, a finance Ph.D. from Chicago, developed.

After the Japanese real estate and stock bubble of the late 1980s, the technology stock bubble in the United States, and the housing bubble of 2002–2006, it is hard to say with a straight face that asset prices always reflect economic fundamentals, and that herding isn’t a major problem. The efficient market hypothesis has finally been discredited; even some of its original promoters admit it was oversold. In an article that appeared in The Journal of Economic Perspectives, Burton Malkiel, the author of A Random Walk Down Wall Street, wrote, “[P]ricing irregularities and even predictable patterns in stock returns can appear over time and even persist for short periods.” During the dot-com bubble, Malkiel allowed, “the stock market may have temporarily failed in its role as an efficient allocator of capital.”


pages: 236 words: 77,735

Rigged Money: Beating Wall Street at Its Own Game by Lee Munson

affirmative action, Alan Greenspan, asset allocation, backtesting, barriers to entry, Bear Stearns, Bernie Madoff, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, call centre, Credit Default Swap, diversification, diversified portfolio, estate planning, fear index, fiat currency, financial engineering, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, Glass-Steagall Act, global macro, High speed trading, housing crisis, index fund, joint-stock company, junk bonds, managed futures, Market Wizards by Jack D. Schwager, Michael Milken, military-industrial complex, money market fund, moral hazard, Myron Scholes, National best bid and offer, off-the-grid, passive investing, Ponzi scheme, power law, price discovery process, proprietary trading, random walk, Reminiscences of a Stock Operator, risk tolerance, risk-adjusted returns, risk/return, Savings and loan crisis, short squeeze, stocks for the long run, stocks for the long term, too big to fail, trade route, Vanguard fund, walking around money

See Over-the-Counter Over-the-Counter (OTC) P Panic of 1907 passive investing Pay-Up Amendment. See Section 28(e) penny stocks pension pension manager Philip Morris pie charts bar charts versus Pit Bull play-it-safe investment portfolio, moderate risk premium price compression price discovery The Price Is Right price, best prime broker prognostication reports Q QQQ. See NASDAQ 100 ETF R A Random Walk Down Wall Street rebalancing Registered Investment Adviser (RIA) reinvestment Reminisces of a Stock Operator research firms, independent research purpose third-party Revenue Act of 1978 RIA. See Registered Investment Adviser risk budgeting risk, level Rule 19b–3 S S&P 500, volatility versus San Francisco Earthquake scenarios, investment Schwab Affiliate Funds Schwager, Jack Schwartz, Martin Buzzy SEC.

See national best bid and offer New York Stock Exchange Regulation (NYSE Regulation) no-transaction-fee funds (NTFs) noise non-correlated assets A Non-Random Walk Down Wall Street NTFs. See no-transaction-fee funds NYSE Regulation. See New York Stock Exchange Regulation O OER. See operating expense ratio OneSourse Select List operating cost operating expense ratio (OER) opinion, strong OPRA. See Options Price Reporting Authority Options Price Reporting Authority (OPRA) options strategies trading OTC. See Over-the-Counter Over-the-Counter (OTC) P Panic of 1907 passive investing Pay-Up Amendment.

For example, U.S. stocks can be split up into large-cap growth, large-cap value, large-cap high-dividend yield, and large-cap sector-that-is-currently-going-up which you don’t own because your sector is going down. The pitch continues that the classes do not move in tandem, but in a random walk unrelated to each other. A random walk is an overused term. Does anyone really think global markets are just walking around aimlessly with no rhyme or reason? Several people have been awarded the Nobel Prize in Economics for suggesting this. Perhaps the winners are chosen randomly as well. Of course, all of these asset classes are expected to go up over time.


pages: 415 words: 125,089

Against the Gods: The Remarkable Story of Risk by Peter L. Bernstein

Alan Greenspan, Albert Einstein, Alvin Roth, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, behavioural economics, Big bang: deregulation of the City of London, Bretton Woods, business cycle, buttonwood tree, buy and hold, capital asset pricing model, cognitive dissonance, computerized trading, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Lloyd's coffeehouse, endowment effect, experimental economics, fear of failure, Fellow of the Royal Society, Fermat's Last Theorem, financial deregulation, financial engineering, financial innovation, full employment, Great Leap Forward, index fund, invention of movable type, Isaac Newton, John Nash: game theory, John von Neumann, Kenneth Arrow, linear programming, loss aversion, Louis Bachelier, mental accounting, moral hazard, Myron Scholes, Nash equilibrium, Norman Macrae, Paul Samuelson, Philip Mirowski, Post-Keynesian economics, probability theory / Blaise Pascal / Pierre de Fermat, prudent man rule, random walk, Richard Thaler, Robert Shiller, Robert Solow, spectrum auction, statistical model, stocks for the long run, The Bell Curve by Richard Herrnstein and Charles Murray, The Wealth of Nations by Adam Smith, Thomas Bayes, trade route, transaction costs, tulip mania, Vanguard fund, zero-sum game

The normal distribution provides a more rigorous test of the random-walk hypothesis. But one qualification is important. Even if the random walk is a valid description of reality in the stock market-even if changes in stock prices fall into a perfect normal distribution-the mean will be something different from zero. The upward bias should come as no surprise. The wealth of owners of common stocks has risen over the long run as the economy and the revenues and profits of corporations have grown. Since more stock-price movements have been up than down, the average change in stock prices should work out to more than zero. In fact, the average increase in stock prices (excluding dividend income) was 7.7% a year.

If independence is the necessary condition for a normal distribution, we can assume that evidence that distributes itself into a bell curve comes from observations that are independent of one another. Now we can begin to ask some interesting questions. How closely do changes in the prices of stocks resemble a normal distribution? Some authorities on market behavior insist that stock prices follow a random walk-that they resemble the aimless and unplanned lurches of a drunk trying to grab hold of a lamppost. They believe that stock prices have no more memory than a roulette wheel or a pair of dice, and that each observation is independent of the preceding observation. Today's price move will be whatever it is going to be, regardless of what happened a minute ago, yesterday, the day before, or the day before that.

Even though the contents of their portfolios change over time, serious investors tend to keep their money in the stock market for many years, even decades. Does the long run in the stock market really differ from the short run? If the random-walk view is correct, today's stock prices embody all relevant information. The only thing that would make them change is the availability of new information. Since we have no way of knowing what that new information might be, there is no mean for stock prices to regress to. In other words, there is no such thing as a temporary stock price-that is, a price that sits in limbo before moving to some other point. That is also why changes are unpredictable. But there are two other possibilities.


pages: 240 words: 60,660

Models. Behaving. Badly.: Why Confusing Illusion With Reality Can Lead to Disaster, on Wall Street and in Life by Emanuel Derman

Albert Einstein, Asian financial crisis, Augustin-Louis Cauchy, Black-Scholes formula, British Empire, Brownian motion, capital asset pricing model, Cepheid variable, creative destruction, crony capitalism, currency risk, diversified portfolio, Douglas Hofstadter, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial engineering, Financial Modelers Manifesto, fixed income, Ford Model T, Great Leap Forward, Henri Poincaré, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, Isaac Newton, Johannes Kepler, law of one price, low interest rates, Mikhail Gorbachev, Myron Scholes, quantitative trading / quantitative finance, random walk, Richard Feynman, riskless arbitrage, savings glut, Schrödinger's Cat, Sharpe ratio, stochastic volatility, the scientific method, washing machines reduced drudgery, yield curve

A MODEL FOR RISK Risk = The Uncertain Return on an Investment If you buy a stock for $100, you can imagine its price going up to $110 for a return of 10%, or down to $90 for a return of -10%. The risk of the stock is reflected in the range of possible returns you can envisage. A Random Walk for Stock Prices A company is a complex organism. How can one model the range of possible returns that a share of its stock might accrue? The Efficient Market Model’s answer to this question is radical: ignore complexity! It hypothesizes that the market, anthropomorphically speaking, has used all available knowledge about the company to determine the stock price. Therefore the next change in the stock price will arise only from new information, which will arrive randomly and therefore be equally likely to be good or bad as far as the company’s future returns are concerned.

The demand for a stock can become so great that its price leaps up; more commonly the market’s panic to sell can be so intense and contagious that the prices of all stocks crash downward, reflecting fear rather than the ideal of rational information and response. These kinds of events are not rare, yet they don’t fit into the framework of the smooth random walk economists like to focus on. And it is these events that provide much of the true risk as well as the reward of investing. The Efficient Market Model’s price movements are too constrained and elegant to reflect the market accurately. THE UNBEARABLE FUTILITY OF MODELING To use the Law of One Price that underpins financial modeling, one must show that a target security and its replicating portfolio have identical future payoffs under all circumstances.

The EMM’s picture of price movements goes by several names: a random walk, diffusion, and Brownian motion. One of its origins is in the description of the drift of pollen particles through a liquid as they collide with its molecules. Einstein used the diffusion model to successfully predict the square root of the average distance the pollen particles move through the liquid as a function of temperature and time, thus lending credence to the existence of hypothetical molecules and atoms too small to be seen. For particles of pollen, the model is also a theory, and pretty close to a true one. For stock prices, however, it’s only a model.


Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, book value, Brownian motion, business continuity plan, buy and hold, classic study, compound rate of return, Edward Thorp, Elliott wave, endowment effect, financial engineering, fixed income, general-purpose programming language, index fund, Jim Simons, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk free rate, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

T 115 P1: JYS c07 JWBK321-Chan September 24, 2008 14:4 116 Printer: Yet to come QUANTITATIVE TRADING MEAN-REVERTING VERSUS MOMENTUM STRATEGIES Trading strategies can be profitable only if securities prices are either mean-reverting or trending. Otherwise, they are randomwalking, and trading will be futile. If you believe that prices are mean reverting and that they are currently low relative to some reference price, you should buy now and plan to sell higher later. However, if you believe the prices are trending and that they are currently low, you should (short) sell now and plan to buy at an even lower price later. The opposite is true if you believe prices are high. Academic research has indicated that stock prices are on average very close to random walking. However, this does not mean that under certain special conditions, they cannot exhibit some degree of mean reversion or trending behavior.

STATIONARITY AND COINTEGRATION A time series is “stationary” if it never drifts farther and farther away from its initial value. In technical terms, stationary time series are “integrated of order zero,” or I(0). (See Alexander, 2001.) It is obvious that if the price series of a security is stationary, it would be a great candidate for a mean-reversion strategy. Unfortunately, most stock price series are not stationary—they exhibit a geometric random walk that gets them farther and farther away from their starting (i.e., initial public offering) values. However, you can often find P1: JYS c07 JWBK321-Chan September 24, 2008 14:4 Printer: Yet to come 127 Special Topics in Quantitative Trading a pair of stocks such that if you long one and short the other, the market value of the pair is stationary.

Example 6.1: An Interesting Puzzle (or Why Risk Is Bad for You)* Here is a little puzzle that may stymie many a professional trader. Suppose a certain stock exhibits a true (geometric) random walk, by which I mean there is a 50–50 chance that the stock is going up 1 percent or down 1 percent every minute. If you buy this stock, are you most likely—in the long run and ignoring financing costs—to make money, lose money, or be flat? Most traders will blurt out the answer “Flat!,” and that is wrong. The correct answer is that you will lose money, at the rate of 0.005 percent (or 0.5 basis point) every minute! This is because for a geometric random walk, the average compounded rate of return is not the short-term (or one-period) return m (0 here), but is g = m − s 2 /2.


pages: 923 words: 163,556

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures by Frank J. Fabozzi

algorithmic trading, Benoit Mandelbrot, Black Monday: stock market crash in 1987, capital asset pricing model, collateralized debt obligation, correlation coefficient, distributed generation, diversified portfolio, financial engineering, fixed income, global macro, index fund, junk bonds, Louis Bachelier, Myron Scholes, p-value, power law, quantitative trading / quantitative finance, random walk, risk free rate, risk-adjusted returns, short selling, stochastic volatility, subprime mortgage crisis, Thomas Bayes, transaction costs, value at risk

At time t, the price is considered to contain all information available. So at any point in time, next period’s price is exposed to a random shock. Consequently, the best estimate for the following period’s price is this period’s price. Such price processes are called efficient due to their immediate information processing. A more general model, for example, AR(p), of the formSt = α0 + α1St −1 + … + αp St −p + εt with several lagged prices could be considered as well. This price process would permit some slower incorporation of lagged prices into current prices. Now for the price to be a random walk process, the estimation would have to produce a0 = 0, a1 = 1, a2 = … = ap = 0.

One should not be discouraged if these models appear somewhat complicated at this early stage of one’s understanding of statistics. Random Walk Let us consider some price process given by the series {S}t.71 The dynamics of the process are given by(7.4) or, equivalently, ΔSt = εt . In words, tomorrow’s price, St+1, is thought of as today’s price plus some random shock that is independent of the price. As a consequence, in this model, known as the random walk, the increments St - St-1 from t−1 to t are thought of as completely undeterministic. Since the εt have a mean of zero, the increments are considered fair.72 An increase in price is as likely as a downside movement. At time t, the price is considered to contain all information available.

Application to S&P 500 Index Returns As an example to illustrate equation (7.4), consider the daily S&P 500 stock index prices between November 3, 2003 and December 31, 2003. The values are given in Table 7.2 along with the daily price changes. The resulting plot is given in Figure 7.4. The intuition given by the plot is roughly that, on each day, the information influencing the following day’s price is unpredictable and, hence, the price change seems completely arbitrary. Hence, at first glance much in this figure seems to support the concept of a random walk. Concerning the evolution of the underling price process, it looks reasonable to assume that the next day’s price is determined by the previous day’s price plus some random change.


pages: 303 words: 84,023

Heads I Win, Tails I Win by Spencer Jakab

Alan Greenspan, Asian financial crisis, asset allocation, backtesting, Bear Stearns, behavioural economics, Black Monday: stock market crash in 1987, book value, business cycle, buy and hold, collapse of Lehman Brothers, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, diversification, dividend-yielding stocks, dogs of the Dow, Elliott wave, equity risk premium, estate planning, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, fear index, fixed income, geopolitical risk, government statistician, index fund, Isaac Newton, John Bogle, John Meriwether, Long Term Capital Management, low interest rates, Market Wizards by Jack D. Schwager, Mexican peso crisis / tequila crisis, money market fund, Myron Scholes, PalmPilot, passive investing, Paul Samuelson, pets.com, price anchoring, proprietary trading, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, risk tolerance, risk-adjusted returns, Robert Shiller, robo advisor, Savings and loan crisis, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, subprime mortgage crisis, survivorship bias, technology bubble, transaction costs, two and twenty, VA Linux, Vanguard fund, zero-coupon bond, zero-sum game

Of course, there’s a gigantic venue out there that already knows that analysts and other purported seers are full of it and it’s called the stock market. It takes thousands of pieces of information and millions of opinions and factors them into prices instantly, for better or worse. That’s the crux of the efficient market hypothesis put forward by Nobel Prize–winning economist Eugene Fama in his PhD dissertation a half century ago. The idea was popularized in the 1970s in a bestseller by Professor Burton Malkiel, A Random Walk Down Wall Street—a book I recommend highly to any active investor. It’s no coincidence that index funds debuted around the time it was published. The type of seer you the reader are most likely to be keeping in fine fettle is a fund manager (and, indirectly, the analysts and brokers they use).

A quarter century earlier, in the Swinging Sixties, it was anything with the suffix “tronic” or the word “scientific.” Hot companies included Vulcatron, Circuitronics, Astron, and the gratuitously snazzy-sounding “Powerton Ultrasonics.” In his classic A Random Walk Down Wall Street, Burton Malkiel tells the story of a company that sold vinyl records door-to-door. Its stock price surged 600 percent when it changed its name to Space Tone.3 The impetus for the trend in names evoking whiz-bang technology was America’s reaction to Sputnik and later President Kennedy’s pledge to put a man on the moon. Fans of classic TV shows might remember an episode of Leave It to Beaver from 1962 (the peak of the “Kennedy bull market”) that delivered a sober warning about the “-tronics.”

While no fund manager in his right mind would do that, or at least admit to it, some very smart people will tell you that it’s entirely plausible. “A blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by experts,” wrote Burton Malkiel, the Princeton University professor and author of the bestseller A Random Walk Down Wall Street. It turns out, based on actual studies, that he was wrong. The monkey just might do better. Robert Arnott, who runs asset management firm Research Affiliates, did a study in which one hundred random portfolios of thirty stocks each were created from the thousand largest U.S. companies, and ninety-six beat the index.


pages: 512 words: 162,977

New Market Wizards: Conversations With America's Top Traders by Jack D. Schwager

backtesting, beat the dealer, Benoit Mandelbrot, Berlin Wall, Black-Scholes formula, book value, butterfly effect, buy and hold, commodity trading advisor, computerized trading, currency risk, Edward Thorp, Elliott wave, fixed income, full employment, implied volatility, interest rate swap, Louis Bachelier, margin call, market clearing, market fundamentalism, Market Wizards by Jack D. Schwager, money market fund, paper trading, pattern recognition, placebo effect, prediction markets, proprietary trading, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, risk tolerance, risk/return, Saturday Night Live, Sharpe ratio, the map is not the territory, transaction costs, uptick rule, War on Poverty

using both “Toward” and “Away From” motivation; having a goal of full capability plus, with anything less being unacceptable; breaking down potentially overwhelming goals into chunks, with satisfaction garnered from the completion of each individual step; keeping full concentration on the present moment—that is, the single task at hand rather than the long-term goal; being personally involved in achieving goals (as opposed to depending on others); and making self-to-self comparisons to measure progress. 41. PRICES ARE NONRANDOM = THE MARKETS CAN BE BEAT In reference to academicians who believe market prices are random, Trout says, “That’s probably why they’re professors and why Fin making money doing what I’m doing.” The debate over whether prices are random is not yet over. However, my experience with the interviews conducted for this book and its predecessor leaves me with little doubt that the random walk theory is wrong. It is not the magnitude of the winnings registered by the Market Wizards but the consistency of these winnings in some cases that underpin my belief.

I’m not saying this will happen soon, but probably within the next few generations. A good part of the academic community insists that the random nature of price behavior means that it’s impossible to develop trading systems that can beat the market over the long run. What’s your response? 124 / The New Market Wizard The evidence against the random walk theory of market action is staggering. Hundreds of traders and managers have profited from price-based mechanical systems. What about the argument that if you have enough people trading, some of them are going to do well, even if just because of chance?

Among the observations you have made about markets and trading over the years, do any stand out as being particularly surprising or counterintuitive? Some years back, a company ran an annual charting contest. The contestants had to predict the settlement prices of several futures for a certain date by a given deadline. Someone in our office [Dale Dellutri] decided, I believe prankishly, to use the random walk model. In other words, he simply used the settlement prices of the deadline as his prediction. He fell just short of becoming a prizewinner with this procedure. His name was among the first five of a list of fifty or so close runners-up. This contest had hundreds of entrants.


pages: 416 words: 39,022

Asset and Risk Management: Risk Oriented Finance by Louis Esch, Robert Kieffer, Thierry Lopez

asset allocation, Brownian motion, business continuity plan, business process, capital asset pricing model, computer age, corporate governance, discrete time, diversified portfolio, fixed income, implied volatility, index fund, interest rate derivative, iterative process, P = NP, p-value, random walk, risk free rate, risk/return, shareholder value, statistical model, stochastic process, transaction costs, value at risk, Wiener process, yield curve, zero-coupon bond

INTERNET SITES http://www.aptltd.com http://www.bis.org/index.htm http://www.cga-canada.org/fr/magazine/nov-dec02/Cyberguide f.htm http://www.fasb.org http://www.iasc.org.uk/cmt/0001.asp http://www.ifac.org http://www.prim.lu Index absolute global risk 285 absolute risk aversion coefficient 88 accounting standards 9–10 accrued interest 118–19 actuarial output rate on issue 116–17 actuarial return rate at given moment 117 adjustment tests 361 Aitken extrapolation 376 Akaike’s information criterion (AIC) 319 allocation independent allocation 288 joint allocation 289 of performance level 289–90 of systematic risk 288–9 American option 149 American pull 158–9 arbitrage 31 arbitrage models 138–9 with state variable 139–42 arbitrage pricing theory (APT) 97–8, 99 absolute global risk 285 analysis of style 291–2 beta 290, 291 factor-sensitivity profile 285 model 256, 285–94 relative global risk/tracking error 285–7 ARCH 320 ARCH-GARCH models 373 arithmetical mean 36–7 ARMA models 318–20 asset allocation 104, 274 asset liability management replicating portfolios 311–21 repricing schedules 301–11 simulations 300–1 structural risk analysis in 295–9 VaR in 301 autocorrelation test 46 autoregressive integrated moving average 320 autoregressive moving average (ARMA) 318 average deviation 41 bank offered rate (BOR) 305 basis point 127 Basle Committee for Banking Controls 4 Basle Committee on Banking Supervision 3–9 Basle II 5–9 Bayesian information criterion (BIC) 319 bear money spread 177 benchmark abacus 287–8 Bernouilli scheme 350 Best Linear Unbiased Estimators (BLUE) 363 beta APT 290, 291 portfolio 92 bijection 335 binomial distribution 350–1 binomial formula (Newton’s) 111, 351 binomial law of probability 165 binomial trees 110, 174 binomial trellis for underlying equity 162 bisection method 380 Black and Scholes model 33, 155, 174, 226, 228, 239 for call option 169 dividends and 173 for options on equities 168–73 sensitivity parameters 172–3 BLUE (Best Linear Unbiased Estimators) 363 bond portfolio management strategies 135–8 active strategy 137–8 duration and convexity of portfolio 135–6 immunizing a portfolio 136–7 positive strategy: immunisation 135–7 bonds average instant return on 140 390 Index bonds (continued ) definition 115–16 financial risk and 120–9 price 115 price approximation 126 return on 116–19 sources of risk 119–21 valuing 119 bootstrap method 233 Brennan and Schwarz model 139 building approach 316 bull money spread 177 business continuity plan (BCP) 14 insurance and 15–16 operational risk and 16 origin, definition and objective 14 butterfly money spread 177 calendar spread 177 call-associated bonds 120 call option 149, 151, 152 intrinsic value 153 premium breakdown 154 call–put parity relation 166 for European options 157–8 canonical analysis 369 canonical correlation analysis 307–9, 369–70 capital asset pricing model (CAPM or MEDAF) 93–8 equation 95–7, 100, 107, 181 cash 18 catastrophe scenarios 20, 32, 184, 227 Cauchy’s law 367 central limit theorem (CLT) 41, 183, 223, 348–9 Charisma 224 Chase Manhattan 224, 228 Choleski decomposition method 239 Choleski factorisation 220, 222, 336–7 chooser option 176 chord method 377–8 classic chord method 378 clean price 118 collateral management 18–19 compliance 24 compliance tests 361 compound Poisson process 355 conditional normality 203 confidence coefficient 360 confidence interval 360–1 continuous models 30, 108–9, 111–13, 131–2, 134 continuous random variables 341–2 contract-by-contract 314–16 convergence 375–6 convertible bonds 116 convexity 33, 149, 181 of a bond 127–9 corner portfolio 64 correlation 41–2, 346–7 counterparty 23 coupon (nominal) rate 116 coupons 115 covariance 41–2, 346–7 cover law of probability 164 Cox, Ingersoll and Ross model 139, 145–7, 174 Cox, Ross and Rubinstein binomial model 162–8 dividends and 168 one period 163–4 T periods 165–6 two periods 164–5 credit risk 12, 259 critical line algorithm 68–9 debentures 18 decision channels 104, 105 default risk 120 deficit constraint 90 degenerate random variable 341 delta 156, 181, 183 delta hedging 157, 172 derivatives 325–7 calculations 325–6 definition 325 extrema 326–7 geometric interpretations 325 determinist models 108–9 generalisation 109 stochastic model and 134–5 deterministic structure of interest rates 129–35 development models 30 diagonal model 70 direct costs 26 dirty price 118 discrete models 30, 108, 109–11. 130, 132–4 discrete random variables 340–1 dispersion index 26 distortion models 138 dividend discount model 104, 107–8 duration 33, 122–7, 149 and characteristics of a bond 124 definition 121 extension of concept of 148 interpretations 121–3 of equity funds 299 of specific bonds 123–4 Index dynamic interest-rate structure 132–4 dynamic models 30 dynamic spread 303–4 efficiency, concept of 45 efficient frontier 27, 54, 59, 60 for model with risk-free security 78–9 for reformulated problem 62 for restricted Markowitz model 68 for Sharpe’s simple index model 73 unrestricted and restricted 68 efficient portfolio 53, 54 EGARCH models 320, 373 elasticity, concept of 123 Elton, Gruber and Padberg method 79–85, 265, 269–74 adapting to VaR 270–1 cf VaR 271–4 maximising risk premium 269–70 equities definition 35 market efficiency 44–8 market return 39–40 portfolio risk 42–3 return on 35–8 return on a portfolio 38–9 security risk within a portfolio 43–4 equity capital adequacy ratio 4 equity dynamic models 108–13 equity portfolio diversification 51–93 model with risk-free security 75–9 portfolio size and 55–6 principles 515 equity portfolio management strategies 103–8 equity portfolio theory 183 equity valuation models 48–51 equivalence, principle of 117 ergodic estimator 40, 42 estimated variance–covariance matrix method (VC) 201, 202–16, 275, 276, 278 breakdown of financial assets 203–5 calculating VaR 209–16 hypotheses and limitations 235–7 installation and use 239–41 mapping cashflows with standard maturity dates 205–9 valuation models 237–9 estimator for mean of the population 360 European call 158–9 European option 149 event-based risks 32, 184 ex ante rate 117 ex ante tracking error 285, 287 ex post return rate 121 exchange options 174–5 exchange positions 204 391 exchange risk 12 exercise price of option 149 expected return 40 expected return risk 41, 43 expected value 26 exponential smoothing 318 extrema 326–7, 329–31 extreme value theory 230–4, 365–7 asymptotic results 365–7 attraction domains 366–7 calculation of VaR 233–4 exact result 365 extreme value theorem 230–1 generalisation 367 parameter estimation by regression 231–2 parameter estimation using the semi-parametric method 233, 234 factor-8 mimicking portfolio 290 factor-mimicking portfolios 290 factorial analysis 98 fair value 10 fat tail distribution 231 festoon effect 118, 119 final prediction error (FPE) 319 Financial Accounting Standards Board (FASB) 9 financial asset evaluation line 107 first derivative 325 Fisher’s skewness coefficient 345–6 fixed-income securities 204 fixed-rate bonds 115 fixed rates 301 floating-rate contracts 301 floating-rate integration method 311 FRAs 276 Fréchet’s law 366, 367 frequency 253 fundamental analysis 45 gamma 156, 173, 181, 183 gap 296–7, 298 GARCH models 203, 320 Garman–Kohlhagen formula 175 Gauss-Seidel method, nonlinear 381 generalised error distribution 353 generalised Pareto distribution 231 geometric Brownian motion 112, 174, 218, 237, 356 geometric mean 36 geometric series 123, 210, 328–9 global portfolio optimisation via VaR 274–83 generalisation of asset model 275–7 construction of optimal global portfolio 277–8 method 278–83 392 Index good practices 6 Gordon – Shapiro formula 48–50, 107, 149 government bonds 18 Greeks 155–7, 172, 181 gross performance level and risk withdrawal 290–1 Gumbel’s law 366, 367 models for bonds 149 static structure of 130–2 internal audit vs. risk management 22–3 internal notation (IN) 4 intrinsic value of option 153 Itô formula (Ito lemma) 140, 169, 357 Itô process 112, 356 Heath, Jarrow and Morton model 138, 302 hedging formula 172 Hessian matrix 330 high leverage effect 257 Hill’s estimator 233 historical simulation 201, 224–34, 265 basic methodology 224–30 calculations 239 data 238–9 extreme value theory 230–4 hypotheses and limitations 235–7 installation and use 239–41 isolated asset case 224–5 portfolio case 225–6 risk factor case 224 synthesis 226–30 valuation models 237–8 historical volatility 155 histories 199 Ho and Lee model 138 homogeneity tests 361 Hull and White model 302, 303 hypothesis test 361–2 Jensen index 102–3 Johnson distributions 215 joint allocation 289 joint distribution function 342 IAS standards 10 IASB (International Accounting Standards Board) 9 IFAC (International Federation of Accountants) 9 immunisation of bonds 124–5 implied volatility 155 in the money 153, 154 independence tests 361 independent allocation 288 independent random variables 342–3 index funds 103 indifference curves 89 indifference, relation of 86 indirect costs 26 inequalities on calls and puts 159–60 inferential statistics 359–62 estimation 360–1 sampling 359–60 sampling distribution 359–60 instant term interest rate 131 integrated risk management 22, 24–5 interest rate curves 129 kappa see vega kurtosis coefficient 182, 189, 345–6 Lagrangian function 56, 57, 61, 63, 267, 331 for risk-free security model 76 for Sharpe’s simple index model 71 Lagrangian multipliers 57, 331 law of large numbers 223, 224, 344 law of probability 339 least square method 363 legal risk 11, 21, 23–4 Lego approach 316 leptokurtic distribution 41, 182, 183, 189, 218, 345 linear equation system 335–6 linear model 32, 33, 184 linearity condition 202, 203 Lipschitz’s condition 375–6 liquidity bed 316 liquidity crisis 17 liquidity preference 316 liquidity risk 12, 16, 18, 296–7 logarithmic return 37 logistic regression 309–10, 371 log-normal distribution 349–50 log-normal law with parameter 349 long (short) straddle 176 loss distribution approach 13 lottery bonds 116 MacLaurin development 275, 276 mapping cashflows 205–9 according to RiskMetricsT M 206–7 alternative 207–8 elementary 205–6 marginal utility 87 market efficiency 44–8 market model 91–3 market price of the risk 141 market risk 12 market straight line 94 Index market timing 104–7 Markowitz’s portfolio theory 30, 41, 43, 56–69, 93, 94, 182 first formulation 56–60 reformulating the problem 60–9 mathematic valuation models 199 matrix algebra 239 calculus 332–7 diagonal 333 n-order 332 operations 333–4 symmetrical 332–3, 334–5 maturity price of bond 115 maximum outflow 17–18 mean 343–4 mean variance 27, 265 for equities 149 measurement theory 344 media risk 12 Merton model 139, 141–2 minimum equity capital requirements 4 modern portfolio theory (MPT) 265 modified duration 121 money spread 177 monoperiodic models 30 Monte Carlo simulation 201, 216–23, 265, 303 calculations 239 data 238–9 estimation method 218–23 hypotheses and limitations 235–7 installation and use 239–41 probability theory and 216–18 synthesis 221–3 valuation models 237–8 multi-index models 221, 266 multi-normal distribution 349 multivariate random variables 342–3 mutual support 147–9 Nelson and Schaefer model 139 net present value (NPV) 298–9, 302–3 neutral risk 164, 174 New Agreement 4, 5 Newson–Raphson nonlinear iterative method 309, 379–80, 381 Newton’s binomial formula 111, 351 nominal rate of a bond 115, 116 nominal value of a bond 115 non-correlation 347 nonlinear equation systems 380–1 first-order methods 377–9 iterative methods 375–7 n-dimensional iteration 381 principal methods 381 393 solving 375–81 nonlinear Gauss-Seidel method 381 nonlinear models independent of time 33 nonlinear regression 234 non-quantifiable risks 12–13 normal distribution 41, 183, 188–90, 237, 254, 347–8 normal law 188 normal probability law 183 normality 202, 203, 252–4 observed distribution 254 operational risk 12–14 business continuity plan (BCP) and 16 definition 6 management 12–13 philosophy of 5–9 triptych 14 options complex 175–7 definition 149 on bonds 174 sensitivity parameters 155–7 simple 175 strategies on 175–7 uses 150–2 value of 153–60 order of convergence 376 Ornstein – Uhlenbeck process 142–5, 356 OTC derivatives market 18 out of the money 153, 154 outliers 241 Pareto distribution 189, 367 Parsen CAT 319 partial derivatives 329–31 payment and settlement systems 18 Pearson distribution system 183 perfect market 31, 44 performance evaluation 99–108 perpetual bond 123–4 Picard’s iteration 268, 271, 274, 280, 375, 376, 381 pip 247 pockets of inefficiency 47 Poisson distribution 350 Poisson process 354–5 Poisson’s law 351 portfolio beta 92 portfolio risk management investment strategy 258 method 257–64 risk framework 258–64 power of the test 362 precautionary surveillance 3, 4–5 preference, relation of 86 394 Index premium 149 price at issue 115 price-earning ratio 50–1 price of a bond 127 price variation risk 12 probability theory 216–18 process risk 24 product risk 23 pseudo-random numbers 217 put option 149, 152 quadratic form 334–7 qualitative approach 13 quantifiable risks 12, 13 quantile 188, 339–40 quantitative approach 13 Ramaswamy and Sundaresan model 139 random aspect of financial assets 30 random numbers 217 random variables 339–47 random walk 45, 111, 203, 355 statistical tests for 46 range forwards 177 rate fluctuation risk 120 rate mismatches 297–8 rate risk 12, 303–11 redemption price of bond 115 regression line 363 regressions 318, 362–4 multiple 363–4 nonlinear 364 simple 362–3 regular falsi method 378–9 relative fund risk 287–8 relative global risk 285–7 relative risks 43 replicating portfolios 302, 303, 311–21 with optimal value method 316–21 repos market 18 repricing schedules 301–11 residual risk 285 restricted Markowitz model 63–5 rho 157, 173, 183 Richard model 139 risk, attitude towards 87–9 risk aversion 87, 88 risk factors 31, 184 risk-free security 75–9 risk, generalising concept 184 risk indicators 8 risk management cost of 25–6 environment 7 function, purpose of 11 methodology 19–21 vs back office 22 risk mapping 8 risk measurement 8, 41 risk-neutral probability 162, 164 risk neutrality 87 risk of one equity 41 risk of realisation 120 risk of reinvestment 120 risk of reputation 21 risk per share 181–4 risk premium 88 risk return 26–7 risk transfer 14 risk typology 12–19 Risk$TM 224, 228 RiskMetricsTM 202, 203, 206–7, 235, 236, 238, 239–40 scenarios and stress testing 20 Schaefer and Schwartz model 139 Schwarz criterion 319 scope of competence 21 scorecards method 7, 13 security 63–5 security market line 107 self-assessment 7 semi-form of efficiency hypothesis 46 semi-parametric method 233 semi-variance 41 sensitivity coefficient 121 separation theorem 94–5, 106 series 328 Sharpe’s multi-index model 74–5 Sharpe’s simple index method 69–75, 100–1, 132, 191, 213, 265–9 adapting critical line algorithm to VaR 267–8 cf VaR 269 for equities 221 problem of minimisation 266–7 VaR in 266–9 short sale 59 short-term interest rate 130 sign test 46 simulation tests for technical analysis methods 46 simulations 300–1 skewed distribution 182 skewness coefficient 182, 345–6 specific risk 91, 285 speculation bubbles 47 spot 247 Index spot price 150 spot rate 129, 130 spreads 176–7 square root process 145 St Petersburg paradox 85 standard Brownian motion 33, 355 standard deviation 41, 344–5 standard maturity dates 205–9 standard normal law 348 static models 30 static spread 303–4 stationarity condition 202, 203, 236 stationary point 327, 330 stationary random model 33 stochastic bond dynamic models 138–48 stochastic differential 356–7 stochastic duration 121, 147–8 random evolution of rates 147 stochastic integral 356–7 stochastic models 109–13 stochastic process 33, 353–7 particular 354–6 path of 354 stock exchange indexes 39 stock picking 104, 275 stop criteria 376–7 stop loss 258–9 straddles 175, 176 strangles 175, 176 strategic risk 21 stress testing 20, 21, 223 strike 149 strike price 150 strong form of efficiency hypothesis 46–7 Student distribution 189, 235, 351–2 Student’s law 367 Supervisors, role of 8 survival period 17–18 systematic inefficiency 47 systematic risk 44, 91, 285 allocation of 288–9 tail parameter 231 taste for risk 87 Taylor development 33, 125, 214, 216, 275–6 Taylor formula 37, 126, 132, 327–8, 331 technical analysis 45 temporal aspect of financial assets 30 term interest rate 129, 130 theorem of expected utility 86 theoretical reasoning 218 theta 156, 173, 183 three-equity portfolio 54 395 time value of option 153, 154 total risk 43 tracking errors 103, 285–7 transaction risk 23–4 transition bonds 116 trend extrapolations 318 Treynor index 102 two-equity portfolio 51–4 unbiased estimator 360 underlying equity 149 uniform distribution 352 uniform random variable 217 utility function 85–7 utility of return 85 utility theory 85–90, 183 valuation models 30, 31–3, 160–75, 184 value at risk (VaR) 13, 20–1 based on density function 186 based on distribution function 185 bond portfolio case 250–2 breaking down 193–5 calculating 209–16 calculations 244–52 component 195 components of 195 definition 195–6 estimation 199–200 for a portfolio 190–7 for a portfolio of linear values 211–13 for a portfolio of nonlinear values 214–16 for an isolated asset 185–90 for equities 213–14 heading investment 196–7 incremental 195–7 individual 194 link to Sharp index 197 marginal 194–5 maximum, for portfolio 263–4 normal distribution 188–90 Treasury portfolio case 244–9 typology 200–2 value of basis point (VBP) 19–20, 21, 127, 245–7, 260–3 variable contracts 301 variable interest rates 300–1 variable rate bonds 115 variance 41, 344–5 variance of expected returns approach 183 variance – covariance matrix 336 Vasicek model 139, 142–4, 174 396 Index vega (kappa) 156, 173 volatility of option 154–5 yield curve 129 yield to maturity (YTM) 250 weak form of the efficiency hypothesis 46 Weibull’s law 366, 367 Wiener process 355 zero-coupon bond 115, 123, 129 zero-coupon rates, analysis of correlations on 305–7 Index compiled by Annette Musker

This question is addressed in the following paragraphs, and the analysis is carried out at three levels according to the accessibility of information. The least that can be said is that the conclusions of the searches carried out in order to test efficiency are inconclusive and should not be used as a basis for forming clear and definitive ideas. 9 Fama E. F., Behaviour of Stock Market Prices, Journal of Business, Vol. 38, 1965, pp. 34–105. Fama E. F., Random Walks in Stock Market Prices, Financial Analysis Journal, 1965. Fama E. F., Efficient Capital Markets: A Review of Theory and Empirical Work, Journal of Finance, Vol. 25, 1970. 10 This approach is adopted in this work. 11 Refer for example to Bechu T. and Bertrand E., L’Analyse Technique, Economica, 1998. 46 Asset and Risk Management 3.1.2.2 Weak form The weak form of the efficiency hypothesis postulates that it is not possible to gain a particular advantage from the range of historical observations; the rates therefore purely and simply include the previous rate values.

Bonds 141 By differentiating the value of the portfolio, we have: dVt = −Pt (s1 )(µt (s1 ) dt − σt (s1 ) dwt ) + X · Pt (s2 )(µt (s2 ) dt − σt (s2 ) dwt ) = [−Pt (s1 )µt (s1 ) + XPt (s2 )µt (s2 )] · dt + [Pt (s1 )σt (s1 ) − XPt (s2 )σt (s2 )] · dwt The arbitrage logic will therefore lead us to  −Pt (s1 )µt (s1 ) + XPt (s2 )µt (s2 )   = rt  −P (s ) + XP (s ) t t 1 2  P (s )σ (s ) − XPt (s2 )σt (s2 )   t 1 t 1 =0 −Pt (s1 ) + XPt (s2 ) In other words: XPt (s2 ) · (µt (s2 ) − rt ) = Pt (s1 ) · (µt (s1 ) − rt ) XPt (s2 ) · σt (s2 ) = Pt (s1 ) · σt (s1 ) We can eliminate X, for example by dividing the two equations member by member, which gives: µt (s1 ) − rt µt (s2 ) − rt = σt (s1 ) σt (s2 ) This shows that the expression λt (rt ) = µt (s) − rt is independent of s; this expression σt (s) is known as the market price of the risk. By replacing µt and σt with their value in the preceding relation, we arrive at Pt + (a + λb)Pr + b2 P − rP = 0 2 rr What we are looking at here is the partial derivatives equation of the second order, which together with the initial condition Ps (s, rt ) = l, defines the price process. This equation must be resolved for each specification of a(t, rt ), b(t, rt ) and λt (rt ). 4.5.1.2 The Merton model31 Because of its historical interest,32 we are showing the simplest model, the Merton model. This model assumes that the instant term rate follows a random walk model: drt = α · dt + σ · dwt with α and σ being constant and the market price of risk being zero (λ = 0).


pages: 403 words: 119,206

Toward Rational Exuberance: The Evolution of the Modern Stock Market by B. Mark Smith

Alan Greenspan, bank run, banking crisis, book value, business climate, business cycle, buy and hold, capital asset pricing model, compound rate of return, computerized trading, Cornelius Vanderbilt, credit crunch, cuban missile crisis, discounted cash flows, diversified portfolio, Donald Trump, equity risk premium, Eugene Fama: efficient market hypothesis, financial independence, financial innovation, fixed income, full employment, Glass-Steagall Act, income inequality, index arbitrage, index fund, joint-stock company, junk bonds, locking in a profit, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market clearing, merger arbitrage, Michael Milken, money market fund, Myron Scholes, Paul Samuelson, price stability, prudent man rule, random walk, Richard Thaler, risk free rate, risk tolerance, Robert Bork, Robert Shiller, Ronald Reagan, scientific management, shareholder value, short selling, stocks for the long run, the market place, transaction costs

Nine months after publishing the article in the Journal of Business, Fama wrote a simplified version for the Financial Analysts Journal entitled “Random Walks in Stock Market Prices.” Comparing the movement of stock prices to the “random walk” of a drunk stumbling from point to point, Fama argued that price movements in an “efficient market” were random, representing adjustments to unpredictable news items that, when made public, would immediately be reflected in the price of stocks. Fama did not invent the term “random walk,” but his work certainly popularized the expression. Fama became something of a celebrity, appearing on television talk shows, and was profiled in Forbes, Fortune, and The Wall Street Journal.

The determination of these fluctuations depends on an infinite number of factors; it is, therefore, impossible to aspire to mathematical predictions of it … the dynamics of the Exchange will never be an exact science.9 Bachelier had another important insight—that stock price fluctuations tend to grow larger as the time horizon lengthens. The formula he developed to describe the phenomenon bears a remarkable resemblance to the formula that describes the random collision of molecules as they move in space. Many years later this process would be described as a random walk, a key concept underlying much of the academic work on the stock market in the second half of the twentieth century. A great deal of Bachelier’s work was revolutionary.

., p. 23. 3 Institutional Investor, October 1970. 4 Ibid. 5 Eugene Fama and G. William Schwert, “Asset Returns and Inflation,” Journal of Financial Economics 5 (2), 1977. 6 Institutional Investor, September 1971. 7 Ibid. 8 Ibid. 9 Ibid. 10 Ibid. 11 Ibid. 12 Institutional Investor, April 1977. 13 Burton Malkiel, A Random Walk Down Wall Street (New York: W. W. Norton, 1999), p. 193. 14 Institutional Investor, April 1977. 15 Peter Bernstein, Capital Ideas, p. 292. 16 Bernstein, Journal of Portfolio Management, Fall 1974. 13. CRUNCH 1 Stephen Fay, Beyond Greed (New York: Viking Press, 1982), p. 235. 2 Barron’s, 15 October 1979. 3 Barron’s, 10 May 1980. 4 Fay, p. 14. 5 Ibid., p. 212. 6 Ibid., p. 218. 7 Institutional Investor, June 1980. 8 Institutional Investor, January 1979. 9 Institutional Investor, June 1980. 10 Marshall Blume, Jeremy Siegel, and Dan Rottenburg, Revolution on Wall Street, p. 92. 11 Sanjoy Basu, Journal of Finance 32 (3), 1977. 12 Robert Shiller, American Economic Review, June 1981. 13 Peter Bernstein, Capital Ideas, p. 211. 14.


Mathematics for Finance: An Introduction to Financial Engineering by Marek Capinski, Tomasz Zastawniak

Black-Scholes formula, Brownian motion, capital asset pricing model, cellular automata, delta neutral, discounted cash flows, discrete time, diversified portfolio, financial engineering, fixed income, interest rate derivative, interest rate swap, locking in a profit, London Interbank Offered Rate, margin call, martingale, quantitative trading / quantitative finance, random walk, risk free rate, short selling, stochastic process, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

Glossary of Symbols A B β c C C CA CE CE Cov delta div div0 D D DA E E∗ f F gamma Φ k K i m fixed income (risk free) security price; money market account bond price beta factor covariance call price; coupon value covariance matrix American call price European call price discounted European call price covariance Greek parameter delta dividend present value of dividends derivative security price; duration discounted derivative security price price of an American type derivative security expectation risk-neutral expectation futures price; payoff of an option; forward rate forward price; future value; face value Greek parameter gamma cumulative binomial distribution logarithmic return return coupon rate compounding frequency; expected logarithmic return 305 306 Mathematics for Finance M m µ N N k ω Ω p p∗ P PA PE PE PA r rdiv re rF rho ρ S S σ t T τ theta u V Var VaR vega w w W x X y z market portfolio expected returns as a row matrix expected return cumulative normal distribution the number of k-element combinations out of N elements scenario probability space branching probability in a binomial tree risk-neutral probability put price; principal American put price European put price discounted European put price present value factor of an annuity interest rate dividend yield effective rate risk-free return Greek parameter rho correlation risky security (stock) price discounted risky security (stock) price standard deviation; risk; volatility current time maturity time; expiry time; exercise time; delivery time time step Greek parameter theta row matrix with all entries 1 portfolio value; forward contract value, futures contract value variance value at risk Greek parameter vega symmetric random walk; weights in a portfolio weights in a portfolio as a row matrix Wiener process, Brownian motion position in a risky security strike price position in a fixed income (risk free) security; yield of a bond position in a derivative security Index admissible – portfolio 5 – strategy 79, 88 American – call option 147 – derivative security – put option 147 amortised loan 30 annuity 29 arbitrage 7 at the money 169 attainable – portfolio 107 – set 107 183 basis – of a forward contract 128 – of a futures contract 140 basis point 218 bear spread 208 beta factor 121 binomial – distribution 57, 180 – tree model 7, 55, 81, 174, 238 Black–Derman–Toy model 260 Black–Scholes – equation 198 – formula 188 bond – at par 42, 249 – callable 255 – face value 39 – fixed-coupon 255 – floating-coupon 255 – maturity date 39 – stripped 230 – unit 39 – with coupons 41 – zero-coupon 39 Brownian motion 69 bull spread 208 butterfly 208 – reversed 209 call option 13, 181 – American 147 – European 147, 188 callable bond 255 cap 258 Capital Asset Pricing Model 118 capital market line 118 caplet 258 CAPM 118 Central Limit Theorem 70 characteristic line 120 compounding – continuous 32 – discrete 25 – equivalent 36 – periodic 25 – preferable 36 conditional expectation 62 contingent claim 18, 85, 148 – American 183 – European 173 continuous compounding 32 continuous time limit 66 correlation coefficient 99 coupon bond 41 coupon rate 249 307 308 covariance matrix 107 Cox–Ingersoll–Ross model 260 Cox–Ross–Rubinstein formula 181 cum-dividend price 292 delta 174, 192, 193, 197 delta hedging 192 delta neutral portfolio 192 delta-gamma hedging 199 delta-gamma neutral portfolio 198 delta-vega hedging 200 delta-vega neutral portfolio 198 derivative security 18, 85, 253 – American 183 – European 173 discount factor 24, 27, 33 discounted stock price 63 discounted value 24, 27 discrete compounding 25 distribution – binomial 57, 180 – log normal 71, 186 – normal 70, 186 diversifiable risk 122 dividend yield 131 divisibility 4, 74, 76, 87 duration 222 dynamic hedging 226 effective rate 36 efficient – frontier 115 – portfolio 115 equivalent compounding 36 European – call option 147, 181, 188 – derivative security 173 – put option 147, 181, 189 ex-coupon price 248 ex-dividend price 292 exercise – price 13, 147 – time 13, 147 expected return 10, 53, 97, 108 expiry time 147 face value 39 fixed interest 255 fixed-coupon bond 255 flat term structure 229 floating interest 255 floating-coupon bond 255 floor 259 floorlet 259 Mathematics for Finance forward – contract 11, 125 – price 11, 125 – rate 233 fundamental theorem of asset pricing 83, 88 future value 22, 25 futures – contract 134 – price 134 gamma 197 Girsanov theorem 187 Greek parameters 197 growth factor 22, 25, 32 Heath–Jarrow–Morton model hedging – delta 192 – delta-gamma 199 – delta-vega 200 – dynamic 226 in the money 169 initial – forward rate 232 – margin 135 – term structure 229 instantaneous forward rate interest – compounded 25, 32 – fixed 255 – floating 255 – simple 22 – variable 255 interest rate 22 interest rate option 254 interest rate swap 255 261 233 LIBID 232 LIBOR 232 line of best fit 120 liquidity 4, 74, 77, 87 log normal distribution 71, 186 logarithmic return 34, 52 long forward position 11, 125 maintenance margin 135 margin call 135 market portfolio 119 market price of risk 212 marking to market 134 Markowitz bullet 113 martingale 63, 83 Index 309 martingale probability 63, 250 maturity date 39 minimum variance – line 109 – portfolio 108 money market 43, 235 no-arbitrage principle 7, 79, 88 normal distribution 70, 186 option – American 183 – at the money 169 – call 13, 147, 181, 188 – European 173, 181 – in the money 169 – interest rate 254 – intrinsic value 169 – out of the money 169 – payoff 173 – put 18, 147, 181, 189 – time value 170 out of the money 169 par, bond trading at 42, 249 payoff 148, 173 periodic compounding 25 perpetuity 24, 30 portfolio 76, 87 – admissible 5 – attainable 107 – delta neutral 192 – delta-gamma neutral 198 – delta-vega neutral 198 – expected return 108 – market 119 – variance 108 – vega neutral 197 positive part 148 predictable strategy 77, 88 preferable compounding 36 present value 24, 27 principal 22 put option 18, 181 – American 147 – European 147, 189 put-call parity 150 – estimates 153 random interest rates random walk 67 rate – coupon 249 – effective 36 237 – forward 233 – – initial 232 – – instantaneous 233 – of interest 22 – of return 1, 49 – spot 229 regression line 120 residual random variable 121 residual variance 122 return 1, 49 – expected 53 – including dividends 50 – logarithmic 34, 52 reversed butterfly 209 rho 197 risk 10, 91 – diversifiable 122 – market price of 212 – systematic 122 – undiversifiable 122 risk premium 119, 123 risk-neutral – expectation 60, 83 – market 60 – probability 60, 83, 250 scenario 47 security market line 123 self-financing strategy 76, 88 short forward position 11, 125 short rate 235 short selling 5, 74, 77, 87 simple interest 22 spot rate 229 Standard and Poor Index 141 state 238 stochastic calculus 71, 185 stochastic differential equation 71 stock index 141 stock price 47 strategy 76, 87 – admissible 79, 88 – predictable 77, 88 – self-financing 76, 88 – value of 76, 87 strike price 13, 147 stripped bond 230 swap 256 swaption 258 systematic risk 122 term structure 229 theta 197 time value of money 21 310 trinomial tree model Mathematics for Finance 64 underlying 85, 147 undiversifiable risk 122 unit bond 39 value at risk 202 value of a portfolio 2 value of a strategy 76, 87 VaR 202 variable interest 255 Vasiček model 260 vega 197 vega neutral portfolio volatility 71 weights in a portfolio Wiener process 69 yield 216 yield to maturity 229 zero-coupon bond 39 197 94

Next, S(nτ + τ ) 1 ≈ 1 + mτ + σξ(n + 1) + σ 2 τ S(nτ ) 2 1 2 = 1 + m + σ τ + σξ(n + 1), 2 and so S(nτ + τ ) − S(nτ ) ≈ 1 m + σ 2 S(nτ )τ + σS(nτ )ξ(n + 1). 2 Since ξ(n + 1) = w(nτ + τ ) − w(nτ ), we obtain an approximate equation describing the dynamics of stock prices: S(t + τ ) − S(t) ≈ 1 m + σ 2 S(t)τ + σS(t)(w(t + τ ) − w(t)), 2 (3.8) where t = nτ . The solution S(t) of this approximate equation is given by the same formula as in Proposition 3.7. For any N = 1, 2, . . . we consider a binomial tree model with time step of length τ = N1 . Let SN (t) be the corresponding stock prices and let wN (t) be the corresponding symmetric random walk with increments ξN (t) = wN (t) − n is the time after n steps. wN (t − N1 ), where t = N Exercise 3.25 Compute the expectation and variance of wN (t), where t = n N. 3.

Exercise 3.24 Write S(1) and S(2) in terms of m, σ, τ , ξ(1) and ξ(2). Next, we introduce an important sequence of random variables w(n), called a symmetric random walk, such that w(n) = ξ(1) + ξ(2) + · · · + ξ(n), and w(0) = 0. Clearly, ξ(n) = w(n) − w(n − 1). Because of the last equality, the ξ(n) are referred to as the increments of w(n). From now on we shall often write S(t) and w(t) instead of S(n) and w(n) for t = τ n, where n = 1, 2, . . . . Proposition 3.7 The stock price at time t = τ n is given by S(t) = S(0) exp(mt + σw(t)). Proof By (3.2) S(t) = S(nτ ) = S(nτ − τ )ek(n) = S(nτ − 2τ )ek(n−1)+k(n) = · · · = S(0)ek(1)+···+k(n) = S(0)emnτ +σ(ξ(1)+···+ξ(n)) = S(0)emt+σw(t) , as required. 68 Mathematics for Finance In order to pass to the continuous-time limit we use the approximation 1 ex ≈ 1 + x + x2 , 2 accurate for small values of x, to obtain S(nτ + τ ) 1 = ek(n+1) ≈ 1 + k(n + 1) + k(n + 1)2 .


High-Frequency Trading by David Easley, Marcos López de Prado, Maureen O'Hara

algorithmic trading, asset allocation, backtesting, Bear Stearns, Brownian motion, capital asset pricing model, computer vision, continuous double auction, dark matter, discrete time, finite state, fixed income, Flash crash, High speed trading, index arbitrage, information asymmetry, interest rate swap, Large Hadron Collider, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, National best bid and offer, natural language processing, offshore financial centre, pattern recognition, power law, price discovery process, price discrimination, price stability, proprietary trading, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

To do this we must define and measure the efficient price and any deviations from it at each moment in time. We take the standard approach of assuming the efficient price is unpredictable, ie, it follows a random walk. Minus trading frictions, the efficient price at the daily or intra-day frequency can be characterised as a martingale process. Let mj be this latent price (9.7) mj = mj−1 + wt Sometimes the quote midpoint is assumed to represent this latent price. However, quote midpoints are not generally martingales with respect to all available order flow, in which case Hasbrouck (1995, p. 1179) proposes to view the random-walk component of a Stock and Watson (1988) decomposition as the “implicit efficient price”.

Mandelbrot and Taylor open their paper with the following assertion: Price changes over a fixed number of transactions may have a Gaussian distribution. Price changes over a fixed time period may follow a stable Paretian distribution, whose variance is infinite. 5 i i i i i i “Easley” — 2013/10/8 — 11:31 — page 6 — #26 i i HIGH-FREQUENCY TRADING Since the number of transactions in any time period is random, the above statements are not necessarily in disagreement.… Basically, our point is this: the Gaussian random walk as applied to transactions is compatible with a symmetric stable Paretian random walk as applied to fixed time intervals.

Economists and finance professionals often talk about the market’s auctioning process as a given, but it is microstructure theorists who wade into the minutiae of how prices and volumes are actually formed. Because the devil is in the detail, understanding how exactly the order flow is handled, and thus how trades and prices are formed, allows potential profits to be made for those who can manipulate these market dynamics (Figure 1.1). Over short intervals of time, prices are not the random walks so beloved by the efficient market hypothesis, but can instead be predictable artefacts of the market microstructure. Thus, the paradox: billions are invested in HFT research and infrastructure, topics that LF traders do not even recognise as an issue.


pages: 1,082 words: 87,792

Python for Algorithmic Trading: From Idea to Cloud Deployment by Yves Hilpisch

algorithmic trading, Amazon Web Services, automated trading system, backtesting, barriers to entry, bitcoin, Brownian motion, cloud computing, coronavirus, cryptocurrency, data science, deep learning, Edward Thorp, fiat currency, global macro, Gordon Gekko, Guido van Rossum, implied volatility, information retrieval, margin call, market microstructure, Myron Scholes, natural language processing, paper trading, passive investing, popular electronics, prediction markets, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Rubik’s Cube, seminal paper, Sharpe ratio, short selling, sorting algorithm, systematic trading, transaction costs, value at risk

Just the data object needs to be replaced: In [24]: lags = 5 In [25]: cols = [] for lag in range(1, lags + 1): col = f'lag_{lag}' data[col] = data['price'].shift(lag) cols.append(col) data.dropna(inplace=True) In [26]: reg = np.linalg.lstsq(data[cols], data['price'], rcond=None)[0] In [27]: reg Out[27]: array([ 0.98635864, 0.02292172, -0.04769849, 0.05037365, -0.01208135]) Takes the price column and shifts it by lag. The optimal regression parameters illustrate what is typically called the random walk hypothesis. This hypothesis states that stock prices or exchange rates, for example, follow a random walk with the consequence that the best predictor for tomorrow’s price is today’s price. The optimal parameters seem to support such a hypothesis since today’s price almost completely explains the predicted price level for tomorrow.

They write: Rather than focus on the relative returns of securities in the cross-section, time series momentum focuses purely on a security’s own past return….Our finding of time series momentum in virtually every instrument we examine seems to challenge the “random walk” hypothesis, which in its most basic form implies that knowing whether a price went up or down in the past should not be informative about whether it will go up or down in the future. Getting into the Basics Consider end-of-day closing prices for the gold price in USD (XAU=): In [74]: data = pd.DataFrame(raw['XAU=']) In [75]: data.rename(columns={'XAU=': 'price'}, inplace=True) In [76]: data['returns'] = np.log(data['price'] / data['price'].shift(1)) The most simple time series momentum strategy is to buy the stock if the last return was positive and to sell it if it was negative.

Figure 5-4 shows the results for a three months time window. This plot illustrates that the prediction for tomorrow’s rate is roughly today’s rate. The prediction is more or less a shift of the original rate to the right by one trading day: In [30]: data[['price', 'prediction']].loc['2019-10-1':].plot( figsize=(10, 6)); Applying linear OLS regression to predict rates for EUR/USD based on historical rates provides support for the random walk hypothesis. The results of the numerical example show that today’s rate is the best predictor for tomorrow’s rate in a least-squares sense. Figure 5-4. EUR/USD exchange rate and predicted values based on linear regression (five lags, three months only) Predicting Future Returns So far, the analysis is based on absolute rate levels.


pages: 345 words: 87,745

The Power of Passive Investing: More Wealth With Less Work by Richard A. Ferri

Alan Greenspan, asset allocation, backtesting, Benchmark Capital, Bernie Madoff, book value, buy and hold, capital asset pricing model, cognitive dissonance, correlation coefficient, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, John Bogle, junk bonds, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Performance of Mutual Funds in the Period, Ponzi scheme, prediction markets, proprietary trading, prudent man rule, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, Tax Reform Act of 1986, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

Fama’s meticulously researched Ph.D. thesis was published in 1965 and titled “The Behavior of Stock Market Prices.” The purpose of the paper was to test the theory that stock market prices are random and follow what’s commonly referred to today as a random walk.8 Fama’s work led to the formation of the efficient market hypothesis (EMH), which is a theory of efficient security pricing in free and open markets. The theory states that all known and available information is already reflected in current securities prices. Thus, the price agreed to by a willing buyer and seller in the open market is the best estimate, good or bad, of the investment value of a security.

See Early performance studies Permanent loss Persistence of performance academic studies: bond funds Carhart’s work Fama and French “Hot Hands” study Personal trust(s): fiduciary investing and taxes and Pioneering Portfolio Management (Swensen) Plain vanilla index Policy changes Ponzi scheme Poor accounting Portfolio choices: bottom line and changing the model efficient portfolios fund selection strategies modeling the active bet modifications to model portfolios of active funds quantifying of random portfolio results real-world test relative performance model short-term/long-term Portfolio management: annual evaluation debate on facts about objective of options for Portfolio Selection: Efficient Diversification of Investments (Markowitz) Portfolio theory, modern Positive period weighting Predictors of top performance: fund expenses as qualitative factors as ratings as Pre-inflation return Price-earnings ratio (P/E): growth/value stocks portfolio returns and Price-to-book (P/B) Price-to-cash-flow Price Waterhouse Private trust management: categories of trusts restatement of trusts (third) taxes and UPIA and active management UPIA and passive investing Procrastinating non-index investors: changing/staying the course definition of endowment effect and land of the lost modern portfolio theory and veering off course Prospect theory Prudence, elements of Prudent Investor Act: A Guide to Understanding, The (Simon) Prudent Investor Rule Prudent Man Rule “Purity Hypothesis, The” Qualitative factors, performance and Random walk Random Walk Down Wall Street, A (Malkiel) Rating methods, performance and Real estate Real Estate Investment Trust Act Real Estate Investment Trusts (REITs) Real return Rebalancing portfolio Recovery, market Registered investment advisor (RIA) Reinganum, Marc REITs. See Real Estate Investment Trusts (REITs) Restatement, Third, of Trusts Restatement Commentary Restatement of trusts (third) Restoring American Financial Stability Act of 2010 Retirement income: account types, (see also 401(k) plans; Pension funds; Self-directed retirement plans) focus shifts and Retirement target Return(s): calculation errors estimating real return total return approach Reuters Group PLC Reverse mortgage Revocable trusts RIA.

In 1981, Rex Sinquefield became chairman of Dimensional Fund Advisors (DFA) and McQuown of Wells Fargo joined DFA’s board of directors. DFA develops low-cost index-based investment strategies for advisor clients through mutual funds and for institutional clients through privately managed portfolios. The First Index Fund In his bestselling book, A Random Walk Down Wall Street (W.W. Norton, 1973), Princeton professor Burton G. Malkiel published his own in-depth mutual fund analysis. His conclusions were similar to all the other academics who studied the data. Where was the skill? Most active fund managers don’t beat the market and can’t beat the market.


Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei

backpropagation, bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, disinformation, distributed generation, finite state, industrial research laboratory, information retrieval, information security, iterative process, knowledge worker, linked data, machine readable, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, power law, random walk, recommendation engine, RFID, search costs, semantic web, seminal paper, sentiment analysis, sparse data, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application

Vertices c, d, and e are peripheral vertices. Figure 11.13 A graph, G, where vertices c, d, and e are peripheral. SimRank: Similarity Based on Random Walk and Structural Context For some applications, geodesic distance may be inappropriate in measuring the similarity between vertices in a graph. Here we introduce SimRank, a similarity measure based on random walk and on the structural context of the graph. In mathematics, a random walk is a trajectory that consists of taking successive random steps. Similarity between people in a social network Let's consider measuring the similarity between two vertices in the AllElectronics customer social network of Example 11.18.

The closeness between Ada and Bob can then be measured by the likelihood that other customers simultaneously receive the promotional information that was originally sent to Ada and Bob. This kind of similarity is based on the random walk reachability over the network, and thus is referred to as similarity based on random walk. Let's have a closer look at what is meant by similarity based on structural context, and similarity based on random walk. The intuition behind similarity based on structural context is that two vertices in a graph are similar if they are connected to similar vertices. To measure such similarity, we need to define the notion of individual neighborhood.

The probability of the tour is defined as(11.35) To measure the probability that a vertex w receives a message that originated simultaneously from u and v, we extend the expected distance to the notion of expected meeting distance, that is,(11.36) where is a pair of tours and of the same length. Using a constant C between 0 and 1, we define the expected meeting probability as(11.37) which is a similarity measure based on random walk. Here, the parameter C specifies the probability of continuing the walk at each step of the trajectory. It has been shown that for any two vertices, u and v. That is, SimRank is based on both structural context and random walk. 11.3.3. Graph Clustering Methods Let's consider how to conduct clustering on a graph. We first describe the intuition behind graph clustering. We then discuss two general categories of graph clustering methods.


pages: 263 words: 75,455

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, book value, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, financial engineering, forensic accounting, Henry Singleton, hindsight bias, intangible asset, Jim Simons, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk free rate, risk-adjusted returns, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, stock buybacks, survivorship bias, systematic trading, Teledyne, The Myth of the Rational Market, time value of money, transaction costs

In a collection of essays called The Random Character of Stock Market Prices (1964), Thorp read the English translation of a French dissertation written in 1900 by a student at the University of Paris, Louis Bachelier. Bachelier's dissertation unlocked the secret to valuing warrants: the so-called “random walk” theory. As the name suggests, the “random walk” holds that the movements made by security prices are random. While it might seem paradoxical, the random nature of the moves makes it possible to probabilistically determine the future price of the security. The implications of the random walk theory are profound, and they weren't lost on Thorp.

See Look-ahead bias Price ratios analysis of compound annual growth rates alpha and adjusted performance risk-adjusted performance and absolute measures of risk value premium and spread book-to-market composite formed from all metrics formed from the “best” price ratios top-performing earnings yield EBIT variation, outperformance by enterprise yield (EBITDA and EBIT variations) forward earnings estimate free cash flow yield gross profits yield long-term study methods of studying Princeton-Newport Partners PROBM model Procter & Gamble Profit margins growth maximum stability Pronovost, Peter Puthenpurackal, John Quality and Price, improving compared with Magic Formula finding Price finding Quality Quantitative value checklist Quantitative value strategy examining, results of analysis legend beating the market black box, looking inside man versus machine risk and return robustness Greenblatt's Magic Formula bargain price examination of findings good business Quality and Price, improving compared with Magic Formula finding Price finding Quality simplifying strategy implementation checklist tried-and-true value investing principles Quinn, Kevin The Random Character of Stock Market Prices (Bachelier) Random walk theory Regression analysis Representativeness heuristic “Returns to Trading Strategies Based on Price-to-Earnings and Price-to-Sales Ratios” (Nathan, Sivakumar, & Vijayakumar) Ridgeline Partners Risk-adjusted performance and absolute measures of risk R-squared Ruane, William Scaled net operating assets (SNOA) Scaled total accruals (STA) Schedule 13D Security Analysis (Graham & Dodd) See's Candies Self-attribution bias Sequoia Fund Sharpe, William Sharpe ratio Shiller, Robert Short selling Shumway, Tyler Simons, Jim Singleton, Henry Sloan, Richard Small sample bias “Some Insiders Are Indeed Smart Investors” (Giamouridis, Liodakis, & Moniz) Sortino ratio Stock buybacks, issuance, and announcements Stock market, predicting movements in sustainable alpha quantitative value strategy simplifying tried-and-true value investing principles model, testing benchmarking data errors historical data versus forward data size of portfolio and target stocks small sample bias transaction costs universe, parameters of Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart (Ayres) “The Superinvestors of Graham-and-Doddsville” (Buffett) Survivorship bias Sustainable alpha Taleb, Nassim Teledyne Tetlock, Philip Theory of Investment Value (Williams) Third Avenue Value Fund Thorp, Ed Total enterprise value (TEV) Transaction costs Tsai, Claire Tversky, Amos Value investors'errors Value portfolio Value premium and spread Wellman, Jay What Works on Wall Street (O'Shaughnessy) Whitman, Martin J.

At first blush, each man's strategy seems diametrically opposed to the other, and irretrievably so. They agreed, however, on one very important point: both believed it was possible to outperform the stock market, a belief that flew in the face of the efficient market hypothesis. While it is true that Thorp's strategy was grounded in the random walk, a key component of the efficient market hypothesis, he disagreed with the efficient market believers that it necessarily implied that markets were efficient. Indeed, Thorp went so as far as to call his book Beat the Market. Buffett also thought the efficient market hypothesis was nonsense, writing in his 1988 Shareholder Letter15: This doctrine [the efficient market hypothesis] became highly fashionable—indeed, almost holy scripture in academic circles during the 1970s.


pages: 267 words: 71,941

How to Predict the Unpredictable by William Poundstone

accounting loophole / creative accounting, Albert Einstein, Bernie Madoff, Brownian motion, business cycle, butter production in bangladesh, buy and hold, buy low sell high, call centre, centre right, Claude Shannon: information theory, computer age, crowdsourcing, Daniel Kahneman / Amos Tversky, Edward Thorp, Firefox, fixed income, forensic accounting, high net worth, index card, index fund, Jim Simons, John von Neumann, market bubble, money market fund, pattern recognition, Paul Samuelson, Ponzi scheme, power law, prediction markets, proprietary trading, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Rubik’s Cube, statistical model, Steven Pinker, subprime mortgage crisis, transaction costs

Those who pay the best price at the wrong time can shell out double what someone else did a few days before or afterward. On the next page is a price chart for a Microsoft Xbox 360 (Limited Edition Gears of War 3 bundle) as reported by the price-tracking app Decide.com. These are the lowest prices you could have obtained on the Web at any given moment in 2012. Though erratic, the price is clearly not a random walk. That means it’s predictable to a degree. The average rock-bottom Web price for this Xbox bundle was $379 (about £230). The price hovered close to that average for most of the year. You could have paid as little as $280 (£170) or as much as $600 (£370).

He had done important work on the random walk hypothesis. But in “Market Making and Reversal on the Stock Exchange,” published in the Journal of the American Statistical Association (1966), Niederhoffer and Osborne argued that stock price movements are not random at all. They described a way to out-guess the market. Here is a chart redrawn from one in their paper. It shows a few minutes of trading in the stock of Allied Chemical. At that time, stock prices were quoted in eighths of a dollar (12½ cents). The line in this chart doesn’t look too random, and it’s not. At upper left, the Allied Chemical price zigzags between two price points like a Ping-Pong ball.

See hot hand theory presidential campaign (1964), 89 price-to-earnings (PE) ratio. See stock market price-to-earnings (PE) ratios priming, 54, 56 probabilities, 79, 81, 87–88, 167–172 Proceedings of the American Philosophical Society, 109, 110 Procter & Gamble, 207 product placement, 28 property prices, 199–204 Psotka, Joseph, 103–105 psychic experiments. See ESP (extrasensory perception)/telepathy publicity stunts, 27–29 pupil dilation, 89–90 “Purloined Letter, The” (Poe), 10, 54–55 push-button keypads (for telephones), 39–40 radio, 27–33 random walk theory, 213 randomness, 171–172, 183–184, 250–251 crowd-sourced ratings and, 105 deck of cards and, 164–165 difficulty in achieving, 4 ESP machine and, 39 experiments by Chapanis, 40–44 experiments, history of, 43–45 fake numbers and, 114, 120, 122–123, 125 hot hand theory and, 158–160, 162–169 human perceptions of, 22 Lacan and, 55 lotteries and, 72, 74–75 mentalists and, 45–49 passwords and, 94, 97–98, 101, 102 in playing card games, 86–90 random vs. random-looking, 37 rock/paper/scissors (RPS) and, 55 soccer penalty kicks and, 83–85 stock market and, 213–214 tennis and, 79–81 Zenith experiments and, 35–38 See also outguessing machines; tests ratings.


Risk Management in Trading by Davis Edwards

Abraham Maslow, asset allocation, asset-backed security, backtesting, Bear Stearns, Black-Scholes formula, Brownian motion, business cycle, computerized trading, correlation coefficient, Credit Default Swap, discrete time, diversified portfolio, financial engineering, fixed income, Glass-Steagall Act, global macro, implied volatility, intangible asset, interest rate swap, iterative process, John Meriwether, junk bonds, London Whale, Long Term Capital Management, low interest rates, margin call, Myron Scholes, Nick Leeson, p-value, paper trading, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, statistical model, stochastic process, systematic trading, time value of money, transaction costs, value at risk, Wiener process, zero-coupon bond

. • Kurtosis affects the relative frequency of extreme events relative to events near the center of the distribution. FIGURE 3.8 (+) Leptokurtic (0) Mesokurtic (Normal) (–) Platykurtic Kurtosis RANDOM WALKS (STOCHASTIC PROCESSES) A random walk is a special type of random process that describes the path taken by a series of random steps. In finance, random walk processes are commonly used to model how prices or interest rates might move in the future. In finance, most models are usually limited to a single dimension (like an interest rate rising and falling) rather than a more general case (like a model of a gas particle, which can move in three dimensions).

Develop random inputs Generate a simulation using the random inputs Calculate the result of trading with the strategy Run the simulation multiple times and aggregate the results. Develop Random Inputs Monte Carlo simulations are driven by random inputs. A common choice for an input is a model that adds random movement onto an existing price (this is called stochastic process). For example, gold prices might be modeled assuming they start at the current price (observed in the market today) and then follow a random walk into the future where each day a random adjustment is applied to the price from the previous day. These inputs are typically a professional judgment—and not all model inputs will work out equally well in real life. To minimize some of the judgment, inputs are typically calibrated by analyzing historical data.

This links the value of the calls and puts with one another. 58 RISK MANAGEMENT IN TRADING Short Call −C = −Max(Asset Price - Strike Price, 0) Long Call C = Max(Asset Price - Strike Price, 0) n tio ira xp tE ff a yo Pa ium rem fP ff yo Pa o et n Premium Payoff Value (C) Payoff Value (C) Strike price (X) Strike price (X) Premium Pa y Pa off ne yof f a t of P tE xpi rem iu rat ion m Asset Price (S) Asset Price (S) Pa yo ff Pa yof f ne at Ex p Strike price (X) ira t to fP rem Short Put −P = −Max(Strike Price - Asset Price, 0) ion Payoff Value (P) Payoff Value (P) Long Put P = Max(Strike Price - Asset Price, 0) ium Premium Strike price (X) Premium ium f yof Pa Asset Price (S) rem fP to ne n tio ira xp tE ff a yo Pa Asset Price (S) FIGURE 2.8 Option Payoff Diagrams Put-call parity, like any other type of financial math, requires that calculations have all values brought to the same point in time.


pages: 586 words: 159,901

Wall Street: How It Works And for Whom by Doug Henwood

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, affirmative action, Alan Greenspan, Andrei Shleifer, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, bond market vigilante , book value, borderless world, Bretton Woods, British Empire, business cycle, buy the rumour, sell the news, capital asset pricing model, capital controls, Carl Icahn, central bank independence, computerized trading, corporate governance, corporate raider, correlation coefficient, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, debt deflation, declining real wages, deindustrialization, dematerialisation, disinformation, diversification, diversified portfolio, Donald Trump, equity premium, Eugene Fama: efficient market hypothesis, experimental subject, facts on the ground, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, George Gilder, Glass-Steagall Act, hiring and firing, Hyman Minsky, implied volatility, index arbitrage, index fund, information asymmetry, interest rate swap, Internet Archive, invisible hand, Irwin Jacobs, Isaac Newton, joint-stock company, Joseph Schumpeter, junk bonds, kremlinology, labor-force participation, late capitalism, law of one price, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, London Interbank Offered Rate, long and variable lags, Louis Bachelier, low interest rates, market bubble, Mexican peso crisis / tequila crisis, Michael Milken, microcredit, minimum wage unemployment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, oil shock, Paul Samuelson, payday loans, pension reform, planned obsolescence, plutocrats, Post-Keynesian economics, price mechanism, price stability, prisoner's dilemma, profit maximization, proprietary trading, publication bias, Ralph Nader, random walk, reserve currency, Richard Thaler, risk tolerance, Robert Gordon, Robert Shiller, Savings and loan crisis, selection bias, shareholder value, short selling, Slavoj Žižek, South Sea Bubble, stock buybacks, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Market for Lemons, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, transcontinental railway, women in the workforce, yield curve, zero-coupon bond

," Har\'ard Business School Working Paper 95-072 (February). Faludi, Susan (1990). "The Reckoning: Safeway LBO Yields "Vast Profits but Exacts a Heavy Human Toll," Wall Street Journal, May 16, p. Al. Fama, Eugene F. (1965a). "The Behavior of Stock Prices," Journal of Business 57, pp. 34-105. — (1965b). "Random Walks in Stock Market Prices," Financial Analysts JournaKSeptem- ber-October), pp. 55-59. — (1968). "What 'Random Walk' Really Means," Institutional Investor (April), pp. 38-40. — (1970). "Efficient Capital Markets: A Review of Theory and Empirical ^ork," Journal of Finance 25. pp. 383-423. — (1980). "Banking in the Theory of Finance,"/owmfl/ of Monetary Economics^, pp. 3S>-57

Some technical analysts do fairly rigorous statistical work, tracking changes in trading volume or price momentum, looking for possible clues of imminent trend reversal. Others, little better than haruspices, try to divine patterns in price graphs that supposedly portend dramatic upward or downward moves. Such "chartists" speak enthusiastically of pennants, rising wedges, head and shoulders, saucer bottoms. There is little evidence that chart-reading works at all; the patterns seen are probably little different from the butterflies and genitalia that one sees in a Rorschach test. The economist Burton Malkiel, author of the popular investment text A Random Walk Down Wall Street, had his students construct mythical stock price charts by flipping coins.

Louis Bachelier argued in a 1900 study (that was ignored WALL STREET for 60 years) that over the long term, speculators should consistently earn no extraordinary profits; market prices, in other words, are a "fair game." Another precursor of EM theory was Alfred Cowles, who showed in two studies (Cowles 1933; 1944) that a variety of forecasts by pundits and investment professionals yielded results that were at best no better than the overall market, and often quite worse. In the more modern form, market efficiency partisans hold that the courses of prices constitute something like a "random walk," meaning that they cannot be predicted, since their day-to-day variations — oscillations around some fundamental value, as determined by expected future return — do not follow a pattern significantly different from what chance would specify.


pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, Alan Greenspan, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Black-Scholes formula, book value, Brownian motion, business cycle, buy and hold, buy low sell high, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, currency risk, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, financial engineering, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, global macro, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, junk bonds, late capitalism, law of one price, Long Term Capital Management, low interest rates, managed futures, margin call, market clearing, market design, market friction, Market Wizards by Jack D. Schwager, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, Phillips curve, price discovery process, price stability, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, risk free rate, risk-adjusted returns, risk/return, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, short squeeze, SoftBank, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, tail risk, technology bubble, time dilation, time value of money, total factor productivity, transaction costs, two and twenty, value at risk, Vanguard fund, yield curve, zero-coupon bond

To answer these questions, we first needed to know whether we were facing a liquidity spiral or an unlucky step in the random walk of an efficient market. The efficient market theory says that, going forward, prices should fluctuate randomly, whereas the liquidity spiral theory says that when prices are depressed by forced selling, prices will likely bounce back later. These theories clearly had different implications for how to position our portfolio. On Monday, we became completely convinced that we were facing a liquidity event. All market dynamics pointed clearly in the direction of liquidity and defied the random walk theory (which implies that losing every 10 minutes for several days in a row is next to impossible).

This means that, if the dividend yield is one percentage point larger, then the stock return is also expected to be one percentage point larger. In other words, the dividend yield predicts the stock return because it is part of the stock return (as seen in equation 10.3), but it does not predict the price appreciation. In contrast, the random walk hypothesis b = 0 means that the price appreciation is expected to be low when the dividend yield is high, such that the overall expected equity return is independent of dividend yields. Perhaps the truth lies somewhere between these benchmarks? The data suggest otherwise. I run this regression from 1926 to 2013 with U.S. monthly data, where the monthly excess return is annualized by multiplying by 12 to make it comparable to annual dividends (the result is almost the same with 1-year forward returns, but the t-statistics must be estimated in a more complex way with overlapping data).2 The time series of the dividend yield is plotted in figure 10.1.

Quality Investing: Buying high-quality securities—profitable, stable, growing, and well-managed companies—while shorting low-quality securities. Slow adjustment: Securities with strong quality characteristics should have high prices, but if markets adjust slowly, then these securities will have high returns. Preface My first experience as a hedge fund manager was seeing hundreds of millions of dollars being lost. The losses came with remarkable consistency. Looking at the blinking screen with live P&L (profits and losses), I saw new million-dollar losses every 10 minutes for a couple of days—a clear pattern that defied the random walk theory of efficient markets and, ironically, showed remarkable likeness to my own theories.


pages: 436 words: 76

Culture and Prosperity: The Truth About Markets - Why Some Nations Are Rich but Most Remain Poor by John Kay

Alan Greenspan, Albert Einstein, Asian financial crisis, Barry Marshall: ulcers, behavioural economics, Berlin Wall, Big bang: deregulation of the City of London, Bletchley Park, business cycle, California gold rush, Charles Babbage, complexity theory, computer age, constrained optimization, corporate governance, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, Donald Trump, double entry bookkeeping, double helix, Dr. Strangelove, Dutch auction, Edward Lloyd's coffeehouse, electricity market, equity premium, equity risk premium, Ernest Rutherford, European colonialism, experimental economics, Exxon Valdez, failed state, Fairchild Semiconductor, financial innovation, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, George Akerlof, George Gilder, Goodhart's law, Great Leap Forward, greed is good, Gunnar Myrdal, haute couture, Helicobacter pylori, illegal immigration, income inequality, industrial cluster, information asymmetry, intangible asset, invention of the telephone, invention of the wheel, invisible hand, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Kenneth Arrow, Kevin Kelly, knowledge economy, Larry Ellison, light touch regulation, Long Term Capital Management, loss aversion, Mahatma Gandhi, market bubble, market clearing, market fundamentalism, means of production, Menlo Park, Michael Milken, Mikhail Gorbachev, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, Naomi Klein, Nash equilibrium, new economy, oil shale / tar sands, oil shock, Pareto efficiency, Paul Samuelson, pets.com, Phillips curve, popular electronics, price discrimination, price mechanism, prisoner's dilemma, profit maximization, proprietary trading, purchasing power parity, QWERTY keyboard, Ralph Nader, RAND corporation, random walk, rent-seeking, Right to Buy, risk tolerance, road to serfdom, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, second-price auction, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, Steve Jobs, Stuart Kauffman, telemarketer, The Chicago School, The Market for Lemons, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, total factor productivity, transaction costs, tulip mania, urban decay, Vilfredo Pareto, Washington Consensus, women in the workforce, work culture , yield curve, yield management

They are the reasons why the odds on Seabiscuit are short, the price of General Electric shares is high, mobile phone companies trade at large multiples of their current earnings, and the dollar is strong. There is powerful evidence to support the efficient market hypothesis. The theory predicts that the prices of risks will follow a "random walk." A random walk is a process in which the next step is equally likely to be in any direction. Many physical processes have these characteristics, such as the movement of particles in liquids. This is an area where models derived from statistical mechanics seem to work, and the Black-Scholes model described below is grounded in the analysis of physical systems.

This is an area where models derived from statistical mechanics seem to work, and the Black-Scholes model described below is grounded in the analysis of physical systems. And numerous statistical analyses of prices in markets for securities and commodities have confirmed that they display the characteristics of a random walk. In an early test of the theory, the statistician Maurice Kendall discovered that all but one of the series he studied fitted the random walk prediction. 5 It emerged that the one that did not was not in fact a series of actual market transactions but had been prepared as an average of estimated market prices. This is the kind of satisfYing confirmation of a theory that physicists often experience but is rarely available in the social sciences.

A characteristic of a market in which one side information asymmetry (buyer or seller) is better informed about the properties of the good or service than the other (seller or buyer). intellectual property Rights created by copyright, patent, or trademark legislation and associated regulations. market anomalies Observed deviations from the efficient market hypothesis. mercantilism A theory of international trade (widely held before Adam Smith and still adhered to by some devotees of DIY economics) that draws economies of scale { 364} noise trader Pareto efficiency Pareto improvement path dependency primary market productivity purchasing power parity put option random walk theory secondary market winner's curse Glossary an analogy between the exports and imports of states and the revenues and expenses of firms. A buyer or seller (especially in securities markets) whose behavior does not reflect views about the fundamental value (prospective earnings, etc.) of what he or she is buying.


pages: 612 words: 179,328

Buffett by Roger Lowenstein

Alan Greenspan, asset allocation, Bear Stearns, book value, Bretton Woods, buy and hold, Carl Icahn, cashless society, collective bargaining, computerized trading, corporate raider, credit crunch, cuban missile crisis, Eugene Fama: efficient market hypothesis, index card, index fund, interest rate derivative, invisible hand, Jeffrey Epstein, John Meriwether, junk bonds, Long Term Capital Management, Michael Milken, moral hazard, Paul Samuelson, random walk, risk tolerance, Robert Shiller, Ronald Reagan, Savings and loan crisis, selection bias, Teledyne, The Predators' Ball, traveling salesman, Works Progress Administration, Yogi Berra, young professional, zero-coupon bond

But the theorists replaced the chartist voodoo with a voodoo of their own. They defused the idea that prices foretold the future, but ascribed to those same prices an unerring appraisal of the present. Prices, that is, were never wrong. They incorporated, with as much perfection as humans could manage, all there was to know of a company’s long-term prospects. Studying those prospects was therefore pointless. Thus, the theorists’ attack spread from the chartists to “fundamental analysts,” such as Buffett, who combed through corporate reports looking for undervalued stocks. Quoting Fama: If the random walk theory is valid and if security exchanges are “efficient” markets, then stock prices at any point in time will represent good estimates of intrinsic or fundamental values.

Malkiel, a Princeton economist, popularized this notion in his best-selling A Random Walk down Wall Street: But while I believe in the possibility of superior professional investment performance, I must emphasize that the evidence we have thus far does not support the view that such competence exists.…24 Malkiel saw no evidence of “competence” beyond that of coin-flippers able to disguise their luck as talent. “God Almighty,” Malkiel proclaimed, “does not know the proper price-earnings multiple for a common stock.”25 This fetching comment introduced a straw man. Graham-and-Dodders did not claim to know the proper price for a stock. Theirs was a rough science, at best.

Bernstein, Capital Ideas: The Improbable Origins of Modem Wall Street (New York: Free Press, 1992), 115, 118–19. 4. Paul A. Samuelson, “Proof That Properly Anticipated Prices Fluctuate Randomly,” MIT Industrial Management Review, Spring 1965, pp. 782–85. 5. Paul A. Samuelson, memorandum with testimony on mutual funds, U.S. Senate, Committee on Banking and Currency, August 2, 1967. 6. Thorson, “Omahan in Search.” 7. Paul A. Samuelson. 8. Bernstein, Capital Ideas, 117. 9. Eugene F. Fama, “Random Walks in Stock Market Prices,” Financial Analysts Journal, September-October 1965. 10. Ibid. 11. “The Stock-picking Fallacy,” Economist, August 8, 1992. 12.


pages: 464 words: 117,495

The New Trading for a Living: Psychology, Discipline, Trading Tools and Systems, Risk Control, Trade Management by Alexander Elder

additive manufacturing, Atul Gawande, backtesting, behavioural economics, Benoit Mandelbrot, buy and hold, buy low sell high, Checklist Manifesto, computerized trading, deliberate practice, diversification, Elliott wave, endowment effect, fear index, loss aversion, mandelbrot fractal, margin call, offshore financial centre, paper trading, Ponzi scheme, price stability, psychological pricing, quantitative easing, random walk, Reminiscences of a Stock Operator, risk tolerance, short selling, South Sea Bubble, systematic trading, systems thinking, The Wisdom of Crowds, transaction costs, transfer pricing, traveling salesman, tulip mania, zero-sum game

market data for profit targets in stops in Positive Directional line Positive mathematical expectation Power: of bears vs. bulls: A/D closing prices divergences Force Index MACD-Histogram MACD Line miscellaneous indicators and NH-NL zero line On-Balance Volume open interest volume profits and feeling of of trends Premiums: futures options Press, signals from Prechter, Robert Price(s). See also Closing prices; Opening prices on bar charts as consensus of value crowd behavior reflected in divergences from in Force Index indicators derived from as leader of market crowd long-term cycles in memories of of options in Random Walk theory short-term cycles in slippage support and resistance levels and understanding of volume value vs.

It helps sell courses, trading systems, and newsletters. Mystics, Random Walk academics, and Efficient Market theorists have one trait in common. They are equally divorced from the reality of the markets. ■ 18. Support and Resistance A ball hits the floor and bounces. Toss it up, and it'll drop after hitting the ceiling. Support and resistance are like a floor and a ceiling, with prices sandwiched between them. Understanding support and resistance is essential for understanding price trends. Rating their strength helps you decide whether the trend is likely to punch through or to reverse. Support is a price level where buying is strong enough to interrupt or reverse a downtrend.

People may have knowledge, but the emotional pull of the crowd often leads them to trade irrationally. A good analyst can detect repetitive patterns of crowd behavior on his charts and exploit them. Random Walk theorists claim that market prices change at random. Sure, there is a fair bit of randomness or “noise” in the markets, just as there is randomness in any crowd. Still, an intelligent observer can identify repetitive behavior patterns of a crowd and make sensible bets on their continuation or reversal. People have memories; they remember past prices, and their memories influence their decisions to buy or sell. Memories help create support under the market and resistance above it.


Principles of Corporate Finance by Richard A. Brealey, Stewart C. Myers, Franklin Allen

3Com Palm IPO, accelerated depreciation, accounting loophole / creative accounting, Airbus A320, Alan Greenspan, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, banking crisis, Bear Stearns, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black-Scholes formula, Boeing 747, book value, break the buck, Brownian motion, business cycle, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, capital controls, Carl Icahn, Carmen Reinhart, carried interest, collateralized debt obligation, compound rate of return, computerized trading, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cross-border payments, cross-subsidies, currency risk, discounted cash flows, disintermediation, diversified portfolio, Dutch auction, equity premium, equity risk premium, eurozone crisis, fear index, financial engineering, financial innovation, financial intermediation, fixed income, frictionless, fudge factor, German hyperinflation, implied volatility, index fund, information asymmetry, intangible asset, interest rate swap, inventory management, Iridium satellite, James Webb Space Telescope, junk bonds, Kenneth Rogoff, Larry Ellison, law of one price, linear programming, Livingstone, I presume, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, market bubble, market friction, money market fund, moral hazard, Myron Scholes, new economy, Nick Leeson, Northern Rock, offshore financial centre, PalmPilot, Ponzi scheme, prediction markets, price discrimination, principal–agent problem, profit maximization, purchasing power parity, QR code, quantitative trading / quantitative finance, random walk, Real Time Gross Settlement, risk free rate, risk tolerance, risk/return, Robert Shiller, Scaled Composites, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, Skype, SpaceShipOne, Steve Jobs, subprime mortgage crisis, sunk-cost fallacy, systematic bias, Tax Reform Act of 1986, The Nature of the Firm, the payments system, the rule of 72, time value of money, too big to fail, transaction costs, University of East Anglia, urban renewal, VA Linux, value at risk, Vanguard fund, vertical integration, yield curve, zero-coupon bond, zero-sum game, Zipcar

As investors try to take advantage of the information in past prices, prices adjust immediately until the superior profits from studying past price movements disappear. As a result, all the information in past prices will be reflected in today’s stock price, not tomorrow’s. Patterns in prices will no longer exist and price changes in one period will be independent of changes in the next. In other words, the share price will follow a random walk. In competitive markets today’s stock price must already reflect the information in past prices. But why stop there? If markets are competitive, shouldn’t today’s stock price reflect all the information that is available to investors?

Each series appeared to be “a ‘wandering’ one, almost as if once a week the Demon of Chance drew a random number . . . and added it to the current price to determine the next week’s price.” In other words, the prices of stocks and commodities seemed to follow a random walk. If you are not sure what we mean by “random walk,” you might like to think of the following example: You are given $100 to play a game. At the end of each week a coin is tossed. If it comes up heads, you win 3% of your investment; if it is tails, you lose 2.5%. Therefore, your capital at the end of the first week is either $103.00 or $97.50. At the end of the second week the coin is tossed again. Now the possible outcomes are: This process is a random walk with a positive drift of .25% per week.3 It is a random walk because successive changes in value are independent.

If markets are efficient in the weak sense, then it is impossible to make consistently superior profits by studying past returns. Prices will follow a random walk. The second level of efficiency requires that prices reflect not just past prices but all other public information, for example, from the Internet or the financial press. This is known as semistrong market efficiency. If markets are semistrong efficient, then prices will adjust immediately to public information such as the announcement of the last quarter’s earnings, a new issue of stock, or a proposal to merge two companies. With strong market efficiency, prices reflect all the information that can be acquired by painstaking analysis of the company and the economy.


pages: 120 words: 39,637

The Little Book That Still Beats the Market by Joel Greenblatt

backtesting, book value, General Magic , index fund, intangible asset, random walk, survivorship bias, transaction costs

43 Company A Company B Enterprise value (price + debt) 60 + 0 = $60 10 + 50 = $60 EBIT 10 10 A Random Walk Spoiled For many years, academics have debated whether it is possible to find bargain-priced stocks other than by chance. This notion, sometimes loosely referred to as the random walk or efficient market theory, suggests that for the most part, the stock market is very efficient at taking in all publicly available information and setting stock prices. That is, through the interaction of knowledgeable buyers and sellers, the market does a pretty good job of pricing stocks at “fair” value. This theory, along with the failure of most professional managers to beat the market averages over the long term,44 has understandably led to the movement toward indexing, a cost-effective strategy designed to merely match the market’s return.

Not only that, the magic formula used 69 fewer factors and a lot less math!52 So, here’s the point. The magic formula appears to perform very well. I think and hope it will continue to perform well in the future. I also hope that, just as Mark Twain aptly referred to golf as “a good walk spoiled,” perhaps someday the random walk will finally be considered spoiled as well.53 1 Bank deposits up to $100,000 are guaranteed by an agency of the U.S. government. You must hold your bank deposit or your bond until it matures (possibly 5 or 10 years, depending upon what you buy) to guarantee no loss of your original investment. 2 And yes, the dog was fine. 3 To find out what Jimbo should do, check out the box at the end of the chapter!

Earnings Yield EBIT/ Enterprise Value Earnings yield was measured by calculating the ratio of pretax operating earnings (EBIT) to enterprise value (market value of equity41 + net interest-bearing debt). This ratio was used rather than the more commonly used P/E ratio (price/earnings ratio) or E/P ratio (earnings/price ratio) for several reasons. The basic idea behind the concept of earnings yield is simply to figure out how much a business earns relative to the purchase price of the business. Enterprise value was used instead of merely the price of equity (i.e., total market capitalization, share price multiplied by shares outstanding) because enterprise value takes into account both the price paid for an equity stake in a business as well as the debt financing used by a company to help generate operating earnings.


pages: 437 words: 132,041

Alex's Adventures in Numberland by Alex Bellos

Andrew Wiles, Antoine Gombaud: Chevalier de Méré, beat the dealer, Black Swan, Black-Scholes formula, Claude Shannon: information theory, computer age, Daniel Kahneman / Amos Tversky, digital rights, Edward Thorp, family office, forensic accounting, game design, Georg Cantor, Henri Poincaré, Isaac Newton, Johannes Kepler, lateral thinking, Myron Scholes, pattern recognition, Paul Erdős, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, random walk, Richard Feynman, Rubik’s Cube, SETI@home, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, traveling salesman, two and twenty

Of course, no casino bets are as generous as the flipping of a coin (which has a payback percentage of 100). If the chances of losing are greater than the chances of winning, the map of the random walk drifts downward, rather than tracking the horizontal axis. In other words, bankruptcy looms quicker. Random walks explain why gambling favours the very rich. Not only will it take much longer to go bankrupt, but there is also more chance that your random walk will occasionally meander upward. The secret of winning, for the rich or the poor, however, is knowing when to stop. Inevitably, the mathematics of random walks contains some head-popping paradoxes. In the graphs chapter 9 where Coin Man moves up or down depending on the results of a coin toss, one would expect the graph of his random walk to regularly cross the horizontal axis.

Do this repeatedly to create a path. Venn carried this out with the most unpredictable sequence of numbers he knew: the decimal expansion of pi (excluding 8s and 9s, and starting with 1415). The result, he wrote, was ‘a very fair graphical indication of randomness’. Venn’s sketch is thought to be the first-ever diagram of a ‘random walk’. It is often called the ‘drunkard’s walk’ because it is more colourful to imagine that the original dot is instead a lamp-post and the path traced is the random staggering of a drunk. One of the most obvious questions to ask is how far will the drunk wander from the point of origin before collapsing?

The answer is only 0.34, or about a third. It was weird to realize that in two dimensions the chance of a drunkard walking back into the lamp-post was an absolute certainty, but it seems even weirder to think that a bee buzzing for ever is very unlikely ever to return home. The first-ever random walk appeared in the third edition of John Venn’s Logic of Chance (1888). The rules for the direction of the walk (my addition) follow the digits 0–7 that appear in pi after the decimal point. In Luke Rhinehart’s bestselling novel The Dice Man, the eponymous hero makes life decisions based on the throwing of dice.


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby

Alan Greenspan, Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Bear Stearns, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, book value, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, deal flow, do well by doing good, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, Jim Simons, John Bogle, John Meriwether, junk bonds, Kenneth Rogoff, Kickstarter, Long Term Capital Management, low interest rates, machine translation, margin call, market bubble, market clearing, market fundamentalism, Market Wizards by Jack D. Schwager, Mary Meeker, merger arbitrage, Michael Milken, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, operational security, pattern recognition, Paul Samuelson, pre–internet, proprietary trading, public intellectual, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Savings and loan crisis, Sharpe ratio, short selling, short squeeze, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, tail risk, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs, two and twenty, uptick rule

But from the mid 1960s to the mid 1980s, the prevailing view was that the market is efficient, prices follow a random walk, and hedge funds succeed mainly by being lucky. There is a powerful logic to this account. If it were possible to know with any confidence that the price of a particular bond or equity is likely to move up, smart investors would have pounced and it would have moved up already. Pouncing investors ensure that all relevant information is already in prices, though the next move of a stock will be determined by something unexpected. It follows that professional money managers who try to foresee price moves will generally fail in their mission.

He was required to write a memo to the management explaining his miscalculations.23 The new risk-control system was connected to another rethink that followed the corn debacle: Weymar and his colleagues developed fresh respect for trends in prices. Of course, efficient-market theory holds that such trends do not exist: The random-walk consensus was so dominant that, through the 1970s and much of the 1980s, it was hard to get alternative views published in academic journals.24 But Frank Vannerson had gotten his hands on a trove of historical commodity price data that had been gathered and formatted by Dunn & Hargitt, a firm in Indiana. Before leaving Nabisco, Vannerson had spent a year working on the Dunn & Hargitt data, analyzing daily prices for fifteen commodities; and by the time Commodities Corporation opened its doors in March 1970, he had satisfied himself that price trends really did exist, no matter what academics might assert to the contrary.25 Moreover, Vannerson had devised a computer program that could trade on that finding.

In his 1949 essay in Fortune, Jones had singled out a Russian immigrant named Nicholas Molodovsky as “the most scientific and experimental of technical students,” reporting that with the exception of two episodes in which he had called the market wrong, “his predictions have been nearly perfect.” But in 1965 Molodovsky, by then the editor of the influential Financial Analysts Journal, commissioned a paper from a rising academic star named Eugene Fama, which appeared under the title “Random Walks in Stock Market Prices.” Fama compared chart following to astrology. By popularizing Fama’s random-walk theory, Molodovsky was burning the ground under Jones’s feet; the premise of Jones’s fund was under attack from one of its progenitors. The blow must have felt especially heavy since Jones and Molodovsky were close; Molodovsky introduced Jones to Richard Radcliffe, whom Jones hired subsequently, and Radcliffe recalls Molodovsky as an intellectual influence on the Jones fund during his period there between 1954 and 1965.


One Up on Wall Street by Peter Lynch

air freight, Apple's 1984 Super Bowl advert, Boeing 747, book value, buy and hold, Carl Icahn, corporate raider, cuban missile crisis, Donald Trump, fixed income, index fund, Irwin Jacobs, Isaac Newton, junk bonds, large denomination, money market fund, prediction markets, random walk, shareholder value, Silicon Valley, Teledyne, vertical integration, Y2K, Yom Kippur War, zero-sum game

I also found it difficult to integrate the efficient-market hypothesis (that everything in the stock market is “known” and prices are always “rational”) with the random-walk hypothesis (that the ups and downs of the market are irrational and entirely unpredictable). Already I’d seen enough odd fluctuations to doubt the rational part, and the success of the great Fidelity fund managers was hardly unpredictable. It also was obvious that Wharton professors who believed in quantum analysis and random walk weren’t doing nearly as well as my new colleagues at Fidelity, so between theory and practice, I cast my lot with the practitioners.

I was thrilled to be hired at Fidelity, and also to be installed in Gerry Tsai’s old office, after Tsai had departed for the Manhattan Fund in New York. Of course the Dow Jones industrials, at 925 when I reported for work the first week of May, 1966, had fallen below 800 by the time I headed off to graduate school in September, just as the Lynch Law would have predicted. RANDOM WALK AND MAINE SUGAR Summer interns such as me, with no experience in corporate finance or accounting, were put to work researching companies and writing reports, the same as the regular analysts. The whole intimidating business was suddenly demystified—even liberal arts majors could analyze a stock.

., 277 Noble, George, 212 Nucor, 90, 110 NutraSweet, 211 Nynex, 135 Odyssey Partners, 279 oil services industry, 151, 264 One Potato, Two, 158 OPEC, 277 options, 270–73, 280 cost of, 271 expiration of, 271–72 as insurance, 272–73 put, 273 Orion Pictures, 96 OshKosh B’Gosh, 193 over-the-counter exchange, 279 Owens Corning, 34 Pacific Telesis, 135, 213 Pampers, 107–8, 198 Pan Am, 89 Paramount, 96 Paramount Famous Lasky, 71 patents, 141 Paychex, 25, 26 PBS, 40 Pebble Beach, 40, 102, 125, 140, 209 Penn Central, 128, 134 as asset-play company, 126, 209 bankruptcy of, 122, 207 book value of, 207, 209 as turnaround company, 122, 124, 213 Pennzoil, 205 pension funds, 59, 64 pension plans, 217 People, 60 People Express, 42, 89, 269 Pep Boys, 59, 95–96, 131, 145, 190, 192, 214 Pepsi-Cola, 284 p/e ratio, see price/earnings ratio percent of sales, 198 Perot, Ross, 14, 170 Petrie, Milton, 248 Phelps Dodge, 34, 187 Philadelphia Electric, 288 Philip Morris, 129, 214, 246, 281 growth, history of, 217–18, 261 Kraft bought by, 133 negative-growth industry and, 152, 217–18 stock chart of, 262–63 Photronics, 24 Pickens, Boone, 257, 279 picks and shovels strategy, 14 Pic ’N’ Save, 59, 95, 110, 192 Piedmont Airlines, 42, 269 Pier 1 Imports, 36, 193, 247 Pizza Time Theater, 158 plastics, 119, 133 Polaroid, 49, 98n, 171–72, 254, 259, 274 portfolios: diversity of, 59–60, 239, 240 insurance for, 272–73 minimizing risk in, 241 multibaggers and, 32–33 rotating stocks in, 242–43 size of, 40, 239–41 stop orders and, 244 Postum, 71 Potter, Beatrix, 194 Premark International, 134 Prepaid Legal Services, 26 pretax profit margin, 220–21 Priam, 158 Price, Michael, 56 Price Club, 153, 268 price/earnings ratio, 165–69, 199 dot.com stocks and, 12–13 of finance companies, 200 growth rate and, 199, 218, 219 high, 165–69, 170–71 interest rates and, 172 levels of, 169, 170–71, 172 meaning of, 169 overpricing of stocks and, 168, 171–72 relativity of, 170 of stock market, 172 Primerica, 281 Pritzkers, 257 Procter and Gamble, 107–8, 109, 129, 187, 198 earnings of, 217–18 as stalwart company, 112, 115, 118 stock chart of, 115 products, demand for, 142, 254 profit margin, calculation of, 220–21 Radice, 89, 208–9 raiders, 284 see also acquisitions Ralston-Purina, 112, 118, 129, 162, 207 random-walk hypothesis, 52 Ranger Oil, 53 Raymond Industries, 212 RCA, 71, 72, 264 real estate: advantages of, 77–80 houses and, 77–80 recession of 1981–82, 86 recession of 1990, 23 Reebok, 193 Reichmanns, 256, 279 Reliance electric, 154 Remington Typewriter, 71 reports, analysts’: on Internet, 16–17 see also S&P reports reports, of companies, 194–97 see also annual reports; balance sheets Reserve Fund, 69 Resorts International, 275 restrictions, trade, 64 Retin-A, 108 Reynolds Metals, 106, 187 Rite Aid, 281 Robitussin, 142, 209 Rockefeller, John D., 66, 204 Rogers, Jimmy, 56 Rogers, Will, 54 Rukeyser, Louis, 279 Safety-Kleen, 132, 133, 137, 145, 159 sales, percent of, 198 S&P reports, 17, 21, 27, 69, 74, 123, 136, 170, 184, 197, 199, 238 Santa Fe Southern Pacific, 126, 210 Sara Lee, 37 savings accounts, 69 savings-and-loan stocks, 17, 54 savings bonds, U.S., 69 savings rates, U.S., 285 Sceilig Hotel, 28, 29 Schlumberger, 13, 34, 98, 100, 201 SCI Systems, 160 Scotty’s, 268 Scudder, Stevens and Clark, 65 Seagram, 212 Searle, 211 Sears, 59, 62, 110, 223 Securities and Exchange Commission, 64, 143 Sensormatic, 161, 223, 229–30 Service Corporation International (SCI), 35, 36, 58, 116, 137–38, 139–40 service sector, U.S., growth of, 283 7-Eleven, 42, 54, 59, 181 Seven Oaks International, 62–63, 64, 66, 131 Seven-Up, 218 shares: buybacks of, 144–45, 153, 157, 197 insider buying of, 135, 142–43 insider selling of, 143–44 see also companies; stock Shearson, 58 Shelley, Percy Bysshe, 184 Shoney’s, 116, 131, 164, 168, 261 Shop and Go, 54 shorting stocks, 273–75 Siliconix, 24 Singer, 134 Singleton, Henry E., 144 Smith, Morris, 127 SmithKline Beckman, 97–98, 99, 100, 110, 141, 187, 266 Smith Labs, 157, 158 Sorg Paper, 51 Soros, George, 56 Southland, 54 Southmark, 207 Southwestern Bell, 135 Spectrum Surveys, 136 Spielberg, Steven, 96 spinoffs, 133–36, 159 splits, 34n Sprague Tech., 134 Sprint, 135 SPUD, 158 Squibb, 281 SS Kresge, 280 SSMC, 134 Staples, 25, 26 Star Wars, 163 Steinberg, Saul, 256, 257 Sterling Drug, 108, 187 stock, indexes, 280 stock charts, 98n, 112, 164 of Avon, 166 of Bristol-Myers, 116–17 of Chrysler, 147 of Con Ed, 206 of Dow Chemical, 165 of Dreyfus, 103 of Ford Motor, 120–21 of Genesco, 156 of Home Shopping Network, 150 of Houston Industries, 113 of The Limited, 168 of Marriott, 168 of Melville, 156 of Merck, 267 of Navistar, 146 of Philip Morris, 262–63 of Procter and Gamble, 115 of Shoney’s, 168 of SmithKline Beckman, 99 of Wal-Mart Stores, 114 stock market: breaks in, 246 bullish vs. bearish, 22–23 cause and effect in, 50 distrust of, 48, 73 fluctuations of, 29–30, 82 individual stocks vs., 89–91 interest rates and, 85 Japanese, 55, 278 in 1980s, 278 of 1987–88 vs. 1929–30, 22, 282–283 in 1990s, 9, 10 in October, 1987, 29–30, 69, 86, 278, 280, 282 October, 1988 recovery of, 30 overvaluation of, 90 p/e ratio of, 172 predictability of, 84–88 preparedness for, 86–87 random-walk hypothesis of, 52 as stud poker game, 74–75, 76 theories of, 52 trading hours of, 278 turnover in, 281 volume of, 281 weak, 33 world events and, 276–80, 283 see also investment; stocks stocks: annual gain of, 72, 85 approved lists of, 59–60 average return of, 237–38 bargain, 261–64 blue-chip, see blue-chip stocks bonds vs., 69–70, 88, 112, 237 cash flow and, 214 charts of, see stock charts choosing, 95–97, 98, 231–33; see also investment classification of, 110–27 comebacks of, 264 common misconceptions of, 258–69 conservative, 265; see also blue-chip stocks diluting of, 145 dividends and, see dividends dot.com, see dot.com stocks efficient market hypothesis of, 52 falling of, 259–60, 269–70 fluctuations of, 29–30, 82 frequent trading of, 238–39 hot, 149–52 initial public offering (IPO) of, 159 insider buying of, 135, 142–43 insider selling of, 143–44, 180 institutional ownership of, 55, 57, 136, 142–45, 179 Internet and, 10–12 length of ownership of, 112, 115, 266 market vs., 89–91 money-market funds vs., 69, 88 overpricing of, and p/e ratio, 168, 171–72 portfolios of, see portfolios public attitude toward, 47–48, 73 real estate vs., 78–80 rising of, 260–61, 269–70 risk of, 71–76, 80 shorting of, 273–75 summary evaluation of, 227–33 whisper, 157–59, 280 see also companies; investment; stock market stock tables, 165–68 Stop & Shop, 59, 128, 163, 261 stock, return of, 33 stop orders, 244 Storer Communications, 101–2, 256 street lag, 57–60, 101–2 Student Loan Marketing, 247 Subaru, 33, 34n, 36, 58, 95, 115, 251, 260 Sullivan, D.


pages: 231 words: 64,734

Safe Haven: Investing for Financial Storms by Mark Spitznagel

Albert Einstein, Antoine Gombaud: Chevalier de Méré, asset allocation, behavioural economics, bitcoin, Black Swan, blockchain, book value, Brownian motion, Buckminster Fuller, cognitive dissonance, commodity trading advisor, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, delayed gratification, diversification, diversified portfolio, Edward Thorp, fiat currency, financial engineering, Fractional reserve banking, global macro, Henri Poincaré, hindsight bias, Long Term Capital Management, Mark Spitznagel, Paul Samuelson, phenotype, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, rent-seeking, Richard Feynman, risk free rate, risk-adjusted returns, Schrödinger's Cat, Sharpe ratio, spice trade, Steve Jobs, tail risk, the scientific method, transaction costs, value at risk, yield curve, zero-sum game

But there's “many a slip,” as stock market sell‐offs can reflexively lead to lower fundamental values as well; the cause‐and‐effect relationship is rarely straightforward in investing. And although the economists take the idea way too far, there's something to their “efficient markets” argument that it can't be common knowledge that a stock is undervalued (after a crash, say)—otherwise its price wouldn't be there. This is why economists typically push their “random walk down Wall Street” hypothesis, where once a big crash occurs, we have no new information going forward about how stocks will perform; bygones are bygones. Sadly, we are stuck with our null hypothesis that buying the SPX after a crash doesn't make any difference to its subsequent returns.

After the second roll, your wealth would be 1 × 1.05 × 1.5 = 1.575; by the sixth roll, it would be: And this is after, in this case, each side of the die randomly turning up exactly once. It would seem that this should be something like your average, right? And yet you're underwater? For the full 300 rolls, you would, of course, continue these multiplicative steps all the way to your ending wealth. (This is what is known as a geometric random walk.) Something to note from this multiplicative compounding example: Because each subsequent return is multiplied by the next, and thanks to the commutative property of multiplication, it doesn't matter when that 50% loss happens. Whether the side with a 1 comes up in the third or the last roll, the effect on your final wealth is the same.

See also Cost‐effectiveness analysis (CEA) with insurance, 88–92, 94–95 and Kelly criterion, 81, 84–87 and Kelly optimal bet size, 84, 86 with Nietzsche's demon, 70–72 and non‐ergodicity, 72–77 optimizing, 53–54 in Petersburg merchant trade, 48–52 in Petersburg wager, 41–43 raising, 187 with Schrödinger's demon, 68 of SPX portfolio with safe havens, 132–135 as time average, 75 25‐year compounded SPX returns, 121 for US Treasuries, 172 Geometric effect: in cost‐effectiveness analysis, 136–142 for gold, 181 in Petersburg merchant trade, 47 for real‐world safe havens, 184 and safe haven frontier, 186 tradeoff between arithmetic cost and, 152 when reshuffling returns, 143 Geometric growth, 49, 50 Geometric mean maximization criterion, 54, 80, 81 Geometric random walk, 70 Gladiators, 162 Global macro strategy, 108 Goal of investing, 15, 99 Gold, 166, 178–184, 187 Golden Theorem, 30, 67, 70, 73 Goldilocks weightings, 81, 91–92, 134 Graham, Benjamin, 9, 55, 83, 104, 151 Great Pirates, 157–160, 198 Growth: compound, 49–50, 135 compound annual growth rate, 15, 16, 20–21.


pages: 500 words: 145,005

Misbehaving: The Making of Behavioral Economics by Richard H. Thaler

3Com Palm IPO, Alan Greenspan, Albert Einstein, Alvin Roth, Amazon Mechanical Turk, Andrei Shleifer, Apple's 1984 Super Bowl advert, Atul Gawande, behavioural economics, Berlin Wall, Bernie Madoff, Black-Scholes formula, book value, business cycle, capital asset pricing model, Cass Sunstein, Checklist Manifesto, choice architecture, clean water, cognitive dissonance, conceptual framework, constrained optimization, Daniel Kahneman / Amos Tversky, delayed gratification, diversification, diversified portfolio, Edward Glaeser, endowment effect, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, experimental economics, Fall of the Berlin Wall, George Akerlof, hindsight bias, Home mortgage interest deduction, impulse control, index fund, information asymmetry, invisible hand, Jean Tirole, John Nash: game theory, John von Neumann, Kenneth Arrow, Kickstarter, late fees, law of one price, libertarian paternalism, Long Term Capital Management, loss aversion, low interest rates, market clearing, Mason jar, mental accounting, meta-analysis, money market fund, More Guns, Less Crime, mortgage debt, Myron Scholes, Nash equilibrium, Nate Silver, New Journalism, nudge unit, PalmPilot, Paul Samuelson, payday loans, Ponzi scheme, Post-Keynesian economics, presumed consent, pre–internet, principal–agent problem, prisoner's dilemma, profit maximization, random walk, randomized controlled trial, Richard Thaler, risk free rate, Robert Shiller, Robert Solow, Ronald Coase, Silicon Valley, South Sea Bubble, Stanford marshmallow experiment, statistical model, Steve Jobs, sunk-cost fallacy, Supply of New York City Cabdrivers, systematic bias, technology bubble, The Chicago School, The Myth of the Rational Market, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, transaction costs, ultimatum game, Vilfredo Pareto, Walter Mischel, zero-sum game

., 29 surveys used in experiments of, 38 psychological accounting, see mental accounting “Psychology and Economics Conference Handbook,” 163 “Psychology and Savings Policies” (Thaler), 310–13 Ptolemaic astronomy, 169–70 public goods, 144–45 Public Goods Game, 144–46 Punishment Game, 141–43, 146 Pythagorean theorem, 25–27 qualified default investment alternatives, 316 quantitative analysis, 293 Quarterly Journal of Economics, 197, 201 quasi-hyperbolic discounting, 91–92 quilt, 57, 59, 61, 65 Rabin, Matthew, 110, 181–83, 353 paternalism and, 323 racetracks, 80–81, 174–75 Radiolab, 305 randomized control trials (RCTs), 8, 338–43, 344, 371 in education, 353–54 Random Walk Down Walk Street, A (Malkiel), 242 rational expectations, 98, 191 in macroeconomics, 209 rational forecasts, 230–31 rationality: bounded, 23–24, 29, 162 Chicago debate on, 159–63, 167–68, 169, 170, 205 READY4K!, 343 real business cycle, 191 real estate speculation, 372 rebates, 121–22, 363 recessions, 131–32 fiscal policy in, 209 reciprocity, 182 Reder, Mel, 159 Reeves, Richard, 330, 332 reference price, 59, 61–62 regression toward the mean, 222–23 research and development, 189 reservation price, 150 retirement, savings for, see savings, for retirement return, risk vs., 225–29 returns, discounts and, 242 revealed preferences, 86 “right to carry” law, 265n risk: measurement of, 225–29 return vs., 225–29 “Risk and Uncertainty: A Fallacy of Large Numbers” (Samuelson), 194 risk aversion, 28–29, 33, 83, 84 crowds and, 301, 369 on Deal or No Deal, 298–99 equity premium and, 191–92 of managers, 190–91 moderate vs. extreme, 298–99 risk premium, 14–16, 226 irrationality of, 16–17 risk-seeking behavior, 81, 83 roadside stands, 146–47 Robie House, 270 Rochester, University of, 41, 51, 205, 216 Roger and Me (film), 122 rogue traders, 84 Roll, Richard, 167, 208 Romer, David, 292 Rosen, Sherwin, 12, 15, 17, 21, 35, 42, 321 at behavioral economics debate, 159 Rosett, Richard, 17, 34, 46, 68, 73 Ross, Lee, 181 Ross, Steve, 167 Roth, Alvin, 130, 148 Royal Dutch Shell, 248, 249, 251 rules (in self-control), 106–9, 111 Russell, Thomas, 18, 203 Russell Sage Foundation, 177–78, 179, 181, 185 Russell Sage summer camps, 181–84, 199 Russian roulette, 13–14 Russo, Jay, 122 S&P 500, 232, 233 Sadoff, Sally, 354 safety, paying for, 13–14 St.

Look around”: Quoted in Fox (2009), p. 199. 240 more rigorous, thorough, and polite version of the “idiots” paper: De Long et al. (1990). 241 “an expensive monument”: Graham ([1949] 1973), p. 242. 242 That is exactly what we found: Lee, Shleifer, and Thaler (1991). 242 thesis on closed-end funds: Thompson (1978). 242 A Random Walk Down Wall Street: Malkiel (1973). 243 “But they can’t”: Chen, Kan, and Miller (1993), p. 795. 243 the last set of stones: The five papers are: Lee, Shleifer, and Thaler (1991), Chen, Kan, and Miller (1993a), Chopra et al. (1993a), Chen, Kan, and Miller (1993b), and Chopra et al. (1993b). Chapter 26: Fruit Flies, Icebergs, and Negative Stock Prices 249 LTCM had collapsed: Lowenstein (2000). 249 in a paper they published on this topic: Shleifer and Vishny (1997). 250 an academic paper about the . . . episode: Lamont and Thaler (2003). 251 “we might define an efficient market”: Black (1986), p. 553. 252 “liar loans”: See Mian and Sufi (2014).

Economist Rex Thompson wrote his thesis on closed-end funds, and found that a strategy of buying the funds with the biggest discounts earned superior returns (a strategy also advocated by Benjamin Graham). The well-known efficient market guru Burton Malkiel, author of the perpetual best-seller A Random Walk Down Wall Street, has also advocated such a strategy. Nevertheless, our paper made some people upset, and it infuriated Merton Miller, the Nobel Prize–winning financial economist at the University of Chicago who was Shleifer’s senior colleague. To this day I do not know exactly what about our paper made Miller so upset, but I suspect that while others had written about these funds before, we were the first to do so since Graham without following the mannerly procedure of apologizing and making excuses for our anomalous findings.


pages: 491 words: 131,769

Crisis Economics: A Crash Course in the Future of Finance by Nouriel Roubini, Stephen Mihm

Alan Greenspan, Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, bond market vigilante , bonus culture, Bretton Woods, BRICs, British Empire, business cycle, call centre, capital controls, Carmen Reinhart, central bank independence, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, dark matter, David Ricardo: comparative advantage, debt deflation, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, fiat currency, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, financial intermediation, full employment, George Akerlof, Glass-Steagall Act, global pandemic, global reserve currency, Gordon Gekko, Greenspan put, Growth in a Time of Debt, housing crisis, Hyman Minsky, information asymmetry, interest rate swap, invisible hand, Joseph Schumpeter, junk bonds, Kenneth Rogoff, laissez-faire capitalism, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, market fundamentalism, Martin Wolf, means of production, Minsky moment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, new economy, Northern Rock, offshore financial centre, oil shock, Paradox of Choice, paradox of thrift, Paul Samuelson, Ponzi scheme, price stability, principal–agent problem, private sector deleveraging, proprietary trading, pushing on a string, quantitative easing, quantitative trading / quantitative finance, race to the bottom, random walk, regulatory arbitrage, reserve currency, risk tolerance, Robert Shiller, Satyajit Das, Savings and loan crisis, savings glut, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, subprime mortgage crisis, Suez crisis 1956, The Great Moderation, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, too big to fail, tulip mania, Tyler Cowen, unorthodox policies, value at risk, We are all Keynesians now, Works Progress Administration, yield curve, Yom Kippur War

A professor named Eugene Fama and others sympathetic to laissez-faire economic policies began to construct elaborate mathematical models aimed at proving that markets are utterly rational and efficient. Again, they believed that the price of any given asset at any time is always completely correct. In other words, an asset cannot be overvalued or undervalued; the current price is the right price, nothing more and nothing less. This theory posited that all public information is immediately and accurately incorporated into the asset’s price, and any future price changes must depend on things not yet known. Therefore, predicting where prices will move next is impossible. This insight begat the “random walk” theory: that when it comes to picking stocks, there is no point trying to beat the market.

Northern Pacific Railroad Northern Rock Norway Obama, Barack (Obama administration) regulation and Obstfeld, Maurice Office of National Insurance Office of the Comptroller of the Currency Office of Thrift Supervision offshore financial centers oil price of shocks open market operations optimism Options Clearing Corporation “originate and distribute” model “originate and hold” model overnight repro financing over-the-counter (OTC) derivatives Pakistan pandemics crises compared with death of decoupling and disease vectors and emerging economies and financing of shared excesses and panics, financial of 1825 of 1837 of 1847 of 1857 of 1866 of 1873 of 1893 of 1907 paradox of thrift Paulson, Henry pension fund managers pension funds Peru peso, Argentinian peso, Mexican Philippines Philippon, Thomas Plato Poland politics crisis in Japan lobbying and Ponzi borrowers portfolio insurance Portugal pound, British poverty, the poor prices: decline in deflation and, see deflation feedback theory and future increases in of gold housing increase in monetarist view of oil real estate Primary Dealer Credit Facility (PCDF) Prince, Chuck principal-agent problem Principles of Political Economy (Mills) procyclicality production, industrial productivity proprietary trading strategies protectionism see also tariffs Prussia Public-Private Investment Program (PPIP; Pee-Pip) public works projects Putin, Vladimir quantitative easing railroads Great Britain and Rajan, Raghuram Rand, Ayn random walk theory Rashomon (film) rating agencies reforms and see also specific ratings real estate boom price of real estate bubbles in Japan recession China and deflation and in emerging Europe in Europe exogenous negative supply-side shock and fiscal policy and Great Moderation and in Japan in Latin America monetary policy and in U.S.

.: Cambridge University Press, 2005), 179-243, 278-96, 322-83. 40 Louis Bachelier: Louis Bachelier, “Théorie de la spéculation,” in Annales Scientifiques de l’École Normale Supérieure 3 (1900): 21-86; Justin Fox, The Myth of the Rational Market: A History of Risk, Reward, and Delusion on Wall Street (New York: Harper Business, 2009), 6-8. 40 “The consensus of judgment . . .”: Lawrence quoted in John Kenneth Galbraith, The Great Crash, 1929 (Boston: Houghton Mifflin, 1954), 75. 41 postwar academic departments: Fox, Myth of the Rational Market, 89-107. 41 “random walk” theory: Burton G. Malkiel, A Random Walk Down Wall Street (New York: W.W. Norton, 1973). 41 “Don’t bother . . .”: Andrew W. Lo and A. Craig MacKinlay, A Non-Random Walk Down Wall Street (Princeton, N.J.: Princeton University Press, 1999), 6. 41 Yale economist Robert Shiller: Robert J. Shiller, “Consumption, Asset Markets and Macroeconomic Fluctuations,” Carnegie-Rochester Conference Series on Public Policy 17 (1982): 203-38. 42 “While markets are not totally crazy . . .”: Robert J.


pages: 250 words: 79,360

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It by Erica Thompson

Alan Greenspan, Bayesian statistics, behavioural economics, Big Tech, Black Swan, butterfly effect, carbon tax, coronavirus, correlation does not imply causation, COVID-19, data is the new oil, data science, decarbonisation, DeepMind, Donald Trump, Drosophila, Emanuel Derman, Financial Modelers Manifesto, fudge factor, germ theory of disease, global pandemic, hindcast, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, implied volatility, Intergovernmental Panel on Climate Change (IPCC), John von Neumann, junk bonds, Kim Stanley Robinson, lockdown, Long Term Capital Management, moral hazard, mouse model, Myron Scholes, Nate Silver, Neal Stephenson, negative emissions, paperclip maximiser, precautionary principle, RAND corporation, random walk, risk tolerance, selection bias, self-driving car, social distancing, Stanford marshmallow experiment, statistical model, systematic bias, tacit knowledge, tail risk, TED Talk, The Great Moderation, The Great Resignation, the scientific method, too big to fail, trolley problem, value at risk, volatility smile, Y2K

On such relatively simple assumptions are built complex mathematical structures of pricing, arcane trading strategies and arbitrage. The random-walk model for prices is extended to the well-known Black–Scholes equation for option pricing: given that a certain stock has in the recent past exhibited movements with certain random properties, we can calculate a ‘fair’ price to pay for the option to buy that same stock after a period of time has elapsed. Now, one interesting question is whether the price calculated as being fair is actually the price that is paid for such a contract. Economists Fischer Black and Myron Scholes compared the theoretical predictions of their equation with the actual prices of options traded by a New York broker and found a systematic mispricing in which the sellers of options tended to get a ‘better deal’ than buyers.

This is an ideal playground for the mathematician or mathematical modeller, since success is extremely well-defined and the terms of interaction with the market are clearly constrained: if you wish to make a transaction, you must find a counter-party to take the other side of the transaction and thus generate a price for whatever it is you are trading. The existence of a time series of previous data for similar transactions gives you some evidence on which to base decisions. The past data might also give a reason to think that they are created by some underlying generative process which might be modellable and in principle at least some characteristics of its behaviour might be predictable. Theories of stock behaviour that model prices as a ‘random walk’ assume that the price movements from day to day, rather than being driven by some predictable process, happen ‘at random’.

In particular, it became clear that levels of volatility in stocks are not constant, and they do not always follow convenient log-normally distributed random walks. The events of October 1987 were so far outside the range of plausible outcomes given the models in use at the time that they very effectively falsified the models. But this crisis of confidence in markets, models and mathematics did not result in the summary ejection of the Black–Scholes equation from the traders’ toolbox. Instead, and interestingly, it began to form a framework for discussing model imperfection. Rather than taking the straightforward approach of estimating parameters to determine an outcome (option price), the inverse perspective took the observed option price and back-calculated an ‘implied volatility’, a quantification of the expectations about the future that were contained in the prices.


Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

For example, I have seen blogs comment recently that stock market highs and lows are more likely to occur near the open and the close, vs. the middle of the day. It’s the same fallacy. In a random walk process, there is not a uniform distribution of highs and lows throughout the day, week, month or year. Instead, we see a U-shaped pattern with more highs and lows near the start and the end of the series. This fundamental property of random walks is described by a counterintuitive branch of probability known as Arcsine law. If you spend a bit of time thinking about a random walk, it starts to make sense that the high or low is more likely to occur at the start or end of the year. Imagine a series of coin flips where you simply add 1 for heads or subtract 1 for tails.

The 1956 classic “How to Lie with Statistics” by Darrell Huff does a super job of explaining the basics of how to handle statistics like this. I think every human being should read that book, even if they don’t care about statistics or finance. Example 4 : Arcsine Law People don’t always understand random walks This example has a direct application to trading and finance. In fact, before I knew about Arcsine Law, I made a mistake that I now see others making from time to time. This one is crazy complicated, so if you don’t understand the reasoning, just at least make sure you remember the conclusion.

The longer you flip the coin, the farther the sum will move away from the starting point (zero) and thus the start and end point are more likely to be the extreme points relative to any point in between. Our intuition is that they would oscillate around zero, but this is not the case. They slowly move away. Figure 7.18 demonstrates how random walks slowly make their away further and further from their starting point of zero. Therefore, the extreme low and high points appear more often near the start or the end of the series. Example 5 : The Birthday Paradox People don’t always understand probability Here’s a simpler one. There are 70 people in a room, what are the odds that two of them will have the same birthday?


pages: 348 words: 83,490

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Alan Greenspan, Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, behavioural economics, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, John Bogle, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Performance of Mutual Funds in the Period, Pierre-Simon Laplace, power law, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Richard Florida, Richard Thaler, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, Stuart Kauffman, survivorship bias, systems thinking, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

Exhibit 8.1 provides some numbers to illustrate these concepts.6 The basis for this analysis is an annual geometric mean return of 10 percent and a standard deviation of 20.5 percent (nearly identical to the actual mean and standard deviation from 1926 through 2006).7 The table also assumes that stock prices follow a random walk (an imperfect but workable assumption) and a loss-aversion factor of 2. (Utility = Probability of a price increase - probability of a decline x 2.) EXHIBIT 8.1 Time, Returns, and Utility Source: Author analysis. A glance at the exhibit shows that the probability of a gain or a loss in the very short term is close to fifty/fifty. Further, positive utility—essentially the avoidance of loss aversion—requires a holding period of nearly one year.

The bad news, as physicist Phil Anderson notes above, is that the tails of the distribution often control the world. Tell Tail Normal distributions are the bedrock of finance, including the random walk, capital asset pricing, value-at-risk (VaR), and Black-Scholes models. Value-at-risk models, for example, attempt to quantify how much loss a portfolio may suffer with a given probability. While there are various forms of VaR models, a basic version relies on standard deviation as a measure of risk. Given a normal distribution, it is relatively straightforward to measure standard deviation, and hence risk. However, if price changes are not normally distributed, standard deviation can be a very misleading proxy for risk.2 The research, some done as far back as the early 1960s, shows that price changes do not follow a normal distribution.

Of note, too, is immediately after DiMaggio’s fifty-six-game streak was broken, he went on to a sixteen-game hitting streak. So he got a hit in seventy-two of seventy-three games during the course of the 1941 season. 8 Here’s a sample of some references (there are too many to list exhaustively): Burton G. Malkiel, A Random Walk Down Wall Street (New York: W. W. Norton & Company, 2003), 191; Nassim Taleb, Fooled By Randomness: The Hidden Role of Chance in Markets and in Life (New York: Texere, 2001), 128-131; Gregory Baer and Gary Gensler, The Great Mutual Fund Trap (New York: Broadway Books, 2002), 16-17; Peter L. Bernstein, Capital Ideas: The Improbable Origins of Modern Wall Street (New York: Free Press, 1992), 141-43. 9 Baer and Gensler, The Great Mutual Fund Trap, 17.


pages: 261 words: 86,905

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, asset allocation, Basel III, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamond, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, fear index, financial engineering, financial innovation, Flash crash, forward guidance, Garrett Hardin, Gini coefficient, Glass-Steagall Act, global reserve currency, high net worth, High speed trading, hindsight bias, hype cycle, income inequality, inflation targeting, interest rate swap, inverted yield curve, Isaac Newton, Jaron Lanier, John Perry Barlow, joint-stock company, joint-stock limited liability company, junk bonds, Kodak vs Instagram, Kondratiev cycle, Large Hadron Collider, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, low interest rates, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Ponzi scheme, precautionary principle, proprietary trading, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, trickle-down economics, two and twenty, Two Sigma, Tyler Cowen, Washington Consensus, wealth creators, working poor, yield curve

In other words, Amazon is 300 times more expensive than Apple. That might seem nuts, but the price is based on the idea that in the future, Amazon will earn huge amounts of money, so you buy the share now in order to get in early for the huge takeoff that is going to come. Apple on the other hand is more of a known quantity, so you are getting what you pay for. It’s very difficult to know what the realistic P/E ratio is for any stock: as Burton Malkiel put it in his efficient-market theory investment classic, A Random Walk Down Wall Street, “God Almighty does not know the proper price-earnings-multiple for a common stock.” 63 Historically, companies with low P/E ratios—what are known as “value stocks”—have tended to outperform those with high P/Es, in part because a high P/E implies high expectations that are easily disappointed.

Commenting on her in 1979, he said, “This election was about a woman who believes in inequality, passionately, who isn’t Keynesian, who is not worried about dole queues.” In his biography of Thatcher, Charles Moore says that in Walden’s view, “if interviewers had wanted to find the truth, they should have asked her, ‘Mrs Thatcher, do you believe in a more unequal society?’ ” # Book recommendation: Burton Malkiel’s classic A Random Walk Down Wall Street lays out a thoroughly convincing explication of the thesis, with lots of practical advice for private investors. Part II A LEXICON OF MONEY the aaaaa number A term I’ve just made up to denote 16,438, for the purpose of making sure it comes first in this lexicon. This number is, in the words of Melinda Gates, “the most important statistic in the world.”

Alice Shroeder’s The Snowball, a biography of Warren Buffett, is very different in tone and texture, but it brings in a lot of stories and information from the world of finance, as does Sebastian Mallaby’s More Money Than God, a (suprisingly and convincingly positive) study of hedge funds. Some of you may well be thinking: but how is any of this going to help me become rich? If you are, here are two books for you: Burton Malkiel’s A Random Walk Down Wall Street, which explains efficient-market theory for the ordinary investor, and John Kay’s The Long and Short of It. Kay’s book is the best book ever written for the British individual investor, by a country mile. Ben Graham’s The Intelligent Investor, the first book written on the subject, remains one of the best.


pages: 407 words: 114,478

The Four Pillars of Investing: Lessons for Building a Winning Portfolio by William J. Bernstein

Alan Greenspan, asset allocation, behavioural economics, book value, Bretton Woods, British Empire, business cycle, butter production in bangladesh, buy and hold, buy low sell high, carried interest, corporate governance, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, diversification, diversified portfolio, Edmond Halley, equity premium, estate planning, Eugene Fama: efficient market hypothesis, financial engineering, financial independence, financial innovation, fixed income, George Santayana, German hyperinflation, Glass-Steagall Act, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Bogle, John Harrison: Longitude, junk bonds, Long Term Capital Management, loss aversion, low interest rates, market bubble, mental accounting, money market fund, mortgage debt, new economy, pattern recognition, Paul Samuelson, Performance of Mutual Funds in the Period, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Savings and loan crisis, South Sea Bubble, stock buybacks, stocks for the long run, stocks for the long term, survivorship bias, Teledyne, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

., 100 Precious metals stocks, 123–124, 155 Present value vs. discount rate, discounted dividend model (DDM), 46–48 Press coverage, 219–225 Prestiti, Venetian, 10–13 Price, annuity, 9–13 Price-to-earnings (P/E) ratio, 58, 68–69, 150, 174-175 Prices, stock (See Stock prices) Primerica, 83 Principal transaction, 196 Principia Pro software, Morningstar Inc., 98, 152, 205 Prudential-Bache, 200 Psychology of investing (Pillar 3) (See Behavioral economics) Purchase vs. investment, 45 Quinn, Jane Bryant, 220, 221 Radio Corporation of America, 132, 147 Railroad bubble, 143-145, 158, 159–160 “Railway time,” 144 “Random walk,” 25 A Random Walk Down Wall Street (Malkiel), 224 Randomness in market, 25, 175–177, 186 (See also Performance) Raskob, John J., 65, 147, 148 RCA, 132, 147 Real Estate Investment Trusts (REITs), 69, 72, 109, 123, 124, 250, 254, 263, 296 Real (inflation-adjusted) returns bonds, twentieth century, 19 discounted dividend model (DDM) for different instruments, 68–69 establishment of, 7 future outlook, 67–71 retirement investments, 230 retirement withdrawal strategies, 231–234 stock, 26 and young savers, 238–239 Realized returns, 71–73 Rebalancing, 286-292 Regan, Donald, 194 Regret avoidance, 177 Reinvesting income (benefits of), 61 REITs (Real Estate Investment Trusts), 69, 72, 109, 123, 124, 250, 254, 263, 296 Retained earnings and dividends paid, 59–60 Retirement planning, 229–241 end-period wealth, 26–27 immortality assumption, 229–235 impact of crash in stock market, 61-62 portfolio rebalancing, 276, 282, 285, 286-293 vs. young savers, 236–239 Returns in brokerage accounts, 198–199, 200 calculation of, 186–187n1 expected (See Expected returns) and market capitalization, 32–34 mutual funds, 203-208 rebalanced, 286-293 Risk bond prices, 11-20 company quality, 34–38 cyclical companies, 64 defined, 11 discounted dividend model (DDM), 41-42 historic record as gauge of, 32 interest rates, 13, 260 long-term, 22-29 and market capitalization, 34 and measurement, 22–29 Risk-return relationship diversification and rebalancing, 286-291 historical perspective, 6–13, 22-29, 38 retirement years, 231–236 short- vs. long-term risk and behavioral economics, 172–173, 184-185 summary, by investment type, 38–39 Risk premium, 184 Riskless assets, 110, 114, 260, 264 Rockefeller, Percy, 147 Rocket (Stephenson), 143 Roman Empire, interest rates in, 8–9 Russell 2000, 248 Russell 3000, 245, 246 Safety penalty, 184 Sales training for brokers, 200 Samuelson, Paul, 214 Sanborn, Robert, 84–85 Santayana, George, 6, 129 Sarnoff, Mrs.

The pattern of annual stock returns is almost totally random and unpredictable. The return in the last year, or the past five years, gives you no hint of next year’s return—it is a “random walk.” As we’ll see later, no one—not the pundits from the big brokerage firms, not the newsletter writers, not the mutual fund managers, and certainly not your broker—can predict where the market will go tomorrow or next year. So the twentieth century has seen three severe drops in stock prices, one of them catastrophic. The message to the average investor is brutally clear: expect at least one, and perhaps two, very severe bear markets during your investing career.

First, do not read any more magazine or newspaper articles on finance, and, whatever you do, do not watch Wall Street Week, Nightly Business Report, or CNBC. With the extra hour or two you’ll gain each week from turning off the TV, I would start a regular reading program. Begin with two classics: 1. A Random Walk Down Wall Street, by Burton Malkiel, is an excellent investment primer. It explains the basics of stocks, bonds, and mutual funds and will reinforce the efficient market concept. 2. Common Sense on Mutual Funds, by John Bogle, will provide more information than you ever wanted to know about this important investment vehicle.


pages: 440 words: 108,137

The Meritocracy Myth by Stephen J. McNamee

Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, American ideology, antiwork, Bernie Madoff, British Empire, business cycle, classic study, collective bargaining, computer age, conceptual framework, corporate governance, deindustrialization, delayed gratification, demographic transition, desegregation, deskilling, Dr. Strangelove, equal pay for equal work, estate planning, failed state, fixed income, food desert, Gary Kildall, gender pay gap, Gini coefficient, glass ceiling, helicopter parent, income inequality, informal economy, invisible hand, job automation, joint-stock company, junk bonds, labor-force participation, longitudinal study, low-wage service sector, marginal employment, Mark Zuckerberg, meritocracy, Michael Milken, mortgage debt, mortgage tax deduction, new economy, New Urbanism, obamacare, occupational segregation, old-boy network, pink-collar, plutocrats, Ponzi scheme, post-industrial society, prediction markets, profit motive, race to the bottom, random walk, Savings and loan crisis, school choice, Scientific racism, Steve Jobs, The Bell Curve by Richard Herrnstein and Charles Murray, The Spirit Level, the strength of weak ties, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, too big to fail, trickle-down economics, upwardly mobile, We are the 99%, white flight, young professional

Some of the variance “unexplained” by these models could come from a combination of leaving out factors that matter and from less-than-perfect measures of the factors included. But some of the unexplained or residual variation is also likely due to simple random variation—or, in more everyday language, “luck.” The Random-Walk Hypothesis So far in this chapter, our discussion has revolved around education, jobs, and income. We have argued that the “going rate” of return for the jobs that people hold depends, at least in part, on factors that lie outside the control of individual workers themselves. Getting ahead in terms of the occupations people hold and the pay they receive involves an element of luck—being in the right place at the right time.

Finally, developmental disequilibrium refers to unequal conditions of development in different countries that create opportunities to introduce products and services available in one place that are not yet available in another place. To some extent, those who are the most clever or most insightful might be better able to anticipate various market shakeups. However, the “random-walk hypothesis” developed by economists seems to account best for who ends up with the right idea, the right product, or the right service. The argument is simply that striking it rich tends to be like getting struck by lightning: many are walking around, but only a few get randomly struck. Large fortunes tend to be made quickly, taking early advantage of market shakeups.

Those who succeed do not necessarily work harder than those who fail; nor are they necessarily more inherently capable or meritorious. Having sufficient start-up capital to launch new enterprises (it takes money to make money) and being in the right place at the right time with the right idea (random-walk hypothesis) do, however, have a great deal to do with entrepreneurial success. The Case of Microsoft In rare circumstances, such individuals may take advantage of temporary market imbalances and launch new enterprises that start out small but evolve into corporate giants. One particularly prominent example is the establishment of the Microsoft Corporation in 1975, now the forty-second largest corporation in the world (Forbes 2012a).


Concentrated Investing by Allen C. Benello

activist fund / activist shareholder / activist investor, asset allocation, barriers to entry, beat the dealer, Benoit Mandelbrot, Bob Noyce, Boeing 747, book value, business cycle, buy and hold, carried interest, Claude Shannon: information theory, corporate governance, corporate raider, delta neutral, discounted cash flows, diversification, diversified portfolio, Dutch auction, Edward Thorp, family office, fixed income, Henry Singleton, high net worth, index fund, John Bogle, John von Neumann, junk bonds, Louis Bachelier, margin call, merger arbitrage, Paul Samuelson, performance metric, prudent man rule, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, survivorship bias, technology bubble, Teledyne, transaction costs, zero-sum game

In the mid‐1960s, he started giving talks at MIT on the subject of scientific investing.22 Scientific investing did not mean technical analysis. Shannon had tinkered with technical charting in the early 1960s, but had rejected it, describing the price charts used by technicians as “a very noisy reproduction of the important data.”23 Rather, Shannon lectured on statistical methods for profiting from a stock’s random walk. One such method was what Poundstone named Shannon’s Demon. The idea was to form a portfolio of equal parts cash and a stock, and rebalance regularly to take advantage of the stock’s randomly jittering price movements. Shannon’s Demon worked as follows: Let’s say we begin with a portfolio of $10,000: $5,000 will be held in cash, and $5,000 will be invested in a stock at noon.

., 143 Norwegian Cruise Line, 146–151 Noyce, Robert, 161–162 O Oak Value Capital Management, 18 Odegard, Jan Tore, 132–133 Oian, Anne, 147 Olsen, Fred, 132, 135, 136–137 Olsen Group, 132, 136 opposed risks, Keynes on, 37, 43 O’Shaughnessy, Patrick, 103–104, 211 230 P permanent capital risk and permanent impairment of capital, 63–64 Siem on, 154–155, 155–156 temperament of investors and, 2, 208–209, 211–214 Peters, Betty, 117 Phillips Petroleum, 138, 139 Pidgeon, Larry, 166 Pigou, Arthur, 40 Portfolio Problem, The (Shannon), 74 positive free cash flow, 18–19 Poundstone, William, 72–73, 77, 78, 80–81 P.R. Finance Company, 44–45 price-to-book value, 101 price to earnings ratio earnings yield compared to, 49 Simpson on, 19 Princeton-Newport Partners, 83 Provincial Insurance Company, 38, 60, 206, 212 R random walk convertible arbitrage, 83–87 Shannon’s Demon, 79–83, 80 rebalancing, of uncorrelated assets, 80 Reebok, 23 Ringdal, 134–135 RJ Reynolds, 163 Roberts, Brian, 177 Robertson, Julian, 144 Index Rosenfield, Joe, 8–9, 159–169, 172 Ross, Arthur, 184–186, 190 Ruane, William J., 165 S Saks Fifth Avenue, 180, 181 Salomon Brothers, 25 Samuelson, Paul, 84–85 Sanborn Map, 89–93, 105 Schloss, Walter, 120–121 scientific investing, 79 Scott, Francis C., 39, 45, 49, 60, 206, 212 scuttlebutt method, 116, 215 Securities and Exchange Commission (SEC), 117–118 Security Analysis (Graham), 37, 38, 45 See, Harry, 114–115 See’s Candies, 113–118, 126, 127, 195, 214–215 Selfridge, John, 72 Sequoia Fund, 162, 165, 168, 172 Shannon, Claude, 73–74, 77, 78–83 Shannon’s Demon, 79–83, 80 Shapiro, John, 177–178, 185, 189 Shareholders Management, 10–11 Shaw Communications, 194–196 Shiller, Robert J., 38 shorting, Keynes on, 41 Siem, Ivan, 136 Siem, Kristian, 131–158 DSND Subsea and, 151–153 offshore drilling by, 131–138, 143–146 shipping and cruise lines, 138– 142, 146–151 231 Index temperament for investing by, 205–206, 213, 214, 215–216 on valuation, 153–155, 155–156 Siem Industries, 145–151, 155–156 Simpson, Lou, 5–33 “conservative, concentrated” investment approach of, 25–29, 194 early biographical information, 9–11 GEICO investment decisions by, 11–15, 18–29 GEICO results of, 15–18, 17 Grinnell and, 166 hired as GEICO chief investment officer, 5–9 investment philosophy of, 18–25 overview of portfolio concentration used by, 1, 4 temperament for investing by, 123–124, 204–205, 213, 214, 215–216 Singleton, Henry, 78 $64,000 Question, The (television show), 74, 76–77 Smith, E.

Edward Thorp and Applied Kelly Theory In November 1969, Thorp founded what he believed to be the world’s first market‐neutral hedge fund, Convertible Hedge Associates (renamed Â�Princeton‐Newport Partners in 1974).33 The fund used warrants, over‐the‐ counter (OTC) options, convertible bonds, preferred stock, and common stock to construct a delta‐neutral portfolio—one that was unaffected by changes in the underlying common stock. Like Shannon, he too sought to exploit the random walk phenomenon, not through Shannon’s Demon, but through convertible arbitrage. Thorp’s strategy, though it was considerably more sophisticated, shared with Shannon’s Demon the use of constant rebalancing to eke out tiny profits from changes in the prices of matched securities as they reverted to the mean. In his 1967 book Beat The Market, his follow-up to Beat the Dealer, Thorp also described his investment process as a “a scientific stock market system” perhaps as a nod to Shannon’s scientific investing lectures.


pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

Remarkably, the first man to expound and use these statistical tools, several years before they were publically formulated by Markov, was Albert Einstein. In 1905, calculating the hidden behavior of molecules in Brownian motion, he showed that they occupied a chain of states that jiggled at a rate of around two gigahertz following a “random walk,” as in Markov’s concept. Showing the movements of atoms without seeing or measuring them, Einstein translated from what is now termed a Markov sequence of observable states of a gas to his proof of the then-still-hidden Brownian motion of the molecules. Markov kept his head down during the Russian Revolution while working on his theory.

Treating the Web as a Markov chain enables Google’s search engine to gauge the probability that a particular Web page satisfies your search.5 To construct his uncanny search engine, Larry Page paradoxically began with the Markovian assumption that no one is actually searching for anything. His “random surfer” concept makes Markov central to the Google era. PageRank treats the Internet user as if he were taking a random walk across the Web, which we users know is not what we are doing. Since a random surfer would tend to visit the best-connected sites most frequently, his hypothetical itinerary defines the importance and authority of sites. Because PageRank is a manageably simple model that requires no knowledge about surfers or websites, it enables Markov math quickly and constantly to calculate their rankings across the galactic topography of the Internet.

This is consistent with what we know about statistics: they predict group behavior without accounting for individual decisions or free will. A defining property of a Markov chain is that it is memoryless. The history is assumed to be summed up by the current state and not by any past history of the chain. This feature greatly simplifies the computational process. Following a Markov model, a browser pursues a “random walk” of transitions from one position to another, bouncing off “reflecting states” (unwanted sites), moving through “transitional states” (Utah, Nevada), stopping at “absorbing states” (Google Mountain View headquarters!), all without needing to factor in intentionality or plan. Hierarchical hidden Markov models enable multiple levels of abstraction, from phonemes up a neural network tree to words and phrases and meanings and models of reality.


pages: 483 words: 141,836

Red-Blooded Risk: The Secret History of Wall Street by Aaron Brown, Eric Kim

Abraham Wald, activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, Bear Stearns, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, book value, business cycle, capital asset pricing model, carbon tax, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, currency risk, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, fail fast, fear index, financial engineering, financial innovation, global macro, illegal immigration, implied volatility, independent contractor, index fund, John Bogle, junk bonds, Long Term Capital Management, loss aversion, low interest rates, managed futures, margin call, market clearing, market fundamentalism, market microstructure, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, natural language processing, open economy, Pierre-Simon Laplace, power law, pre–internet, proprietary trading, quantitative trading / quantitative finance, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, stock buybacks, stocks for the long run, tail risk, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

Its only risks now are that there might be some problem with the futures clearinghouse or some mismatch between the Treasuries it holds and the Treasuries deliverable under the futures contracts. Otherwise, it does not care if Treasury prices go up or down, or even if the U.S. government defaults. We always knew there were some risks to this kind of leverage, but they seemed much smaller than the risks you eliminated by hedging. We learned that was not necessarily true. In a severe credit crunch and liquidity crisis, even good leverage, the kind that offsets your risks, could kill. The next step is to think like a frequentist. What things did other people learn that were really just fluctuations in a random walk? U.S. Treasury bonds did great during the crisis, but that might not happen next time.

You do this many times to generate a distribution of possible future outcomes. Simple resampling works only when the data are independent—that is, when yesterday’s move doesn’t tell you anything about today’s. Another name for a series with independent changes is a random walk, which of course is one of the famous models in finance. I believed, however, that financial time series were typically not random walks. One kind of common deviation from a random walk is called autocorrelation. That means yesterday’s move tells you something about today’s move—up days are followed by other up days either more (positive autocorrelation) or less (negative autocorrelation) than half the time.

But with all its money and power, and all its willingness to inflict excruciating pain by raising rates 500 basis points during a recession, the British Treasury still had no chance. Similar mistakes have been repeated many times since by governments trying to fight the financial markets. Instead of treating the exchange rate as a random walk, the British Treasury should have considered it as a bet. In order to win the contest, it had to be able to create a credible scenario in which speculators would have been hurt if they had failed. In the circumstances that may not have been possible, in which case the Treasury could have saved a lot of money and pride by bowing to the inevitable.


pages: 447 words: 104,258

Mathematics of the Financial Markets: Financial Instruments and Derivatives Modelling, Valuation and Risk Issues by Alain Ruttiens

algorithmic trading, asset allocation, asset-backed security, backtesting, banking crisis, Black Swan, Black-Scholes formula, Bob Litterman, book value, Brownian motion, capital asset pricing model, collateralized debt obligation, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, delta neutral, discounted cash flows, discrete time, diversification, financial engineering, fixed income, implied volatility, interest rate derivative, interest rate swap, low interest rates, managed futures, margin call, market microstructure, martingale, p-value, passive investing, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk/return, Satyajit Das, seminal paper, Sharpe ratio, short selling, statistical model, stochastic process, stochastic volatility, time value of money, transaction costs, value at risk, volatility smile, Wiener process, yield curve, zero-coupon bond

MGARCH see multivariate GARCH process mixed data sampling (MIDAS) process mixed jump diffusion model model risk modified duration (MD) modified VaR (MVaR) moments CAPM money markets moneyness Monte Carlo simulation accuracy exotic options fractional Brownian motion jump processes sensitivities simulation examples VaR Moody’s rating agency mortgage-backed securities Morton see Heath, Jarrow and Morton model moving average (MA) process moving averages ARIMA process ARMA process MA process MSCI Barra MtM see marked to market multivariate GARCH (MGARCH) process MVaR see modified VaR NASDAQ index NDFs see non-deliverable forwards NDOs see non-delivery options neural networks (NNs) “no arbitrage” condition non-deliverable forwards (NDFs) non-delivery options (NDOs) non-financial commodity futures non-linear models non-path dependent options non-stationary processes normal distribution Norwegian krone (NOK) OECD see Organisation for Economic Co-operation and Development offer price Ohrstein–Uhlenbeck processes OIS see overnight index swaps Omega ratio “open” prices option pricing Black–Scholes formula CRR model exotic options finite difference methods implied volatility jump processes Merton model Monte Carlo simulations sensitivities valuation troubles volatility see also prices/pricing options bond duration credit derivative valuation option contract value pricing see also exotic options Organisation for Economic Co-operation and Development (OECD) out of the money (OTM) caps options outright forward operation overnight index swaps (OIS) parametric method, VaR Parkinson volatility participating forward contracts (PFCs) path-dependent options payer swaps percent per annum performance absolute measures attribution Calmar ratio contribution global example IR Jensen’s alpha market MDD non-normal returns Omega ratio relative measures risk measures Sharpe ratio Sortino ratio stocks portfolios swaps TE Treynor ratio Z-score PFCs see participating forward contracts platykurtic distributions POF see Proportion of failures test Poisson processes polynomial curve methods portfolios bond duration bond selection immunization performance attribution contribution Portfolio Theory risk management Portfolio Theory APT model CAPM equities hypotheses Markowitz model performance risk and return valuation troubles “position risk” concept present value (PV) bond duration CRSs IRSs short-term rates spot rates zero-coupon swaps price of risk, CAPM prices/pricing APT model bid/ask bonds CAPM caps CBs CDOs CRSs floors futures high/low IRSs “open”/“close” second-generation swaps spot instruments swaptions see also market prices; option pricing price of time, CAPM price-weighted indexes pricing sensitivities see sensitivities probability risk neutral see also stochastic processes Proportion of failures (POF) test putable bonds put options call-put parity see also options PV see present value quanto swaps randomness random numbers random walks RaV see Risk at Value realized volatility models real option method receiver swaps recovery rates reference currency (ref) regime-switching models regression, NNs relative VaR return measures expected return performance Portfolio Theory in practice risk vs return ratios several stock positions single stock positions time periods returns general Wiener process instantaneous measures “reverse cash and carry” operations rho risk see individual types Risk at Value (RaV) “risk-free” bonds risk-free yield curve risk management risk measures performance attribution contribution Portfolio Theory return measures risk vs return ratios several positions single position VaR risk neutral probability risk premium, CAPM “risky” bonds Rogers–Satchell volatility Roll, R.

This can be explained by economic factors: a company is supposed to re-invest all or part (in case of dividend distribution) of its profits, and thus grow over time, and stock prices must also follow inflation over the long run. Of course, on a shorter horizon of time, prices may decline, even during periods lasting several consecutive years. So that, equity and index options pricing models clearly fit with the random walk hypothesis (although not necessarily strictly Gaussian). Currency prices do not present any global trend over time: a currency is priced relatively to another currency, and economic as well as speculative hazards comfort the random walk hypothesis. But over time, interest rates show the peculiar behavior of successive rising and falling phases.

The above relationship looks like a linear regression, but instead of regressing according to a series of independent variables, this regression uses previous values of the dependent variable itself, hence the “autoregression” name. 9.2 THE MOVING AVERAGE (MA) PROCESS Let us consider a series of returns consisting in pure so-called “random numbers” {t}, i.i.d., generally distributed following a normal distribution. These t are generated such as E[t]= 0 V[t] = σ2 cov[t, t′] = 0, that is, the t are mutually independent. An MA(1) process is defined as where b is a constant. rt is a “random walk” built from the successive random numbers. Generalizing, a MA(q) process, involving the q previous values of the series, is defined by It results that E[rt] = 0 V[rt] = σ2Σbk2 (since the t are independent) for |t − t′| ≤ q, cov[rt, rt′] = σ2Σbtbt′ and = 0 for |t − t′| > q. This process is stationary, given E[.] is constant and cov[.] is independent of t.


pages: 471 words: 97,152

Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism by George A. Akerlof, Robert J. Shiller

affirmative action, Andrei Shleifer, asset-backed security, bank run, banking crisis, Bear Stearns, behavioural economics, business cycle, buy and hold, collateralized debt obligation, conceptual framework, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, Deng Xiaoping, Donald Trump, Edward Glaeser, en.wikipedia.org, experimental subject, financial innovation, full employment, Future Shock, George Akerlof, George Santayana, housing crisis, Hyman Minsky, income per capita, inflation targeting, invisible hand, Isaac Newton, Jane Jacobs, Jean Tirole, job satisfaction, Joseph Schumpeter, junk bonds, Long Term Capital Management, loss aversion, market bubble, market clearing, mental accounting, Michael Milken, Mikhail Gorbachev, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, Myron Scholes, new economy, New Urbanism, Paul Samuelson, Phillips curve, plutocrats, Post-Keynesian economics, price stability, profit maximization, public intellectual, purchasing power parity, random walk, Richard Thaler, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, seminal paper, South Sea Bubble, The Chicago School, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, tulip mania, W. E. B. Du Bois, We are all Keynesians now, working-age population, Y2K, Yom Kippur War

See also financial prices price-to-earnings-to-price feedback, 135 price-to-GDP-to-price feedback, 154 price-to-price feedback, 134–35, 154 Primary Credit Dealer Facility, 187n10 Princeton University, 19 prohibition (of alcohol), 39 Project Link, 16 Pullman Palace Car Company, 63 Purdue University, 128 Quetzalcóatl (López Portillo), 53–54 Quintini, Glenda, 183n14 quits, wages and, 103–4, 106 railroad strike of 1910 (Argentina), 139 Rainwater, Lee, 162, 196n14 Rajan, Raghuram G., 182n21 Randers, Jørgen, 194n29 randomness, 52 random-walk hypothesis, 103, 191n11 ratings of securities, 37, 91, 94, 170 rational expectations, xxiii, 5, 6, 168, 173, 178n4; bimetallism debate and, 60; in classical economics, 2, 3; confidence and, 12–13, 14; corruption and, 39; fairness and, 21, 22; feedback and, 140; financial prices and, 131, 132, 133, 136; money illusion and, 41, 42; real estate market and, 150, 153; saving and, 120, 122 Reagan, Ronald, xxv, 32, 36, 172, 175 real business cycle models, 178n6 real estate market, 4, 6, 135, 136, 149–56, 169–70, 172, 174, 195–96n1–15; baby boom and, 152; confidence and, 11, 13, 149, 156; confidence multiplier in, 153–55; naïve or intuitive beliefs about, 150–53; S&L crisis and, 32, 33.

For a broader discussion of these issues see Thaler (1994). 10. Keynes (1973 [1936], p. 96). 11. Hall (1978) found some apparently striking evidence in favor of this maximizing model in showing that a time series of aggregate U.S. consumption was approximately a random walk. However, subsequent evidence has generated other interpretations (Blinder et al. 1985; Hall 1988). Carroll and Summers (1991) found evidence against the random-walk hypothesis in that individual consumption tends to track predictable life-cycle changes in income, though Carroll (2001) backtracked a bit on their conclusions. Shea (1995a) found evidence that individual consumption changes can be forecast using data on future incomes implicit in union contracts. 12.

If people tend to buy in reaction to stock price increases or sell in reaction to price decreases, then their reaction to past price changes has the potential to feed back into more price changes in the same direction, a phenomenon known as price-to-price feedback.10 A vicious circle can develop, causing a continuation of the cycle, at least for a while. Eventually an upward price movement, a bubble, must burst, since price is supported only by expectations of further price increases. They cannot go on forever. Price-to-price feedback itself may not be strong enough to create the major asset price bubbles we have seen.


pages: 289 words: 95,046

Chaos Kings: How Wall Street Traders Make Billions in the New Age of Crisis by Scott Patterson

"World Economic Forum" Davos, 2021 United States Capitol attack, 4chan, Alan Greenspan, Albert Einstein, asset allocation, backtesting, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, bitcoin, Bitcoin "FTX", Black Lives Matter, Black Monday: stock market crash in 1987, Black Swan, Black Swan Protection Protocol, Black-Scholes formula, blockchain, Bob Litterman, Boris Johnson, Brownian motion, butterfly effect, carbon footprint, carbon tax, Carl Icahn, centre right, clean tech, clean water, collapse of Lehman Brothers, Colonization of Mars, commodity super cycle, complexity theory, contact tracing, coronavirus, correlation does not imply causation, COVID-19, Credit Default Swap, cryptocurrency, Daniel Kahneman / Amos Tversky, decarbonisation, disinformation, diversification, Donald Trump, Doomsday Clock, Edward Lloyd's coffeehouse, effective altruism, Elliott wave, Elon Musk, energy transition, Eugene Fama: efficient market hypothesis, Extinction Rebellion, fear index, financial engineering, fixed income, Flash crash, Gail Bradbrook, George Floyd, global pandemic, global supply chain, Gordon Gekko, Greenspan put, Greta Thunberg, hindsight bias, index fund, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, Jeffrey Epstein, Joan Didion, John von Neumann, junk bonds, Just-in-time delivery, lockdown, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, Mark Spitznagel, Mark Zuckerberg, market fundamentalism, mass immigration, megacity, Mikhail Gorbachev, Mohammed Bouazizi, money market fund, moral hazard, Murray Gell-Mann, Nick Bostrom, off-the-grid, panic early, Pershing Square Capital Management, Peter Singer: altruism, Ponzi scheme, power law, precautionary principle, prediction markets, proprietary trading, public intellectual, QAnon, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Nader, Ralph Nelson Elliott, random walk, Renaissance Technologies, rewilding, Richard Thaler, risk/return, road to serfdom, Ronald Reagan, Ronald Reagan: Tear down this wall, Rory Sutherland, Rupert Read, Sam Bankman-Fried, Silicon Valley, six sigma, smart contracts, social distancing, sovereign wealth fund, statistical arbitrage, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, systematic trading, tail risk, technoutopianism, The Chicago School, The Great Moderation, the scientific method, too big to fail, transaction costs, University of East Anglia, value at risk, Vanguard fund, We are as Gods, Whole Earth Catalog

For investors, this means never trying to time the market because it’s impossible to predict whether it will go up or down over any meaningful time period. You can’t know with any confidence whether the next flip of the coin is going to be heads or tails. It’s always fifty-fifty. The random walk was the other side of the efficient markets coin that had frustrated Brandon Yarckin during his class at Duke. Since the market is always instantly incorporating all known information into prices, its next move is a coin flip, entirely impossible to predict. Sornette agreed that, most of the time, the random walk theory holds for markets. But there are times, he said, when it is possible to predict what will occur. The most important time: when it’s in a bubble.

After entering Duke University at seventeen, having already amassed a full year of credits from advanced-placement classes in high school, he decided to major in economics—and immediately decided the central tenets of modern economic theory were bullshit. “The first lecture was about efficient markets,” he recalled, referring to the theory that the prices for all markets immediately reflect all available information, hence making them thoroughly efficient. The theory is a corollary of the random-walk hypothesis in which the future of the market is unknowable, a random flip of the coin. “The professor spent an entire class talking about the market being efficient. I raised my hand with skeptical questions. It seemed stupid to me.

The phenomenon, which had parallels in Mandelbrot’s fractals, was something he said was bigger than standard power laws—it was a super-power law marked by dizzyingly fast up-and-down oscillations. The French physicist was claiming to have unearthed a phantom. A phenomenon that, according to prevailing economic and financial theory, couldn’t exist. The market, according to this theory, behaves like a random walk. It was the theory first proposed in 1900 by Bachelier, the neurotic French mathematician described by Benoit Mandelbrot at NYU. Sometimes called a drunkard’s walk, the theory claims that markets—all markets—are completely random and therefore unpredictable. Imagine a drunk staggering away from a light pole.


pages: 117 words: 31,221

Fred Schwed's Where Are the Customers' Yachts?: A Modern-Day Interpretation of an Investment Classic by Leo Gough

Albert Einstein, banking crisis, Bernie Madoff, book value, corporate governance, discounted cash flows, disinformation, diversification, fixed income, index fund, John Bogle, junk bonds, Long Term Capital Management, Michael Milken, Northern Rock, passive investing, Ralph Waldo Emerson, random walk, short selling, South Sea Bubble, The Nature of the Firm, the rule of 72, The Wealth of Nations by Adam Smith, transaction costs, young professional

L. 44 mergers and acquisitions (M&As) 88–9 middle age 99 Milken, Michael 73 millionaires, behaviour of 46 Minogue, Kenneth 24 misinformation 72–3, 90, 91 Modern Portfolio Theory 49 modernity and globalisation 78–9 momentum 38–9 mutual funds 86 average returns 105 N Nabisco 89 ‘Names’ 45 new issues 56–7 newsletters 96–7 O Ogilvy, David 88 online brokers 71 ‘open-end’ funds 86 optimism 42–3 ‘options’ 32 OTC (Over The Counter) market 70 overseas markets and diversification 49 P passive investment 111 patterns, identifying 40–1 Patton, George 80 pension schemes 99 Pope, John 104 popular investments 30–1 positive news about companies 42–3 predicting by analysts 97 the market 40–1 returns 80–1 small changes 38–9 ‘present value’ 94 price/book ratio 92 price/earnings (P/E) ratio 55 price/equity to growth (PEG) ratio 93 price/sales ratio 93 probability 26–7 professional stock-pickers 20–1 professionals 96, 98 commissions 72 and investment skills 47, 54–5 risk aversion 58 profit and company size 105 hiding 91 prospectuses 35, 56–7, 87 public overspending 28–9 R Random Walk theory 27 ratio price/book 92 price/earnings (P/E) 55 price/equity to growth (PEG) 93 price/sales 93 reading about stock markets 112–13 ‘regression to the mean’ 105 regulators 84–5 retirement 99 planning for 60–1 returns and diversification 49 estimating 80–1 inflation-adjusted 103 on investments 58–9 long term 64–5 risk adjustment 80–1 and hedge funds 100–1 in large investments 44–5 perception of 108 relative 50–2 in short selling 82–3 in speculation 12–13 and variance 81 Rogers, Jim 36, 77 Rogers, Will 58 Rowe, David 38 Royal Mail 66–7 ‘rule of 72’ 103 rumours, affecting the market 83 Russia, government bonds 51 S Sarbanes-Oxley Act (2002) 85 Schwed, Fred, background 8–9 SEC (Securities and Exchange Commission) 14, 84 Seneca 46 Seven (bank) 41 Shakespeare, William 86 share analysts 37 shares popular 30–1 prices 14–15 releasing 25 short sellers 82–3 short-term fluctuations in share prices 38–9 short-term investing 63 size of company and profit 105 Soros, George 77, 101 South Sea Bubble 56, 109 speculation 12–13 spread betting companies 83 stages of life, planning for 98–9 start-up businesses 106–7 Steinherr, Alfred 20, 33 stock indices 34–5 stock markets share prices 14–15 speculation in 12–13 stock-picking 20–1 stockbrokers 71 T Taiwan 87 takeovers 88–9 tax on trust funds 58–9 technical analysis (TA) 40–1, 112 telecoms companies 108 Thinc Group 84 timing of investments 64–5 tracker funds 21, 62–3 tracking error 63 ‘traction’ 38–9 traders, investment skills 47 see also professionals transaction costs 70–1 trusts 35, 48–9 low returns 58–9 turnarounds 66–7 Twain, Mark 12, 14 U ‘unit trusts’ 86 US, government bonds 80 V Vanderbilt family 16 variance 81 volatility in the Far East 76–7 and risk 51–2, 58 W Wall Street crash 82, 104 Walsh, David 73 wealth passing down through generations 16–17 and relative risk 44–5 and skill in investment 46–7 Wells, H.

In the short-term, according to many statisticians, share prices tend to approximate this level of randomness. There is really no good reason why a price goes up a tick one minute and down a tick the next minute. There is even a theory, called the Random Walk theory, that attempts to explain much of how share prices change in these terms. Over longer periods, though, share price movements look a lot less random. Companies that are doing well in the real world, for instance, tend to enjoy substantial rises in their share price as investors become willing to pay higher prices for the shares. The great question is whether the price of a share is fair, a bargain, or expensive.

On large stock exchanges, like London and New York, millions of shares exchange hands every day, and the share prices are always changing (usually by only a tiny fraction). But in order to sell your shares, there has to be a buyer, and if a government stepped in to force prices up artificially, there would come a point when there would be no buyers: the price would be too high. People would start looking for other ways to invest and billions would drain out of the market and go overseas to other stock exchanges. To function properly, a stock market has to allow prices to fall as well as rise. That’s what investors really want. If Megaboom Plc loses billions and comes near to bankruptcy, you would want to see those losses reflected in its share price, wouldn’t you?


pages: 999 words: 194,942

Clojure Programming by Chas Emerick, Brian Carper, Christophe Grand

Amazon Web Services, Benoit Mandelbrot, cloud computing, cognitive load, continuous integration, database schema, domain-specific language, don't repeat yourself, drop ship, duck typing, en.wikipedia.org, failed state, finite state, Firefox, functional programming, game design, general-purpose programming language, Guido van Rossum, higher-order functions, Larry Wall, mandelbrot fractal, no silver bullet, Paul Graham, platform as a service, premature optimization, random walk, Ruby on Rails, Schrödinger's Cat, semantic web, software as a service, sorting algorithm, SQL injection, Turing complete, type inference, web application

However, we can easily recreate index-step with stepper as well, assuming w and h are globally or locally bound to the width and height of the desired finite grid: (stepper #(filter (fn [[i j]] (and (< -1 i w) (< -1 j h))) (neighbours %)) #{2 3} #{3}) Maze generation Let’s study another example: Wilson’s maze generation algorithm.[112] Wilson’s algorithm is a carving algorithm; it takes a fully walled “maze” and carves an actual maze out of it by removing some walls. Its principle is: Randomly pick a location and mark it as visited. Randomly pick a location that isn’t visited yet—if there’s none, return the maze. Perform a random walk starting from the newly picked location until you stumble on a location that is visited—if you pass through a location more than once during the random walk, always remember the direction you take to leave it. Mark all the locations of the random walk as visited, and remove walls according to the last known “exit direction.” Repeat from 2. Generally, maze algorithms use a matrix to represent the maze, and each item of this matrix is a bitset indicating which walls are still up.

If visited locations had been used instead of unvisited, (seq unvisited) would need to be replaced by (seq (remove visited (keys paths))). (iterate (comp rand-nth paths) loc) generates an infinite random walk: it takes a location, applies paths on it to get the vector of adjacent locations and rand-nth to pick one. If paths had returned sets instead of a sequential type (like a vector), then (comp rand-nth seq paths) would have been necessary instead. (take-while unvisited walk) is the part of the random walk until (but not including) a visited location. (take-while unvisited walk) would be (take-while (complement visited) walk) if the code had been written with visited.

(next walk) is infinite, but (take-while unvisited walk) is not, so zipmap only looks at the n first items of (next walk) (where n is (count (take-while unvisited walk))). The n first items of (next walk) is thus the random walk without the start location and including the first visited location. Since the two sequences are shifted by one, each key-value pair is going to be a direction. Creating a map out of these pairs will only retain the most recent direction for a given key, and therefore the last exit direction. Entries of the resulting map are the last exit directions for each location of the random walk. (map set steps) turn the directions (entries) into walls (sets) that we remove from the maze. To test this nice implementation, we need two utility functions: grid, which creates a fully walled maze, and draw, which renders the maze (in this case, to a Swing JFrame): (defn grid [w h] (set (concat (for [i (range (dec w)) j (range h)] #{[i j] [(inc i) j]}) (for [i (range w) j (range (dec h))] #{[i j] [i (inc j)]})))) (defn draw [w h maze] (doto (javax.swing.JFrame.


pages: 589 words: 69,193

Mastering Pandas by Femi Anthony

Amazon Web Services, Bayesian statistics, correlation coefficient, correlation does not imply causation, data science, Debian, en.wikipedia.org, Internet of things, Large Hadron Collider, natural language processing, p-value, power law, random walk, side project, sparse data, statistical model, Thomas Bayes

A memoryless random variable exhibits the property whereby its future state depends only on relevant information about the current time and not the information from further in the past. An example of modeling a Markovian/memoryless random variable is modeling short-term stock price behavior and the idea that it follows a random walk. This leads to what is called the Efficient Market hypothesis in Finance. For more information, refer to http://en.wikipedia.org/wiki/Random_walk_hypothesis. The PDF of the exponential distribution is given by =. The expectation and variance are given by the following expression: For a reference, refer to the link at http://en.wikipedia.org/wiki/Exponential_distribution.

probability density function (PDF) / Continuous probability distributions probability distributionsabout / Probability distributions probability mass function (pmf)about / Discrete probability distributions PYMC Pandas ExampleURL / IPython Notebook PyPI Readline packageURL / Windows Pythonabout / How Python and pandas fit into the data analytics mix features / How Python and pandas fit into the data analytics mix URL / How Python and pandas fit into the data analytics mix, Selecting a version of Python to use, Installing Python from compressed tarball libraries / How Python and pandas fit into the data analytics mix version, selecting / Selecting a version of Python to use installation, on Linux / Linux installation, on Windows / Core Python installation installation, on Mac OS/X / Mac OS X Anaconda package, URL / Installation of Python and pandas from a third-party vendor Python(x,y)URL / Other numeric or analytics-focused Python distributions Python 3.0URL / Selecting a version of Python to use references / Selecting a version of Python to use Python decoratorsreference link / pandas/util Python dictionary, DataFrame objectsDataFrame.to_panel method, using / Using the DataFrame.to_panel method DataFrame.to_panel method, references / Using the DataFrame.to_panel method other operations / Other operations Python extensionsused, for improving performance / Improving performance using Python extensions Python installation, on Linuxabout / Linux from compressed tarball / Installing Python from compressed tarball Python installation, on Mac OS/Xabout / Mac OS X URL / Mac OS X package manager, using / Installation using a package manager Python installation, on Windowsabout / Windows core Python installation / Core Python installation third-party software install / Third-party Python software installation URL / Third-party Python software installation Python Lexical AnalysisURL / Accessing attributes using dot operator Q quartileabout / Quartile reference link / Quartile R Rdata types / R data types column name, specifying in / Specifying column name in R multiple columns, selecting in / Multicolumn selection in R %in% operator / R %in% operator logical subsetting / Logical subsetting in R split-apply-combine, implementing in / Implementation in R melt() function / The R melt() function cut() method / An R example using cut() R, and pandasmatching operators, comparing in / Comparing matching operators in R and pandas R-matrixversus Numpy array / R-matrix and NumPy array compared random forest / Random forest random walk hypothesisreference link / The exponential distribution range / Range R DataFramesabout / R DataFrames versus pandas DataFrames / R's DataFrames versus pandas' DataFrames README file, scikit-learnreference link / Installing on Windows R listsabout / R lists versus pandas series / R lists and pandas series compared role of pandas, in machine learning / Role of pandas in machine learning S sample covariancereference link / The mean sample meanreference link / The mean scikit-learnabout / Role of pandas in machine learning installing / Installation of scikit-learn installing, via Anacondas / Installing via Anaconda installing, on Unix (Linux/Mac OSX) / Installing on Unix (Linux/Mac OS X) installing, on Windows / Installing on Windows reference link / Installing on Windows model. constructing for / Constructing a model using Patsy for scikit-learn scikit-learn ML/classifier interfaceabout / The scikit-learn ML/classifier interface reference link / The scikit-learn ML/classifier interface scipy.stats functionreference link / Quartile Scipy Lecture Notes, Interfacing with Creference link / Improving performance using Python extensions Seriescreating / Series creation creating, with numpy.ndarray / Using numpy.ndarray creating, with Python dictionary / Using Python dictionary creating, with scalar values / Using scalar values operations / Operations on Series Series operationsassignment / Assignment slicing / Slicing arithmetic and statistical operations / Other operations Setuptoolsabout / Third-party Python software installation URL / Third-party Python software installation shape manipulation, NumPy arrayabout / Array shape manipulation multi-dimensional array, flattening / Flattening a multi-dimensional array reshaping / Reshaping resizing / Resizing dimension, adding / Adding a dimension shifting / Shifting/lagging single rowappending, to DataFrame / Appending a single row to a DataFrame sortlevel() method / MultiIndexing sparse.pyreference link / pandas/core split-apply-combineabout / Split-apply-combine implementing, in R / Implementation in R implementing, in pandas / Implementation in pandas SQL-like merging/joining, of DataFrame objects / SQL-like merging/joining of DataFrame objects SQL joinsreference link / SQL-like merging/joining of DataFrame objects stack() functionabout / The stack() function stackingabout / Stacking and unstacking statistical hypothesis testsabout / Statistical hypothesis tests background / Background z-test / The z-test t-test / The t-test structured array, DataFrameURL / Using a structured array submodules, pandas/compatchainmap.py / pandas/compat chainmap_impl.py / pandas/compat pickle_compat.py / pandas/compat openpyxl_compat.py / pandas/compat submodules, pandas/computationapi.py / pandas/computation align.py / pandas/computation common.py / pandas/computation engines.py / pandas/computation eval.py / pandas/computation expressions.py / pandas/computation ops.py / pandas/computation pytables.py / pandas/computation scope.py / pandas/computation submodules, pandas/coreapi.py / pandas/core array.py / pandas/core base.py / pandas/core common.py / pandas/core config.py / pandas/core datetools.py / pandas/core frame.py / pandas/core generic.py / pandas/core categorical.py / pandas/core format.py / pandas/core groupby.py / pandas/core ops.py / pandas/core index.py / pandas/core internals.py / pandas/core matrix.py / pandas/core nanops.py / pandas/core panel.py / pandas/core panel4d.py / pandas/core panelnd.py / pandas/core series.py / pandas/core sparse.py / pandas/core strings.py / pandas/core submodules, pandas/ioapi.py / pandas/io auth.py / pandas/io common.py / pandas/io data.py / pandas/io date_converters.py / pandas/io excel.py / pandas/io ga.py / pandas/io gbq.py / pandas/io html.py / pandas/io json.py / pandas/io packer.py / pandas/io parsers.py / pandas/io pickle.py / pandas/io pytables.py / pandas/io sql.py / pandas/io to_sql(..) / pandas/io stata.py / pandas/io wb.py / pandas/io submodules, pandas/rpybase.py / pandas/rpy common.py / pandas/rpy mass.py / pandas/rpy var.py / pandas/rpy submodules, pandas/sparseapi.py / pandas/sparse array.py / pandas/sparse frame.py / pandas/sparse list.py / pandas/sparse panel.py / pandas/sparse series.py / pandas/sparse submodules, pandas/statsapi.py / pandas/stats common.py / pandas/stats fama_macbeth.py / pandas/stats interface.py / pandas/stats math.py / pandas/stats misc.py / pandas/stats moments.py / pandas/stats ols.py / pandas/stats plm.py / pandas/stats var.py / pandas/stats submodules, pandas/toolsutil.py / pandas/tools tile.py / pandas/tools rplot.py / pandas/tools plotting.py / pandas/tools pivot.py / pandas/tools merge.py / pandas/tools describe.py / pandas/tools submodules, pandas/tseriesapi.py / pandas/tseries converter.py / pandas/tseries frequencies.py / pandas/tseries holiday.py / pandas/tseries index.py / pandas/tseries interval.py / pandas/tseries offsets.py / pandas/tseries period.py / pandas/tseries plotting.py / pandas/tseries resample.py / pandas/tseries timedeltas.py / pandas/tseries tools.py / pandas/tseries util.py / pandas/tseries submodules, pandas/utilterminal.py / pandas/util print_versions.py / pandas/util misc.py / pandas/util decorators.py / pandas/util clipboard.py / pandas/util supervised learningversus unsupervised learning / Supervised versus unsupervised learning about / Supervised learning supervised learning algorithmsabout / Supervised learning algorithms model, constructing for scikit-learn with Patsy / Constructing a model using Patsy for scikit-learn general boilerplate code explanation / General boilerplate code explanation logistic regression / Logistic regression support vector machine (SVM) / Support vector machine decision trees / Decision trees random forest / Random forest supervised learning problemsclassification / Supervised versus unsupervised learning regression / Supervised versus unsupervised learning support vector machine (SVM) / Support vector machineURL / Support vector machine swaplevel function / Swapping and reordering levels SWIG Documentationreference link / Improving performance using Python extensions switchpoint detection, Bayesian analysis example / Bayesian analysis example – Switchpoint detection T t-distributionreference link / The t-test t-testabout / The t-test one sample independent t-test / Types of t-tests independent samples t-tests / Types of t-tests paired samples t-test / Types of t-tests reference link / Types of t-tests example / A t-test example tailed testreference link / Statistical hypothesis tests time-series-related instance methodsabout / Time series-related instance methods shifting/lagging / Shifting/lagging frequency conversion / Frequency conversion data, resampling / Resampling of data aliases, for Time Series frequencies / Aliases for Time Series frequencies Time-Series-related objectsdatetime.datetime / A summary of Time Series-related objects Timestamp / A summary of Time Series-related objects DatetimeIndex / A summary of Time Series-related objects Period / A summary of Time Series-related objects PeriodIndex / A summary of Time Series-related objects DateOffset / A summary of Time Series-related objects timedelta / A summary of Time Series-related objects TimeDelta object / DateOffset and TimeDelta objects time serieshandling / Handling time series TimeSeries.resample functionabout / Resampling of data Time series conceptsabout / Time series concepts and datatypes time series datareading in / Reading in time series data TimeDelta object / DateOffset and TimeDelta objects DateOffset object / DateOffset and TimeDelta objects Time series datatypesabout / Time series concepts and datatypes Period / Period and PeriodIndex PeriodIndex / PeriodIndex Time Series datatypesconversion between / Conversions between Time Series datatypes time series datatypesPeriodIndex / PeriodIndex Time Series frequenciesaliases / Aliases for Time Series frequencies Titanic problemnaïve approach / A naïve approach to Titanic problem transform() method / The transform() method Type I Error / Type I and Type II errors Type II Error / Type I and Type II errors U UEFA Champions LeagueURL / The groupby operation unbiased estimatorreference link / Deviation and variance Unix (Linux/Mac OSX)scikit-learn, installing on / Installing on Unix (Linux/Mac OS X) unstackingabout / Stacking and unstacking unsupervised learningversus supervised learning / Supervised versus unsupervised learning about / Unsupervised learning unsupervised learning algorithmsabout / Unsupervised learning algorithms dimensionality reduction / Dimensionality reduction K-means clustering / K-means clustering upsamplingabout / Resampling of data V 4V’s of big dataabout / 4 V's of big data, Veracity of big data volume / Volume of big data velocity / Velocity of big data variety / Variety of big data veracity / Veracity of big data varianceabout / Deviation and variance variety, big data / Variety of big data vector auto-regression classes, var.pyVAR / pandas/stats PanelVAR / pandas/stats vector autoregressionreference link / pandas/stats velocity, big data / Velocity of big data veracity, big data / Veracity of big data virtualenv toolabout / Virtualenv installing / Virtualenv installation and usage using / Virtualenv installation and usage URL / Virtualenv installation and usage volume, big data / Volume of big data W Wakariabout / Wakari by Continuum Analytics URL / Wakari by Continuum Analytics where() method / Using the where() method WindowsPython, installing / Windows, Core Python installation Anaconda installation / Windows panda installation / Windows IPython installation / Windows scikit-learn, installing on / Installing on Windows WinPythonURL / Other numeric or analytics-focused Python distributions World Bank Economic dataURL / Benefits of using pandas X xs method / Cross sections Z z-testabout / The z-test zettabytesURL / Volume of big data

Multicolumn selection in R In R, we specify the multiple columns to select by stating them in a vector within square brackets: >stocks_table[c('Symbol','Price')] Symbol Price 1 GOOG 518.70 2 AMZN 307.82 3 FB 74.90 4 AAPL 109.70 5 TWTR 37.10 6 NFLX 334.48 7 LINKD 219.90 >stocks_table[,c('Symbol','Price')] Symbol Price 1 GOOG 518.70 2 AMZN 307.82 3 FB 74.90 4 AAPL 109.70 5 TWTR 37.10 6 NFLX 334.48 7 LINKD 219.90 Multicolumn selection in pandas In pandas, we subset elements in the usual way with the column names in square brackets: In [140]: stocks_df[['Symbol','Price']] Out[140]:Symbol Price 0 GOOG 518.70 1 AMZN 307.82 2 FB 74.90 3 AAPL 109.70 4 TWTR 37.10 5 NFLX 334.48 6 LNKD 219.90 In [145]: stocks_df.loc[:,['Symbol','Price']] Out[145]: Symbol Price 0 GOOG 518.70 1 AMZN 307.82 2 FB 74.90 3 AAPL 109.70 4 TWTR 37.10 5 NFLX 334.48 6 LNKD 219.90 Arithmetic operations on columns In R and pandas, we can apply arithmetic operations in data columns in a similar manner.


pages: 402 words: 110,972

Nerds on Wall Street: Math, Machines and Wired Markets by David J. Leinweber

"World Economic Forum" Davos, AI winter, Alan Greenspan, algorithmic trading, AOL-Time Warner, Apollo 11, asset allocation, banking crisis, barriers to entry, Bear Stearns, Big bang: deregulation of the City of London, Bob Litterman, book value, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, Charles Babbage, citizen journalism, collateralized debt obligation, Cornelius Vanderbilt, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, electricity market, Emanuel Derman, en.wikipedia.org, experimental economics, fake news, financial engineering, financial innovation, fixed income, Ford Model T, Gordon Gekko, Hans Moravec, Herman Kahn, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, Ivan Sutherland, Jim Simons, John Bogle, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, military-industrial complex, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, Savings and loan crisis, semantic web, Sharpe ratio, short selling, short squeeze, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, stock buybacks, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, tontine, too big to fail, transaction costs, Turing machine, two and twenty, Upton Sinclair, value at risk, value engineering, Vernor Vinge, Wayback Machine, yield curve, Yogi Berra, your tax dollars at work

The retranslation of the Russian back to English this time was “The spirit is of willing of but of the flesh is of weak.” 31. The CIA In-Q-Tel venture capitalists are found here: www.inqtel.org/. Part Two Alpha as Life 90 Nerds on Wall Str eet I ndex funds are passive investments; their goal is to deliver a return that matches a benchmark index. The Old Testament of indexing is Burton Malkiel’s classic A Random Walk Down Wall Street, first published in 1973 by W.W. Norton and now in its ninth edition. For typical individual investors, without special access to information, it offers what is likely the best financial advice they will ever get: It is hard to consistently beat the market, especially after fees.

Institutional investors began to use firms like A.G. Becker to actually compare the total performance of their hired managers with index benchmarks, and found that many of them fell short, especially after the substantial fees the investors were paying. Yale professor Burton Malkiel popularized the academic efficient market arguments in A Random Walk Down Wall Street, writing in 1973, “[We need] a new investment instrument: a no-load, minimummanagement-fee mutual fund that simply buys the hundreds of stocks making up the market averages and does no trading [of securities]. . . . Fund spokesmen are quick to point out, ‘you can’t buy the averages.’

Somehow, he managed to work in a story involving his Perils and Pr omise of Evolutionary Computation on Wall Str eet 187 Lithuanian grandmother’s recipe for chicken soup, which began, “First, steal a chicken.” There was no Lithuanian chicken soup at that GECCO, but there were some amazing demonstrations of learning programs. Robot control strategies started out as random walks, and after a few hundred simulated generations, they were moving like R2D2 on a good day. There were novel circuit, network, and even protein designs produced by artificial genetic methods.2 The financial guys, many of whom I recognized from more Wall Street–oriented events, and I were trolling for ideas, people to hire, and software to take home.


pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backpropagation, backtesting, barriers to entry, behavioural economics, book value, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, data science, deep learning, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial intermediation, Flash crash, Geoffrey Hinton, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, low interest rates, machine readable, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, Performance of Mutual Funds in the Period, popular capitalism, prediction markets, price discovery process, profit motive, proprietary trading, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

The EMH gained prominence in the 1960s, and empirical studies of prices and of asset manager performance since then have lent credence to the idea that the market is efficient enough to make it impossible to determine whether top asset managers’ performance is due to anything but luck. The theory also implies that looking for exploitable patterns in prices, and in other forms of publicly available data, will not lead to strategies in which investors can have confidence, from a statistical perspective. An implication of the EMH is that prices will evolve in a process indistinguishable from a random walk. However, another branch of financial economics has sought to disprove the EMH. Behavioral economics studies market imperfections resulting from investor psychological traits or cognitive biases.

Therefore, if they lose against the informed traders (the first terms in equations 1 and 2 below) and win against the uninformed traders (the second terms in equations 1 and 2 below), their profits are a vH 0.5 1 a v 0, (1) vL 0.5 1 v b 0. (2) b The difference between the ask and bid prices, or the spread (S), is then given by S vH 1 0.5 1 vL 1 0.5 1 . (3) Moreover, by further simplifying the model with the assumption that 0.5 , the spread prices follow a random walk at the intraday level becomes a linear function of the probability of informed trading (π): S vH vL . (4) Intraday Data in Alpha Research213 The importance of Glosten and Milgrom’s results lies in the illiquidity premium principle discussed above, stating that higher spreads increase the expected returns.

Shaw & Co. 8 design 25–30 automated searches 111–120 backtesting 33–41 case study 31–41 core concepts 3–6 data inputs 4, 25–26, 43–47 evaluation 28–29 expressions 4 flow chart 41 future performance 29–30 horizons 4–50 intraday alphas 219–221 machine learning 121–126 noise reduction 26 optimization 29–30 prediction frequency 27 quality 5 risk-on/risk off alphas 246–247 robustness 89–93 smoothing 54–55, 59–60 triple-axis plan 83–88 universe 26 value 27–30 digital filters 127–128 digitization 7–9 dimensionality 129–132 disclosures 192 distressed assets 202–203 diversification automated searches 118–119 exchange-traded funds 233 portfolios 83–88, 108 DL see deep learning dot (inner) product 63–64 Dow, Charles 7 DPIN see dynamic measure of the probability of informed trading drawdowns 106–107 dual timestamping 78 dynamic measure of the probability of informed trading (DPIN) 214–215 dynamic parameterization 132 early-exercise premium 174 earnings calls 181, 187–188 earnings estimates 184–185 earnings surprises 185–186 efficiency, automated searches 111–113 Index295 efficient markets hypothesis (EMH) 11, 135 ego 19 elegance of models 75 EMH see efficient markets hypothesis emotions 19 ensemble methods 124–125 ensemble performance 117–118 estimation of risk 102–106 historical 103–106 position-based 102–103 shrinkage 131 ETFs see exchange-traded funds Euclidean space 64–66 evaluation 13–14, 28–29 backtesting 13–14, 33–41, 69–76 bias 77–82 bootstrapping 107 correlation 28–29 cutting losses 20–21 data selection 74–75 drawdowns 107 information ratio 28 margin 28 overfitting 72–75 risk 101–110 robustness 89–93 turnover 49–60 see also validation event-driven strategies 195–205 business cycle 196 capital structure arbitrage 204–205 distressed assets 202–203 index-rebalancing arbitrage 203–204 mergers 196–199 spin-offs, split-offs & carve-outs 200–202 exchange-traded funds (ETFs) 223–240 average daily trading volume 239 challenges 239–240 merits 232–233 momentum alphas 235–237 opportunities 235–238 research 231–240 risks 233–235 seasonality 237–238 see also index alphas exit costs 19, 21 expectedness of news 164 exponential moving averages 54 expressions, simple 4 extreme alpha values 104 extrinsic risk 101, 106, 108–109 factor risk heterogeneity 234 factors financial statements 147 to alphas 148 failure modes 84 fair disclosures 192 fair value of futures 223 Fama–French three-factor model 96 familiarity bias 81 feature extraction 130–131 filters 127–128 finance blogs 181–182 finance portals 180–181, 192 financial statement analysis 141–154 balance sheets 143 basics 142 cash flow statements 144– 145, 150–152 corporate governance 146 factors 147–148 fundamental analysis 149–154 growth 145–146 income statements 144 negative factors 146–147 special considerations 147 finite impulse response (FIR) filters 127–128 296Index FIR filters see finite impulse response filters Fisher Transform 91 five-day reversion alpha 55–59 Float Boost 125 forecasting behavioral economics 11–12 computer adoption 7–9 frequencies 27 horizons 49–50 statistical arbitrage 10–11 UnRule 17–21 see also predictions formation of the industry 8–9 formulation bias 80 forward-looking bias 72 forwards 241–249 checklist 243–244 Commitments of Traders report 244–245 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 frequencies 27 full text analysis 164 fundamental analysis 149–154 future performance 29–30 futures 241–249 checklist 243–244 Commitments of Traders report 244–245 fair value 223 instrument groupings 242–243 seasonality 245–246 underlying assets 241–242 fuzzy logic 126 General Electric 200 generalized correlation 64–66 groupings, futures and forwards 242–243 group momentum 157–158 growth analysis 145–146 habits, successful 265–271 hard neutralization 108 headlines 164 hedge fund betas see risk factors hedge funds, initial 8–9 hedging 108–109 herding 81–82, 190–191 high-pass filters 128 historical risk measures 103–106 horizons 49–50 horizontal mergers 197 Huber loss function 129 humps 54 hypotheses 4 ideas 85–86 identity matrices 65 IIR filters see infinite impulse response filters illiquidity premium 208–211 implementation core concepts 12–13 triple-axis plan 86–88 inaccuracy of models 10–11 income statements 144 index alphas 223–240 index changes 225–228 new entrants 227–228 principles 223–225 value distortion 228–230 see also exchange-traded funds index-rebalancing arbitrage 203–204 industry formation 8–9 industry-specific factors 188–190 infinite impulse response (IIR) filters 127–128 information ratio (IR) 28, 35–36, 74–75 initial hedge funds 8–9 inner product see dot product inputs, for design 25–26 integer effect 138 intermediate variables 115 Index297 intraday data 207–216 expected returns 211–215 illiquidity premium 208–211 market microstructures 208 probability of informed trading 213–215 intraday trading 217–222 alpha design 219–221 liquidity 218–219 vs. daily trading 218–219 intrinsic risk 102–103, 105–106, 109 invariance 89 inverse exchange-traded funds 234 IR see information ratio iterative searches 115 Jensen’s alpha 3 L1 norm 128–129 L2 norm 128–129 latency 46–47, 128, 155–156 lead-lag effects 158 length of testing 75 Level 1/2 tick data 46 leverage 14–15 leveraged exchange-traded funds 234 limiting methods 92–93 liquidity effect 96 intraday data 208–211 intraday trading 218–219 and spreads 51 literature, as a data source 44 look-ahead bias 78–79 lookback days, WebSim 257–258 looking back see backtesting Lo’s hypothesis 97 losses cutting 17–21, 109 drawdowns 106–107 loss functions 128–129 low-pass filters 128 M&A see mergers and acquisitions MAC clause see material adverse change clause MACD see moving average convergence-divergence machine learning 121–126 deep learning 125–126 ensemble methods 124–125 fuzzy logic 126 look-ahead bias 79 neural networks 124 statistical models 123 supervised/unsupervised 122 support vector machines (SVM) 122, 123–124 macroeconomic correlations 153 manual searches, pre-automation 119 margin 28 market commentary sites 181–182 market effects index changes 225–228 see also price changes market microstructure 207–216 expected returns 211–215 illiquidity premium 208–211 probability of informed trading 213–215 types of 208 material adverse change (MAC) clause 198–199 max drawdown 35 max stock weight, WebSim 257 mean-reversion rule 70 mean-squared error minimization 11 media 159–167 academic research 160 categorization 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 298Index sentiment 160–161 social 165–166 mergers and acquisitions (M&A) 196–199 models backtesting 69–76 elegance 75 inaccuracy of 10–11 see also algorithms; design; evaluation; machine learning; optimization momentum alphas 155–158, 165, 235–237 momentum effect 96 momentum-reversion 136–137 morning sunshine 46 moving average convergencedivergence (MACD) 136 multiple hypothesistesting 13, 20–21 narrow framing 81 natural gas reserves 246 negative factors, financial statements 146–147 neocognitron models 126 neural networks (NNs) 124 neutralization 108 WebSim 257 newly indexed companies 227–228 news 159–167 academic research 160 categories 163 expectedness 164 finance information 181–182, 192 momentum 165 novelty 161–162 relevance 162 sentiment 160–161 volatility 164–165 NNs see neural networks noise automated searches 113 differentiation 72–75 reduction 26 nonlinear transformations 64–66 normal distribution, approximation to 91 novelty of news 161–162 open interest 177–178 opportunities 14–15 optimization 29–30 automated searches 112, 115–116 loss functions 128–129 of parameter 131–132 options 169–178 concepts 169 open interest 177–178 popularity 170 trading volume 174–177 volatility skew 171–173 volatility spread 174 option to stock volume ratio (O/S) 174–177 order-driven markets 208 ordering methods 90–92 O/S see option to stock volume ratio outliers 13, 54, 92–93 out-of-sample testing 13, 74 overfitting 72–75 data mining 79–80 reduction 74–75, 269–270 overnight-0 alphas 219–221 overnight-1 alphas 219 parameter minimization 75 parameter optimization 131–132 PCA see principal component analysis Pearson correlation coefficients 62–64, 90 peer pressure 156 percent profitable days 35 performance parameters 85–86 Index299 PH see probability of heuristicdriven trading PIN see probability of informed trading PnL see profit and loss pools see portfolios Popper, Karl 17 popularity of options 170 portfolios correlation 61–62, 66 diversification 83–88, 108 position-based risk measures 102–103 positive bias 190 predictions 4 frequency 27 horizons 49–50 see also forecasting price changes analyst reports 190 behavioral economics 11–12 efficient markets hypothesis 11 expressions 4 index changes 225–228 news effects 159–167 relative 12–13, 26 price targets 184 price-volume strategies 135–139 pride 19 principal component analysis (PCA) 130–131 probability of heuristic-driven trading (PH) 214 probability of informed trading (PIN) 213–215 profit and loss (PnL) correlation 61–62 drawdowns 106–107 see also losses profit per dollar traded 35 programming languages 12 psychological factors see behavioral economics put-call parity relation 174 Python 12 quality 5 quantiles approximation 91 quintile distributions 104–105 quote-driven markets 208 random forest algorithm 124–125 random walks 11 ranking 90 RBM see restricted Boltzmann machine real estate investment trusts (REITs) 227 recommendations by analysts 182–183 recurrent neural networks (RNNs) 125 reduction of dimensionality 130–131 of noise 26 of overfitting 74–75, 269–270 of risk 108–109 Reg FD see Regulation Fair Disclosure region, WebSim 256 regions 85–86 regression models 10–11 regression problems 121 regularization 129 Regulation Fair Disclosure (Reg FD) 192 REITs see real estate investment trusts relationship models 26 relative prices 12–13, 26 relevance, of news 162 Renaissance Technologies 8 research 7–15 analyst reports 179–193 automated searches 111–120 backtesting 13–14 300Index behavioral economics 11–12 computer adoption 7–9 evaluation 13–14 exchange-traded funds 231–240 implementation 12–13 intraday data 207–216 machine learning 121–126 opportunities 14–15 perspectives 7–15 statistical arbitrage 10–11 triple-axis plan 83–88 restricted Boltzmann machine (RBM) 125 Reuleaux triangle 70 reversion alphas, five-day 55–59 risk 101–110 arbitrage 196–199 control 108–109 drawdowns 106–107 estimation 102–106 extrinsic 101, 106, 108–109 intrinsic 102–103, 105–106, 109 risk factors 26, 95–100 risk-on/risk off alphas 246–247 risk-reward matrix 267–268 RNNs see recurrent neural networks robustness 89–93, 103–106 rules 17–18 evaluation 20–21 see also algorithms; UnRule Russell 2000 IWM fund 225–226 SAD see seasonal affective disorder scale of automated searches 111–113 search engines, analyst reports 180–181 search spaces, automated searches 114–116 seasonality exchange-traded funds 237–238 futures and forwards 245–246 momentum strategies 157 and sunshine 46 selection bias 77–79, 117–118 sell-side analysts 179–180 see also analyst reports sensitivity tests 119 sentiment analysis 160–161, 188 shareholder’s equity 151 Sharpe ratios 71, 73, 74–75, 221, 260 annualized 97 Shaw, David 8 shrinkage estimators 131 signals analysts report 190, 191–192 cutting losses 20–21 data sources 25–26 definition 73 earnings calls 187–188 expressions 4 noise reduction 26, 72–75 options trading volume 174–177 smoothing 54–55, 59–60 volatility skew 171–173 volatility spread 174 sign correlation 65 significance tests 119 Simons, James 8 simple moving averages 55 simulation backtesting 71–72 WebSim settings 256–258 see also backtesting size factor 96 smoothing 54–55, 59–60 social media 165–166 sources of data 25–26, 43–44, 74–75 automated searches 113–114 see also data sparse principal component analysis (sPCA) 131 Spearman’s rank correlation 90 Index301 special considerations, financial statements 147 spin-offs 200–202 split-offs 200–202 spreads and liquidity 51 and volatility 51–52 stat arb see statistical arbitrage statistical arbitrage (stat arb) 10–11, 69–70 statistical models, machine learning 123 step-by-step construction 5, 41 storage costs 247–248 storytelling 80 subjectivity 17 sunshine 46 supervised machine learning 122 support vector machines (SVM) 122, 123–124 systemic bias 77–80 TAP see triple-axis plan tax efficiency, exchange-traded funds 233 teams 270–271 temporal-based correlation 63–64, 65 theory-fitting 80 thought processes of analysts 186–187 tick data 46 timestamping and bias 78–79 tracking errors 233–234 trades cost of 50–52 crossing effect 52–53 latency 46–47 trend following 18 trimming 92 triple-axis plan (TAP) 83–88 concepts 83–86 implementation 86–88 tuning of turnover 59–60 see also smoothing turnover 49–60 backtesting 35 control 53–55, 59–60 costs 50–52 crossing 52–53 examples 55–59 horizons 49–50 smoothing 54–55, 59–60 WebSim 260 uncertainty 17–18 underlying principles 72–73 changes in 109 understanding data 46 unexpected news 164 universes 26, 85–86, 239–240, 256 UnRule 17–18, 20–21 unsupervised machine learning 122 validation, data 45–46 valuation methodologies 189 value of alphas 27–30 value distortion, indices 228–230 value factors 96 value investing 96, 141 variance and bias 129–130 vendors as a data source 44 vertical mergers 197 volatility and news 164–165 and spreads 51–52 volatility skew 171–173 volatility spread 174 volume of options trading 174–177 price-volume strategies 135–139 volume-synchronized probability of informed trading (VPIN) 215 302Index VPIN see volume-synchronized probability of informed trading weather effects 46 WebSim 253–261 analysis 258–260 backtesting 33–41 data types 255 example 260–261 settings 256–258 weekly goals 266–267 weighted moving averages 55 Winsorization 92–93 Yahoo finance 180 Z-scoring 92


Stock Market Wizards: Interviews With America's Top Stock Traders by Jack D. Schwager

Asian financial crisis, banking crisis, barriers to entry, Bear Stearns, beat the dealer, Black-Scholes formula, book value, commodity trading advisor, computer vision, East Village, Edward Thorp, financial engineering, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John Meriwether, John von Neumann, junk bonds, locking in a profit, Long Term Capital Management, managed futures, margin call, Market Wizards by Jack D. Schwager, money market fund, Myron Scholes, paper trading, passive investing, pattern recognition, proprietary trading, random walk, risk free rate, risk tolerance, risk-adjusted returns, short selling, short squeeze, Silicon Valley, statistical arbitrage, Teledyne, the scientific method, transaction costs, Y2K

JOHN BENDER tion in the formula is that the probabilities of prices being at different levels at the time of the option expiration can be described by a normal curve*—the highest probabilities being for prices that are close to the current level and the probabilities for any price decreasing the further above or below the market it is.] A normal distribution would be appropriate if stock price movements were analogous to what is commonly called "the drunkard's walk." If you have a drunkard in a narrow corridor, and all he can do is lurch forward or backward, in order for his movements to be considered a random walk, the following criteria would have to be met: 1.

All my trading operates on the premise that the most important part is the part that Black-Scholes left out—the assumption of the probability distribution. Why do you say with such assurance that stock prices don't even come close to a random walk? As one example, whether you believe in it or not, there is such a thing as technical analysis, which tries to define support and resistance levels and trends. Regardless of whether technical analysis has any validity, enough people believe in it to impact the market. For example, if people expect a stock to find support at 65, lo and behold, they're willing to buy it at 66. That is not a random walk statement. *See note in final section of this chapter. Q U E S T I O N I N G THE OBVIOUS I'll give you another example.

—the tech funds. What stocks are they going to buy?—not airlines, they're tech funds. So the tech funds will go up even more. Therefore they're going to have better performance and get the next allocation, and so on. You have all the ingredients for a trend. Again, this is not price behavior that is consistent with a random walk assumption. You've seen this pattern increasingly in the recent run-up in the U.S. stock market. The rampant uptrend has been fueled by constant inflows into the same funds that are buying the same stocks, driving these stocks to values that are ridiculous by any historical valuation.


Think OCaml by Nicholas Monje, Allen Downey

en.wikipedia.org, Free Software Foundation, functional programming, higher-order functions, random walk

If you don’t understand what your program does, you can read it 100 times and never see the error, because the error is in your head. Running experiments can help, especially if you run small, simple tests. But if you run experiments without thinking or reading your code, you might fall into a pattern I call “random walk programming,” which is the process of making random changes until the program does the right thing. Needless to say, random walk programming can take a long time. You have to take time to think. Debugging is like an experimental science. You should have at least one hypothesis about what the problem is. If there are two or more possibilities, try to think of a test that would eliminate one of them.

Index abecedarian, 51 access, 56 accumulator histogram, 103 Ackerman function, 42 addition with carrying, 46 algorithm, 4, 9, 46, 107 Euclid, 43 RSA, 84 square root, 47 ambiguity, 6 anagram, 61 anagram set, 94 and operator, 32 Anonymous functions, 59 argument, 21, 23, 25, 29 arithmetic operator, 13 assignment, 18 tuple, 87–89, 94 assignment statement, 12 base case, 42 benchmarking, 109, 110 binary search, 61 bingo, 94 birthday paradox, 61 bisection search, 61 bisection, debugging by, 47 body, 23, 29, 35 boolean expression, 31, 35 borrowing, subtraction with, 46 bracket operator, 49 branch, 35 bug, 4, 9 calculator, 19 caml.inria.fr, 10 Car Talk, 85, 95 carrying, addition with, 46 case-sensitivity, variable names, 17 chained conditional, 33 char type, 11 character, 18, 49 comment, 17, 19 comparison string, 52 tuple, 89 compile, 1, 8 composition, 22, 25, 29 compound statement, 35 concatenation, 19, 26, 51 list, 56 condition, 35 conditional chained, 33 nested, 35 conditional execution, 32 conditional statement, 32, 35 cons operator, 55 consistency check, 84 counter, 53, 78 cummings, e. e., 4 Currying, 27 data structure, 94, 109 debugging, 4, 8, 9, 17, 28, 41, 53, 60, 84, 94, 109 by bisection, 47 emotional response, 8 experimental, 5 declaration, 85 default value, 106, 110 definition function, 22 recursive, 95 deterministic, 101, 110 development plan random walk programming, 110 diagram stack, 26 state, 12 dictionary lookup, 81 looping with, 80 reverse lookup, 81 Index Directive, 2 divisibility, 31 documentation, 10 dot notation, 29 Doyle, Arthur Conan, 5 Doyle, Sir Arthur Conan, 103 DSU pattern, 94 duplicate, 61, 85 element, 55, 60 emotional debugging, 8 empty list, 55 empty string, 53 encapsulation, 46 encryption, 84 epsilon, 46 error runtime, 4, 17, 39 semantic, 4, 12, 17 syntax, 4, 17 error message, 4, 8, 12, 17 escape character, 7 Euclid’s algorithm, 43 evaluate, 14 exception, 4, 9, 17 IndexError, 50 RuntimeError, 39 SyntaxError, 22 TypeError, 49 ValueError, 88 executable, 2, 9 experimental debugging, 5, 110 expression, 13, 14, 19 boolean, 31, 35 Fermat’s Last Theorem, 36 fibonacci function, 82 filter pattern, 60 find function, 51 flag, 85 float type, 11 floating-point, 18, 46 flow of execution, 24, 29 For loop, 70 for loop, 57 formal language, 5, 9 frame, 26 frequency, 79 letter, 94 word, 101, 111 function, 22, 28 113 ack, 42 fibonacci, 82 find, 51 log, 21 randint, 61 recursive, 37 sqrt, 22 String.length, 50 zip, 88 function argument, 25 function call, 21, 29 function definition, 22, 24, 29 function frame, 26 function parameter, 25 function, math, 21 function, reasons for, 28 function, trigonometric, 21 function, tuple as return value, 88 Functional Programming, 7 Functions Anonymous, 59 Currying, 27 gather, 94 GCD (greatest common divisor), 43 global variable, 85 greatest common divisor (GCD), 43 Guarded Patterns, 35 hash function, 85 hashtable, 77, 78, 84, 85 hashtbale subtraction, 106 header, 23, 29 Hello, World, 7 high-level language, 1, 8 Higher-Order Functions, 25 histogram, 79, 85 random choice, 102, 107 word frequencies, 102 HOF, 25 Holmes, Sherlock, 5 homophone, 86 if statement, 32 immutability, 53 implementation, 79, 85, 109 in, 15 index, 49, 53, 56, 77 starting at zero, 49 IndexError, 50 infinite recursion, 39, 42 114 int type, 11 integer, 18 long, 83 interactive mode, 2, 9 interlocking words, 61 interpret, 1, 8 invocation, 53 item, 53, 55 hashtable, 84 item update, 58 key, 77, 84 key-value pair, 77, 84 keyboard input, 33 keyword, 13, 19 labelled parameter, 104 language formal, 5 high-level, 1 low-level, 1 natural, 5 programming, 1 safe, 4 let, 15 letter frequency, 94 letter rotation, 53, 85 Linux, 5 list, 55, 60 concatenation, 56 element, 56 empty, 55 nested, 55 of tuples, 89 operation, 56 traversal, 57, 60 literalness, 6 local variable, 26, 29 log function, 21 logarithm, 111 logical operator, 31, 32 long integer, 83 lookup, 85 lookup, dictionary, 81 loop for, 57 Looping, 70 looping with dictionaries, 80 low-level language, 1, 8 map pattern, 60 Index mapping, 108 Markov analysis, 107 mash-up, 108 math function, 21 McCloskey, Robert, 51 membership binary search, 61 bisection search, 61 hashtable, 78 set, 78 memo, 82, 85 metathesis, 94 method, 53 string, 53 module, 7, 29 pprint, 84 random, 61, 102 string, 101 modulus operator, 31, 35 natural language, 5, 9 nested conditional, 33, 35 nested list, 55, 60 Newton’s method, 45 not operator, 32 number, random, 101 object code, 2, 9 operand, 13, 19 operator, 13, 19 and, 32 bracket, 49 cons, 55 logical, 31, 32 modulus, 31, 35 not, 32 or, 32 overloading, 16 relational, 32 string, 16 operator, arithmetic, 13 optional parameter, 104 or operator, 32 order of operations, 16, 18 override, 110 palindrome, 42 parameter, 25, 26, 29 labelled, 104 optional, 104 parentheses empty, 23 Index matching, 4 overriding precedence, 16 parameters in, 25 tuples in, 87 parse, 6, 9 Partial Application, 27 pattern filter, 60 map, 60 reduce, 60 search, 52, 53 swap, 87 Pattern Matching, 34 Pattern-Matching Guarded, 35 PEMDAS, 16 pi, 48 plain text, 101 poetry, 6 portability, 1, 8 pprint module, 84 precedence, 19 precondition, 61 prefix, 108 pretty print, 84 print statement, 7, 9 problem solving, 1, 8 program, 3, 9 Programming Functional, 7 programming language, 1 Programming Paradigms, 7 Functional, 7 Object-Oriented, 7 Project Gutenberg, 101 prompt, 2, 9, 34 prose, 6 pseudorandom, 101, 110 Puzzler, 85, 95 quotation mark, 7, 11 radian, 21 Ramanujan, Srinivasa, 48 randint function, 61 random function, 102 random module, 61, 102 random number, 101 random text, 108 random walk programming, 110 Read functions, 33 115 Recursion Tail-end, 40 recursion, 37, 42 infinite, 39 traversal, 50 recursive definition, 95 reduce pattern, 60 reducible word, 86, 95 redundancy, 6 References, 15, 69 relational operator, 32 return value, 21, 29 tuple, 88 reverse lookup, dictionary, 81 reverse lookup, hashtable, 85 reverse word pair, 61 rotation letters, 85 rotation, letter, 53 RSA algorithm, 84 rules of precedence, 16, 19 running pace, 19 runtime error, 4, 17, 39 RuntimeError, 39 safe language, 4 sanity check, 84 scaffolding, 84 scatter, 94 Scope, 15 scope, 15 Scrabble, 94 script, 2, 9 script mode, 2, 9 search pattern, 52, 53 search, binary, 61 search, bisection, 61 semantic error, 4, 9, 12, 17 semantics, 4, 9 sequence, 49, 53, 55, 87 set anagram, 94 set membership, 78 shape, 94 sine function, 21 slice, 53 source code, 2, 8 sqrt function, 22 square root, 45 stack diagram, 26 state diagram, 12, 18 116 statement, 18 assignment, 12 conditional, 32, 35 for, 57 if, 32 print, 7, 9 Strictly typed, 14 string, 11, 18 comparison, 52 operation, 16 string method, 53 String module, 50 string module, 101 string type, 11 String.length function, 50 structure, 6 subexpressions, 14 subtraction hashtable, 106 with borrowing, 46 suffix, 108 swap pattern, 87 syntax, 4, 9 syntax error, 4, 9, 17 SyntaxError, 22 Tail-end Recursion, 40 tail-end recursion, 42 testing interactive mode, 2 text plain, 101 random, 108 token, 6, 9 Toplevel, 2 toplevel, 9 traceback, 39, 41 traversal, 50, 52, 53, 80, 89, 103 list, 57 triangle, 36 trigonometric function, 21 tuple, 87, 88, 94 assignment, 87 comparison, 89 tuple assignment, 88, 89, 94 type, 11, 18 char, 11 float, 11 hashtable, 77 int, 11 list, 55 Index long, 83 str, 11 tuple, 87 unit, 12, 23 TypeError, 49 typographical error, 110 underscore character, 13 uniqueness, 61 unit type, 12, 18, 23 update histogram, 103 item, 58 use before def, 17 User input, 33 value, 11, 18, 85 default, 106 tuple, 88 ValueError, 88 variable, 12, 18 local, 26 Variables References, 15 While loop, 70 word frequency, 101, 111 word list, 78 word, reducible, 86, 95 words.txt, 78 zero, index starting at, 49 zip function, 88 Zipf’s law, 111

., 4 Currying, 27 data structure, 94, 109 debugging, 4, 8, 9, 17, 28, 41, 53, 60, 84, 94, 109 by bisection, 47 emotional response, 8 experimental, 5 declaration, 85 default value, 106, 110 definition function, 22 recursive, 95 deterministic, 101, 110 development plan random walk programming, 110 diagram stack, 26 state, 12 dictionary lookup, 81 looping with, 80 reverse lookup, 81 Index Directive, 2 divisibility, 31 documentation, 10 dot notation, 29 Doyle, Arthur Conan, 5 Doyle, Sir Arthur Conan, 103 DSU pattern, 94 duplicate, 61, 85 element, 55, 60 emotional debugging, 8 empty list, 55 empty string, 53 encapsulation, 46 encryption, 84 epsilon, 46 error runtime, 4, 17, 39 semantic, 4, 12, 17 syntax, 4, 17 error message, 4, 8, 12, 17 escape character, 7 Euclid’s algorithm, 43 evaluate, 14 exception, 4, 9, 17 IndexError, 50 RuntimeError, 39 SyntaxError, 22 TypeError, 49 ValueError, 88 executable, 2, 9 experimental debugging, 5, 110 expression, 13, 14, 19 boolean, 31, 35 Fermat’s Last Theorem, 36 fibonacci function, 82 filter pattern, 60 find function, 51 flag, 85 float type, 11 floating-point, 18, 46 flow of execution, 24, 29 For loop, 70 for loop, 57 formal language, 5, 9 frame, 26 frequency, 79 letter, 94 word, 101, 111 function, 22, 28 113 ack, 42 fibonacci, 82 find, 51 log, 21 randint, 61 recursive, 37 sqrt, 22 String.length, 50 zip, 88 function argument, 25 function call, 21, 29 function definition, 22, 24, 29 function frame, 26 function parameter, 25 function, math, 21 function, reasons for, 28 function, trigonometric, 21 function, tuple as return value, 88 Functional Programming, 7 Functions Anonymous, 59 Currying, 27 gather, 94 GCD (greatest common divisor), 43 global variable, 85 greatest common divisor (GCD), 43 Guarded Patterns, 35 hash function, 85 hashtable, 77, 78, 84, 85 hashtbale subtraction, 106 header, 23, 29 Hello, World, 7 high-level language, 1, 8 Higher-Order Functions, 25 histogram, 79, 85 random choice, 102, 107 word frequencies, 102 HOF, 25 Holmes, Sherlock, 5 homophone, 86 if statement, 32 immutability, 53 implementation, 79, 85, 109 in, 15 index, 49, 53, 56, 77 starting at zero, 49 IndexError, 50 infinite recursion, 39, 42 114 int type, 11 integer, 18 long, 83 interactive mode, 2, 9 interlocking words, 61 interpret, 1, 8 invocation, 53 item, 53, 55 hashtable, 84 item update, 58 key, 77, 84 key-value pair, 77, 84 keyboard input, 33 keyword, 13, 19 labelled parameter, 104 language formal, 5 high-level, 1 low-level, 1 natural, 5 programming, 1 safe, 4 let, 15 letter frequency, 94 letter rotation, 53, 85 Linux, 5 list, 55, 60 concatenation, 56 element, 56 empty, 55 nested, 55 of tuples, 89 operation, 56 traversal, 57, 60 literalness, 6 local variable, 26, 29 log function, 21 logarithm, 111 logical operator, 31, 32 long integer, 83 lookup, 85 lookup, dictionary, 81 loop for, 57 Looping, 70 looping with dictionaries, 80 low-level language, 1, 8 map pattern, 60 Index mapping, 108 Markov analysis, 107 mash-up, 108 math function, 21 McCloskey, Robert, 51 membership binary search, 61 bisection search, 61 hashtable, 78 set, 78 memo, 82, 85 metathesis, 94 method, 53 string, 53 module, 7, 29 pprint, 84 random, 61, 102 string, 101 modulus operator, 31, 35 natural language, 5, 9 nested conditional, 33, 35 nested list, 55, 60 Newton’s method, 45 not operator, 32 number, random, 101 object code, 2, 9 operand, 13, 19 operator, 13, 19 and, 32 bracket, 49 cons, 55 logical, 31, 32 modulus, 31, 35 not, 32 or, 32 overloading, 16 relational, 32 string, 16 operator, arithmetic, 13 optional parameter, 104 or operator, 32 order of operations, 16, 18 override, 110 palindrome, 42 parameter, 25, 26, 29 labelled, 104 optional, 104 parentheses empty, 23 Index matching, 4 overriding precedence, 16 parameters in, 25 tuples in, 87 parse, 6, 9 Partial Application, 27 pattern filter, 60 map, 60 reduce, 60 search, 52, 53 swap, 87 Pattern Matching, 34 Pattern-Matching Guarded, 35 PEMDAS, 16 pi, 48 plain text, 101 poetry, 6 portability, 1, 8 pprint module, 84 precedence, 19 precondition, 61 prefix, 108 pretty print, 84 print statement, 7, 9 problem solving, 1, 8 program, 3, 9 Programming Functional, 7 programming language, 1 Programming Paradigms, 7 Functional, 7 Object-Oriented, 7 Project Gutenberg, 101 prompt, 2, 9, 34 prose, 6 pseudorandom, 101, 110 Puzzler, 85, 95 quotation mark, 7, 11 radian, 21 Ramanujan, Srinivasa, 48 randint function, 61 random function, 102 random module, 61, 102 random number, 101 random text, 108 random walk programming, 110 Read functions, 33 115 Recursion Tail-end, 40 recursion, 37, 42 infinite, 39 traversal, 50 recursive definition, 95 reduce pattern, 60 reducible word, 86, 95 redundancy, 6 References, 15, 69 relational operator, 32 return value, 21, 29 tuple, 88 reverse lookup, dictionary, 81 reverse lookup, hashtable, 85 reverse word pair, 61 rotation letters, 85 rotation, letter, 53 RSA algorithm, 84 rules of precedence, 16, 19 running pace, 19 runtime error, 4, 17, 39 RuntimeError, 39 safe language, 4 sanity check, 84 scaffolding, 84 scatter, 94 Scope, 15 scope, 15 Scrabble, 94 script, 2, 9 script mode, 2, 9 search pattern, 52, 53 search, binary, 61 search, bisection, 61 semantic error, 4, 9, 12, 17 semantics, 4, 9 sequence, 49, 53, 55, 87 set anagram, 94 set membership, 78 shape, 94 sine function, 21 slice, 53 source code, 2, 8 sqrt function, 22 square root, 45 stack diagram, 26 state diagram, 12, 18 116 statement, 18 assignment, 12 conditional, 32, 35 for, 57 if, 32 print, 7, 9 Strictly typed, 14 string, 11, 18 comparison, 52 operation, 16 string method, 53 String module, 50 string module, 101 string type, 11 String.length function, 50 structure, 6 subexpressions, 14 subtraction hashtable, 106 with borrowing, 46 suffix, 108 swap pattern, 87 syntax, 4, 9 syntax error, 4, 9, 17 SyntaxError, 22 Tail-end Recursion, 40 tail-end recursion, 42 testing interactive mode, 2 text plain, 101 random, 108 token, 6, 9 Toplevel, 2 toplevel, 9 traceback, 39, 41 traversal, 50, 52, 53, 80, 89, 103 list, 57 triangle, 36 trigonometric function, 21 tuple, 87, 88, 94 assignment, 87 comparison, 89 tuple assignment, 88, 89, 94 type, 11, 18 char, 11 float, 11 hashtable, 77 int, 11 list, 55 Index long, 83 str, 11 tuple, 87 unit, 12, 23 TypeError, 49 typographical error, 110 underscore character, 13 uniqueness, 61 unit type, 12, 18, 23 update histogram, 103 item, 58 use before def, 17 User input, 33 value, 11, 18, 85 default, 106 tuple, 88 ValueError, 88 variable, 12, 18 local, 26 Variables References, 15 While loop, 70 word frequency, 101, 111 word list, 78 word, reducible, 86, 95 words.txt, 78 zero, index starting at, 49 zip function, 88 Zipf’s law, 111


pages: 478 words: 126,416

Other People's Money: Masters of the Universe or Servants of the People? by John Kay

Affordable Care Act / Obamacare, Alan Greenspan, asset-backed security, bank run, banking crisis, Basel III, Bear Stearns, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Monday: stock market crash in 1987, Black Swan, Bonfire of the Vanities, bonus culture, book value, Bretton Woods, buy and hold, call centre, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, Cornelius Vanderbilt, corporate governance, Credit Default Swap, cross-subsidies, currency risk, dematerialisation, disinformation, disruptive innovation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial innovation, financial intermediation, financial thriller, fixed income, Flash crash, forward guidance, Fractional reserve banking, full employment, George Akerlof, German hyperinflation, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Greenspan put, Growth in a Time of Debt, Ida Tarbell, income inequality, index fund, inflation targeting, information asymmetry, intangible asset, interest rate derivative, interest rate swap, invention of the wheel, Irish property bubble, Isaac Newton, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Jim Simons, John Meriwether, junk bonds, light touch regulation, London Whale, Long Term Capital Management, loose coupling, low cost airline, M-Pesa, market design, Mary Meeker, megaproject, Michael Milken, millennium bug, mittelstand, Money creation, money market fund, moral hazard, mortgage debt, Myron Scholes, NetJets, new economy, Nick Leeson, Northern Rock, obamacare, Occupy movement, offshore financial centre, oil shock, passive investing, Paul Samuelson, Paul Volcker talking about ATMs, peer-to-peer lending, performance metric, Peter Thiel, Piper Alpha, Ponzi scheme, price mechanism, proprietary trading, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, random walk, reality distortion field, regulatory arbitrage, Renaissance Technologies, rent control, risk free rate, risk tolerance, road to serfdom, Robert Shiller, Ronald Reagan, Schrödinger's Cat, seminal paper, shareholder value, Silicon Valley, Simon Kuznets, South Sea Bubble, sovereign wealth fund, Spread Networks laid a new fibre optics cable between New York and Chicago, Steve Jobs, Steve Wozniak, The Great Moderation, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tobin tax, too big to fail, transaction costs, tulip mania, Upton Sinclair, Vanguard fund, vertical integration, Washington Consensus, We are the 99%, Yom Kippur War

EMH asserts that all available information about securities is ‘in the price’. Interest rates are expected to rise, Procter and Gamble owns many powerful brands, the Chinese economy is growing rapidly: these factors are fully reflected in the current level of long-term interest rates, the Procter & Gamble stock price and the exchange rate between the dollar and the renminbi. Since everything that is already known is ‘in the price’, only things that are not already known can influence the price. In an efficient market prices will therefore follow what is picturesquely described as a ‘random walk’ – the next move is as likely to be up as down.

., 2013, ‘Statement on the Tourre Verdict’, US Securities and Exchange Commission Public Statement, 1 August. 18. Loewenstein, G., 1987, ‘Anticipation and the Value of Delayed Consumption’, Economic Journal, 97 (387), September, pp. 666–84. 19. There are many studies of this. See, for example, Malkiel, B. G., 2012, A Random Walk down Wall Street, 10th edn, New York and London, W.W. Norton. pp. 177–83. Porter, G.E., and Trifts, J.W., 2014, ‘The Career Paths of Mutual Fund Managers: The Role of Merit’, Financial Analysts Journal, 70 (4), July/August, pp. 55–71. Philips, C.B., Kinniry Jr, F.M., Schlanger, T., and Hirt, J.M., 2014, ‘The Case for Index-Fund Investing’, Vanguard Research, April, https://advisors.vanguard.com/VGApp/iip/site/advisor/researchcommentary/article/IWE_InvComCase4Index. 20.

., 1987, ‘Anticipation and the Value of Delayed Consumption’, Economic Journal, 97 (387), September. Lucas Jr, R.E., 2003, ‘Macroeconomic Priorities’, The American Economic Review, 93 (1), March, pp. 1–14. Macmillan, H., 1957, ‘Leader’s Speech’, remarks at Conservative Party rally, Bedford, 20 July. Malkiel, B.G., 2012, A Random Walk down Wall Street, 10th edn, New York and London, W.W. Norton. Manne, H.G., 1965, ‘Mergers and the Market for Corporate Control’, The Journal of Political Economy, 73 (2), April, pp. 110–20. Markopolos, H., 2010, No One Would Listen: A True Financial Thriller, Hoboken, NJ, Wiley. Martin, F., 2013, Money: The Unauthorised Biography, London, Bodley Head.


pages: 526 words: 144,019

A First-Class Catastrophe: The Road to Black Monday, the Worst Day in Wall Street History by Diana B. Henriques

Alan Greenspan, asset allocation, bank run, banking crisis, Bear Stearns, behavioural economics, Bernie Madoff, Black Monday: stock market crash in 1987, break the buck, buttonwood tree, buy and hold, buy low sell high, call centre, Carl Icahn, centralized clearinghouse, computerized trading, Cornelius Vanderbilt, corporate governance, corporate raider, Credit Default Swap, cuban missile crisis, Dennis Tito, Edward Thorp, Elliott wave, financial deregulation, financial engineering, financial innovation, Flash crash, friendly fire, Glass-Steagall Act, index arbitrage, index fund, intangible asset, interest rate swap, It's morning again in America, junk bonds, laissez-faire capitalism, locking in a profit, Long Term Capital Management, margin call, Michael Milken, money market fund, Myron Scholes, plutocrats, Ponzi scheme, pre–internet, price stability, proprietary trading, quantitative trading / quantitative finance, random walk, Ronald Reagan, Savings and loan crisis, short selling, Silicon Valley, stock buybacks, The Chicago School, The Myth of the Rational Market, the payments system, tulip mania, uptick rule, Vanguard fund, web of trust

In the 1950s, scholarly skeptics of this “stock-picking” concept had painstakingly collected stock prices going back decades; in the 1960s, they analyzed those prices on primitive computers and reached a shocking conclusion: random collections of stocks, chosen by literally throwing darts at the stock tables in the Wall Street Journal, generally outperformed handpicked portfolios. This so-called “random walk” method of investing was an idea that, in a few years, would radically transform the way giant institutions deployed their money in the market. Another group of “quant” scholars, loosely centered on the University of Chicago, looked at the same historical data about stock prices and reached a slightly different but equally world-changing conclusion about Wall Street.

That notion, too, would shape the financial landscape far into the future. In the San Francisco Bay area of the early 1970s, lightbulbs of financial innovation were burning bright everywhere, not just at Berkeley. A remarkable team of computer geeks and misfit bankers, working in a small unit at Wells Fargo Bank in San Francisco, had seized on the “random walk” concept and were trying to build portfolios for their pension fund clients that would behave as much as possible like the overall market. Their endeavor, called “indexing,” outraged the traditional stock-picking analysts at the bank—and helped nurture the giant institutional investors who would rise to prominence in the next decade.

Rosenberg,” SEC Administrative Proceeding File No. 3–14559, September 22, 2011.) The incident cannot diminish his influence on the melding of academic finance theories and Wall Street practice. an idea that, in a few years, would radically transform: This notion is usually connected with Princeton Professor Burton Malkiel’s classic, A Random Walk Down Wall Street, first published in 1973. It had invaded the academic literature as early as 1960. See Edward F. Renshaw and Paul J. Feldstein, “The Case for an Unmanaged Investment Company,” Financial Analysts Journal 16, no. 1 (January–February 1960), pp. 43–46. See also Michael J. Clowes, The Money Flood: How Pension Funds Revolutionized Investing (Hoboken, NJ: John Wiley and Sons, 2000), pp. 84–92 and 198–200; Kate Ancell, “The Origin of the First Index Fund,” University of Chicago Booth School of Business, 2012, http://www.crsp.com/files/SpringMagazine_IndexFund.pdf; and Fox, The Myth of the Rational Market, pp. 137–41.


pages: 420 words: 94,064

The Revolution That Wasn't: GameStop, Reddit, and the Fleecing of Small Investors by Spencer Jakab

4chan, activist fund / activist shareholder / activist investor, barriers to entry, behavioural economics, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Swan, book value, buy and hold, classic study, cloud computing, coronavirus, COVID-19, crowdsourcing, cryptocurrency, data science, deal flow, democratizing finance, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, Everybody Ought to Be Rich, fake news, family office, financial innovation, gamification, global macro, global pandemic, Google Glasses, Google Hangouts, Gordon Gekko, Hacker News, income inequality, index fund, invisible hand, Jeff Bezos, Jim Simons, John Bogle, lockdown, Long Term Capital Management, loss aversion, Marc Andreessen, margin call, Mark Zuckerberg, market bubble, Masayoshi Son, meme stock, Menlo Park, move fast and break things, Myron Scholes, PalmPilot, passive investing, payment for order flow, Pershing Square Capital Management, pets.com, plutocrats, profit maximization, profit motive, race to the bottom, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robinhood: mobile stock trading app, Saturday Night Live, short selling, short squeeze, Silicon Valley, Silicon Valley billionaire, SoftBank, Steve Jobs, TikTok, Tony Hsieh, trickle-down economics, Vanguard fund, Vision Fund, WeWork, zero-sum game

My team at The Wall Street Journal tried to illustrate this in a lighthearted way by tracking the picks and pans from the speakers at the closely watched Sohn Investment Conference like David Einhorn, Bill Ackman, and, yes, Gabe Plotkin and Chamath Palihapitiya. We took inspiration from Burton Malkiel’s classic A Random Walk Down Wall Street, in which he writes that “a blindfolded monkey throwing darts at a newspaper’s financial pages could select a portfolio that would do just as well as one carefully selected by the experts.”[2] Instead of paying $5,000 for a seat at the conference to hear the stars’ pearls of wisdom, I walked over to the Modell’s Sporting Goods store on Forty-Second Street and bought a set of darts for $9.99.

BACK TO NOTE REFERENCE 16 Berkshire Hathaway letter to shareholders, February 25, 2017, www.berkshirehathaway.com/letters/2016ltr.pdf. BACK TO NOTE REFERENCE 17 Bonus Round Federal Reserve Board 2019 Survey of Consumer Finances, accessed April 2021, www.federalreserve.gov/econres/scfindex.htm. BACK TO NOTE REFERENCE 1 Burton G. Malkiel, A Random Walk Down Wall Street: A Time-Tested Strategy for Successful Investing (New York: W. W. Norton, 2007), 24. BACK TO NOTE REFERENCE 2 Spencer Jakab, “Making Monkeys Out of the Sohn Investing Gurus,” The Wall Street Journal, May 6, 2019. BACK TO NOTE REFERENCE 3 CXO Advisory Group, “Guru Grades,” retrieved June 2, 2021, www.cxoadvisory.com/gurus.

., 179 Klarman, Seth, 184 Koss, 132, 169, 188, 224 Kruger, Justin, 28 Kynikos Associates, 77 L Ladies’ Home Journal, 150 Lamberton, Cait, 54, 62 Lamont, Owen, 80 Langer, Ellen, 27 Langlois, Shawn, 45 Laufer, Henry, 237 Lay, Kenneth, 85 Lebed, Jonathan, 163 Leder, Michelle, 239 Ledger, Heath, 138 Left, Andrew, 39, 116–26, 148, 191, 214, 217 GameStop and, 120–24, 129, 130, 133, 146 harassment of, 122 WallStreetBets and, 121–23, 126, 129, 130, 133, 136, 238 Lehman Brothers, 80, 117 Lending Tree, 162 Levie, Aaron, 26 Lewis, Michael, 16, 88 Lindzon, Howard, 24, 49, 176 LinkedIn, 239 Livermore, Jesse, 78–79 locating a borrow, 72–73, 80 Loeb, Dan, 111 Lombardi, Vince, 8 Long-Term Capital Management, 260 Loop Capital, 128 Los Angeles Times, 215 loss aversion, myopic, 236 lotteries, 62, 239, 241, 242 Lowenstein, Roger, 260 Lucid Motors, 164 M Mad Money, 254 Madoff, Bernie, 117, 206 MagnifyMoney, 162 Mahoney, Seth, 19, 31, 176–77 Malaysia, 75 Malkiel, Burton, 253 Manias, Panics, and Crashes (Kindleberger), 179 Manning, Peyton, 64 Man Who Solved the Market, The (Zuckerman), 237 Maplelane Capital, 217 March Madness, 57 Marcus, 257 margin calls, 203–5 margin debt, 58–59, 62, 67, 138, 188 Markets Insider, 103 MarketWatch, 45, 180 MassMutual, 87, 131, 171 Mavrck, 142 Mayday, 48–50, 66 McCabe, Caitlin, 128–29 McCormick, Packy, 23, 35, 104, 202 McDonald, Larry, 99 McDonald’s, 154 McHenry, Patrick, 239 McLean, Bethany, 85 Medallion Fund, 237 MedBox, 117 Melvin Capital Management, 6–8, 56, 72, 94–96, 110–12, 114, 119, 121, 123, 128–30, 132, 135, 136, 146, 189, 190, 202, 205, 217, 218, 222, 227 meme stocks, xii–xiv, 5, 7–9, 11, 12, 14, 22, 30, 32–34, 36, 39, 40, 47, 54, 63, 67, 72, 73, 76, 100, 108, 123, 125, 127, 129, 132–33, 135, 137–40, 146, 147, 153–55, 157, 159, 160, 162, 164, 169, 170, 178, 179, 181, 183, 185, 191, 193, 194, 198–99, 204–5, 208, 219, 220, 222, 227, 229, 230, 237, 238, 240, 246 AMC, 39, 93, 125, 127, 132, 169, 188, 220–21, 224–26 Bed Bath & Beyond, 115, 133, 188 BlackBerry, 93, 115, 133, 169, 178, 188, 224 bot activity and, 165, 166 GameStop, see GameStop, GameStop short squeeze insiders of, 224 Koss, 132, 169, 188, 224 margin debt and, 58 Naked, 132, 188 Nokia, 169, 178, 188 payment for order flow and, 207 Robinhood’s trading restrictions on, 187–89, 194, 195–200, 203, 206 Merton, Robert, 101, 102, 108 Microsoft, 46, 93 Mihm, Stephen, 48 millennials, 21, 26, 27, 56, 71, 88, 142, 143, 148, 162, 242, 246, 255 Minnis, Chad, 126, 157, 242 MoneyWatch, 59 monthly subscription services, 32 Morgan Stanley, 28, 55, 178, 219 Morningstar, 216, 244, 245, 254, 255 Motherboard, 131–32 Motter, John, 215–17, 226 Mudrick, Jason, 220–21 Mudrick Capital Management, 220 Mulligan, Finley, 230 Mulligan, Quinn, 142, 214 Munger, Charlie, 183–84, 241 Murphy, Paul, 78 Musk, Elon, 19, 75, 82–83, 92, 124, 143, 149, 152–53, 155–57, 160, 161, 167, 212, 216 tweets of, x, 60, 82, 83, 124, 144, 152–54, 161, 170 Must Asset Management, 221 mutual funds, 139, 151, 221, 234, 244, 245, 254–56 myopic loss aversion, 236 N Naked Brand, 132, 188 Nasdaq, 60, 92, 98, 104 Nasdaq Whale, 98, 104–6, 108, 109, 227 Nathan, Dan, 192 National Council on Problem Gambling, 31, 57 National Futures Association, 118 Nations, Scott, 99 Nations Indexes, 99 NCAA Basketball, 57 Netflix, x–xi, 15, 50, 98, 133, 208 Netscape, 24 Neumann, Adam, 105 New Yorker, 143 New York Mets, 8, 161 New York Post, 124, 172 New York Stock Exchange, 49 New York University, 20, 82, 177 Nikola, 64 NIO, 120 Nobel Prize, 101, 260 Nokia, 169, 178, 188 nudges, 31–32, 235–36 Nvidia, 98 O Obama, Barack, 13, 38 Ocasio-Cortez, Alexandria, 160, 197 Occupy Wall Street movement, 12, 125 Odean, Terrance, 235, 238, 243 Odey, Crispin, 126 Ohanian, Alexis, 12, 37–38, 125 O’Mara, Margaret, 38, 156, 157 Omega Family Office, 191 O’Neal, Shaquille, 64 Oppenheimer, Robert, 83 options, 34–35, 99–107, 217 call, see call options delta and, 107, 108 losses and quick approval processes for, 103 put, 46, 99, 106, 111–12, 148 Robinhood and, 34–35, 102–4, 106, 108–9 Options Clearing Corporation, 102 P Pagel, Michaela, 235 Palantir Technologies, 120 Palihapitiya, Chamath, 143, 144, 152–53, 155, 157–58, 160, 164, 212, 234, 246, 253 Palm, 84 PalmPilot, 84 Pao, Ellen, 38 Paperwork Crisis, 49 Parker, Sean, 38 payment for order flow, 10, 33, 153, 196, 206–9 Penn National, 57 penny stocks, 60, 120, 133, 166, 167 Permit Capital, 223 Pershing Square Holdings, 56 Pets.com, 90 PetSmart, 89 Pew Research, 71 Physical Impossibility of Death in the Mind of Someone Living, The (Hirst), 7 Piggly Wiggly, 78–79 PiiQ Media, 166 PIMCO, 216 Plotkin, Gabriel, 41, 56, 67, 73, 80, 85, 86, 95–96, 110–12, 114–15, 116, 122, 123, 129, 130, 133, 140, 146, 148, 157, 158, 161, 191, 197, 213–14, 217, 218, 227, 240, 246, 250, 253 at congressional hearing, 6–11 Porsche, 77 Portnoy, Dave, 57, 152–55, 158–59, 161, 181, 188–89, 212 Povilanskas, Kaspar, 195 Pruzan, Jonathan, 219 Psaki, Jen, 192 Public.com, 196, 207, 209 pump and dump, 163 put options, 46, 99, 106, 111–12, 148 Q Qualcomm, 46 R RagingBull, 163 Random Walk Down Wall Street, A (Malkiel), 253 Raskob, John J., 150–52, 154, 156 Raytheon, 153–54 RC Ventures LLC, 114 Reagan, Ronald, 156, 234 Reddit, xi, xii, 11–12, 19, 22, 23, 25, 36–39, 41, 42, 107, 122, 125, 162, 164, 199 founding of, 37–38 Gill’s influence on, 141–42; see also Gill, Keith; WallStreetBets karma on, 47, 141–42 mechanics and demographics of, and GameStop, 37 offensive subreddits on, 38 r/ClassActionRobinHood, 196 r/GMEbagholders, 140 r/investing, ix, 46 r/wallstreetbets, see WallStreetBets Super Bowl ad of, 12 Volkswagen squeeze and, 78 Reddit Revolution, xv, 41, 42, 75, 99, 152, 170, 192, 206, 211, 219, 220, 230, 246, 261 see also GameStop, GameStop short squeeze; WallStreetBets rehypothecation, 80, 92 reinforcement learning, 35 Reminiscences of a Stock Operator (Lefèvre), 78 Renaissance Technologies, 237 retail trading, xiii, xiv, xvi, 4, 7, 9–14, 49, 56–59, 63–64, 66, 67, 81, 98, 140–41, 143, 169–70, 178, 181, 183, 186, 194, 218, 237, 238, 244, 247 retirement accounts and pension funds, 5, 13, 27, 31–32, 41, 69, 76, 77, 81, 171, 182, 234, 235, 245, 252, 255, 256 Rise of the Planet of the Apes, 135–36 RiskReversal Advisors, 192 Ritter, Jay, 63, 65 Roaring Kitty (Gill’s YouTube persona), 2, 18, 45, 48–49, 92, 130, 133, 144, 171, 174–75, 191, 211, 213 Roaring Kitty LLC, 171 Robinhood, xi, xiii, xv, 4–6, 13–14, 19, 22–35, 41–42, 50, 53, 55, 57, 61, 66, 70, 81, 98, 139, 141, 153, 154, 157, 158, 161, 176, 178, 183, 184, 187–90, 193, 194, 195–210, 212–13, 219, 237–38, 243, 245, 246, 259 account transfer fees of, 54 average revenue per user of, 66–67 Buffett on, 240–41 call options and, 97–98 Citadel and, 10, 11 clearinghouse of, 187 commissions and, 49, 50 customer loan write-offs of, 205 daily average revenue trades of, 59 daily deposit requirement of, 205 former regulators hired by, 239–40 founding of, 3, 23–25, 90 funding crisis of, 187–88, 193, 198, 203, 205–6 gamification and, 29–31 Gold accounts, 32, 58, 97, 202 growth of, 25–26, 50 herding events and, 238 Hertz and, 61 hyperactive traders and, 193, 202, 207, 236 initial public offering of, 200–201, 219 Instant accounts, 32 Kearns and, 103–4 lawsuits against, 196 margin loans of, 58–59, 205 median account balances with, 50, 54 options and, 34–35, 102–4, 106, 108–9 payment for order flow and, 10, 33, 196, 206–9 revenue from securities lending, 73 risky behavior encouraged by, 202–3 Robintrack and, 53, 61 SPACs and, 64 stimulus checks and, 56 Super Bowl ad of, 28, 30, 200 technical snafus by, 53–54 Top 100 Fund and, 61 trading restricted by, 187–89, 194, 195–200, 203, 206, 209 valuation of, 49 WallStreetBets and, 22–23 wholesalers and, 33–35, 49, 104, 106 Robin Hood (charitable foundation), 196–97 robo-advisers, xv, 27, 257–58 Betterment, 27, 54, 183, 193, 242, 257, 258, 261 SoFi, 27, 56, 57, 158 Rockefeller, John D., 9 Rodriguez, Alex, 64 Rogers, Will, 163 Rogozinski, Jaime, 23, 39, 46, 50, 53, 55, 70–71, 97, 122, 138, 144, 190, 231 Roper, Barbara, 29–30, 35, 54, 185, 241 Rozanski, Jeffrey, 46 Rukeyser, Louis, 156 Russell 2000 Value Index, 125, 191 S S3 Partners, 76, 81, 130, 133, 170, 217 SAC Capital Advisors, 7, 110 Sanders, Bernie, 65–66, 198 S&P (Standard & Poor’s), 83 S&P Dow Jones Indices, 70, 254 S&P 500, 76 Sanford C.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", "there is no alternative" (TINA), "World Economic Forum" Davos, affirmative action, Alan Greenspan, Albert Einstein, algorithmic trading, Andy Kessler, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, book value, Bretton Woods, BRICs, British Empire, business cycle, buy the rumour, sell the news, capital asset pricing model, carbon credits, Carl Icahn, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, Daniel Kahneman / Amos Tversky, deal flow, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Dr. Strangelove, Dutch auction, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial engineering, financial independence, financial innovation, financial thriller, fixed income, foreign exchange controls, full employment, Glass-Steagall Act, global reserve currency, Goldman Sachs: Vampire Squid, Goodhart's law, Gordon Gekko, greed is good, Greenspan put, happiness index / gross national happiness, haute cuisine, Herman Kahn, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", job automation, Johann Wolfgang von Goethe, John Bogle, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Michael Milken, Mikhail Gorbachev, Milgram experiment, military-industrial complex, Minsky moment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, Phillips curve, planned obsolescence, plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, Reminiscences of a Stock Operator, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk free rate, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, short squeeze, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, stock buybacks, survivorship bias, tail risk, Teledyne, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, two and twenty, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

VAR signifies the maximum amount that you can lose, statistically, as a result of market moves for a given probability over a fixed time. If you own shares over a year, then most of the time the share price moves up or down a small amount. On some days you may get a large or very large price change. VAR ranks the price changes from largest fall to largest rise. Assuming that prices follow a random walk and price changes fit a normal distribution, you can calculate the probability of a particular size price change. You can answer questions like what is the likely maximum price change and loss on your holding at a specific probability level, say 99 percent, which equates to 1 day out of 100 days.

Prices followed a random walk and market participants could not systematically profit from market inefficiencies. The EMH does not require market price to be always accurate. Investors force the price to fluctuate randomly around its real value. As economist Paul Samuelson put it: “if one could be sure that a price will rise, it would already have risen.”6 The EMH was the finance version of The Price Is Right, the corollary of Chicago’s belief in free markets. Reviewing Markowitz’s work, David Durand observed that the “argument rests on the concept of the Rational Man.” Durand did not think such a creature existed and thought the whole thing had “an air of fantasy.”7 Corporate M&Ms In the late 1950s, Franco Modigliani and Merton Miller, two professors at Carnegie Mellon University, developed two propositions influencing a company’s capital structure (the mix of debt and equity) and dividend policy.

Fund managers with high returns simply took higher risk rather than possessing supernatural skill. Demon of Chance The efficient market hypothesis (EMH) stated that the stock prices followed a random walk, a formal mathematical statement of a trajectory consisting of successive random steps. Pioneers Jules Regnault (in the nineteenth century) and Louis Bachelier (early twentieth century) had discovered that short-term price changes were random—a coin toss could predict up or down moves. Bachelier’s Sorbonne thesis established that the probability of a given change in price was consistent with the Gaussian or bell-shaped normal distribution, well-known in statistical theory.


pages: 299 words: 92,782

The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing by Michael J. Mauboussin

Amazon Mechanical Turk, Atul Gawande, Benoit Mandelbrot, Black Swan, Boeing 747, Checklist Manifesto, Clayton Christensen, cognitive bias, commoditize, Daniel Kahneman / Amos Tversky, David Brooks, deliberate practice, disruptive innovation, Emanuel Derman, fundamental attribution error, Gary Kildall, Gini coefficient, hindsight bias, hiring and firing, income inequality, Innovator's Dilemma, John Bogle, Long Term Capital Management, loss aversion, Menlo Park, mental accounting, moral hazard, Network effects, power law, prisoner's dilemma, random walk, Richard Thaler, risk-adjusted returns, shareholder value, Simon Singh, six sigma, Steven Pinker, transaction costs, winner-take-all economy, zero-sum game, Zipf's Law

These researchers carefully structured the analysis so that it would discriminate between luck and skill in explaining how companies achieved success. The main finding of the study is that “the results consistently indicate that there are many more sustained superior performers than we would expect through the occurrence of lucky random walks.” While this is comforting because it suggests that management's actions, or skill, can lead to success, efforts are ongoing to pinpoint accurately which behaviors were the correct ones. So unlike sports, where there are some observable measures of skill (such as hitting a baseball), all we can really say today is that we cannot explain results by luck alone and that it appears that skill plays a role when companies earn a high return on their assets.

A New Measure That Predicts Performance,” Review of Financial Studies 22, no. 9, September 2009, 3329–3365; and Antti Petajisto, “Active Share and Mutual Fund Performance,” working paper, December 15, 2010. The technical definition of active share: where: ωfund, i = portfolio weight of asset i in the fund ωindex, i = portfolio weight of asset i in the index The sum is taken over the universe of all assets. 27. Jerker Denrell, “Random Walks and Sustained Competitive Advantage,” Management Science 50, no. 7 (July 2004): 922–934. Chapter 8—Building Skill 1. Daniel Kahneman and Gary Klein, “Conditions for Intuitive Expertise: A Failure to Disagree,” American Psychologist 64, no. 6 (September 2009): 515–526. 2. Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011). 3.

Bradford, and Kevin Lang. “Are All Economic Hypotheses False?” Journal of Political Economy 100, no. 6 (December 1992): 1257–1272. Denrell, Jerker. “Vicarious Learning, Undersampling of Failure, and the Myths of Management.” Organization Science 14, no. 3 (May–June 2003): 227–243. Denrell, Jerker. “Random Walks and Sustained Competitive Advantage.” Management Science 50, no. 7 (July 2004): 922–934. Derman, Emanuel. Models. Behaving. Badly: Why Confusing Illusion with Reality Can Lead to Disaster, on Wall Street and in Life. New York: Free Press, 2011. Dixon, Mike J., Kevin A. Harrigan, Rajwant Sandhu, Karen Collins, and Jonathan A.


pages: 162 words: 50,108

The Little Book of Hedge Funds by Anthony Scaramucci

Alan Greenspan, Andrei Shleifer, asset allocation, Bear Stearns, Bernie Madoff, business process, carried interest, corporate raider, Credit Default Swap, diversification, diversified portfolio, Donald Trump, Eugene Fama: efficient market hypothesis, fear of failure, financial engineering, fixed income, follow your passion, global macro, Gordon Gekko, high net worth, index fund, it's over 9,000, John Bogle, John Meriwether, Long Term Capital Management, mail merge, managed futures, margin call, mass immigration, merger arbitrage, Michael Milken, money market fund, Myron Scholes, NetJets, Ponzi scheme, profit motive, proprietary trading, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, risk/return, Ronald Reagan, Saturday Night Live, Sharpe ratio, short selling, short squeeze, Silicon Valley, tail risk, Thales and the olive presses, Thales of Miletus, the new new thing, too big to fail, transaction costs, two and twenty, uptick rule, Vanguard fund, Y2K, Yogi Berra, zero-sum game

As I sat in Baker Library, anxiously waiting for my first interview with Goldman Sachs, I picked up A Random Walk on Wall Street by Burton Malkiel. It was then that I got my first exposure to the efficient market theory. Sure, I had heard the term in a Corporate Finance class at Tufts University—my undergraduate alma mater—but the concept barely registered. In plain prose, Professor Malkiel explained that due to perfect information being priced immediately into the markets, the stock prices moved in a random walk. There was no discernible way to predict future prices. Nope. Sorry. No technical analysis, no fundamental analysis, nothing. See, current stock prices were nothing more than a representation of the net present value of the future cash flow streams of each respective company.

Arbitrage Before we delve into the individual relative value strategies, we must first define arbitrage. Arbitrage is a financial transaction that involves two similar items that are priced differently in different markets. In practice, the trader simultaneously purchases a position in one market and sells the similar position in a different market at a different price. In other words, he is exploiting the price differences of identical positions by buying the same security at a lower price and selling it right away at a higher price. In a perfect scenario, the arbitrageur profits from a difference in the price between the two and earns an immediate profit with no market risk. For example, an announced deal might provide an opportunity for risk arbitrage, or the issuance of a convertible bond by a publicly traded company may signal an opportunity for convertible arbitrage.

Just as he practiced at his alma mater, Steyer’s day began by studying the merger and acquisition action taking place across the continent so that he could pounce on the stock’s initial price offering before it skyrocketed after the takeover bid was announced. If he discovered that a merger or acquisition was about to occur, he would quickly compare the current trading price per share to the bid price. In knowing that the current trading price would move toward the direction of the bidding price if the deal went through, he would buy the stock and short the acquiring firm so that he could pocket the difference if the acquisition was consummated.


pages: 662 words: 180,546

Never Let a Serious Crisis Go to Waste: How Neoliberalism Survived the Financial Meltdown by Philip Mirowski

"there is no alternative" (TINA), Adam Curtis, Alan Greenspan, Alvin Roth, An Inconvenient Truth, Andrei Shleifer, asset-backed security, bank run, barriers to entry, Basel III, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Bernie Sanders, Black Swan, blue-collar work, bond market vigilante , bread and circuses, Bretton Woods, Brownian motion, business cycle, capital controls, carbon credits, Carmen Reinhart, Cass Sunstein, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, constrained optimization, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, dark matter, David Brooks, David Graeber, debt deflation, deindustrialization, democratizing finance, disinformation, do-ocracy, Edward Glaeser, Eugene Fama: efficient market hypothesis, experimental economics, facts on the ground, Fall of the Berlin Wall, financial deregulation, financial engineering, financial innovation, Flash crash, full employment, George Akerlof, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Greenspan put, Hernando de Soto, housing crisis, Hyman Minsky, illegal immigration, income inequality, incomplete markets, information asymmetry, invisible hand, Jean Tirole, joint-stock company, junk bonds, Kenneth Arrow, Kenneth Rogoff, Kickstarter, knowledge economy, l'esprit de l'escalier, labor-force participation, liberal capitalism, liquidity trap, loose coupling, manufacturing employment, market clearing, market design, market fundamentalism, Martin Wolf, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Naomi Klein, Nash equilibrium, night-watchman state, Northern Rock, Occupy movement, offshore financial centre, oil shock, Pareto efficiency, Paul Samuelson, payday loans, Philip Mirowski, Phillips curve, Ponzi scheme, Post-Keynesian economics, precariat, prediction markets, price mechanism, profit motive, public intellectual, quantitative easing, race to the bottom, random walk, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Savings and loan crisis, savings glut, school choice, sealed-bid auction, search costs, Silicon Valley, South Sea Bubble, Steven Levy, subprime mortgage crisis, tail risk, technoutopianism, The Chicago School, The Great Moderation, the map is not the territory, The Myth of the Rational Market, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wisdom of Crowds, theory of mind, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, Tobin tax, tontine, too big to fail, transaction costs, Tyler Cowen, vertical integration, Vilfredo Pareto, War on Poverty, Washington Consensus, We are the 99%, working poor

,” on Hyman Minsky influence of on “informational efficacy” and “allocative efficiency,” on Keynesian Theory in New York Review of Books orthodox economics profession on reason for becoming an economist “The Return of Depression Economics,” Kydland–Prescott notion L La Bute, Neil Laibson, David Laissez-faire Lal, Deepak LAMP (Liberal Archief, Ghent) Lanchester, John Landsbanki Lange, Oskar Lasn, Kalle Late Neoliberalism Lehman Brothers Leoni, Bruno Les Mots et les Choses Levin, Richard Levine, David Levitt, Steven Levy, David Lewis, Michael, The Big Short Liberatarianism Liberty Institute Liberty International Liberty League LIBOR scandal Lilly Endowment LinkedIn L’Institut Universitaire des Hautes Etudes Internationales at Geneva Litan, Robert Competitive Equity The Derivatives Dealer’s Club “In Defense of Much, But Not All, Financial Innovation,” writings of Lloyd’s Bank Lo, Andrew on economic crisis Harris & Harris Group Professor of Finance A Non-Random Walk Down Wall Street “Reading About the Financial Crisis,” Lohmann, Larry “Looting: The Economic Underworld of Bankruptcy for Profit” (Romer) Lowenstein, Roger LSE (London School of Economics) Lucas, Robert E. as Bank of Sweden Prize winner on corruption on economic crisis followers of on Keynes neoclassical economists on rational-expectations macroeconomics movement Luntz, Frank M Mack, Christy MacKenzie, Donald MacKinley, A. Craig, A Non-Random Walk Down Wall Street MacroMarkets LLC Madoff, Bernie Make Markets Be Markets (Roosevelt Institute) Mallaby, Sebastian Mankiw, Gregory Marcet, Albert Market Design, Inc.

But what the journalists like Cassidy, Fox, and Lowenstein and commentators like Krugman neglected to inform their readers was that the back and forth, the intellectual thrust and empirical parry, had ground to a standoff more than a decade before the crisis, as admirably explained in Lo and MacKinlay, A Non-Random Walk Down Wall Street: There is an old joke, widely told among economists, about an economist strolling down the street with a companion when they come upon a $100 bill lying on the ground. As the companion reaches down to pick it up, the economist says, “Don’t bother—if it were a real $100 bill, someone would have already picked it up.”

Lo, Andrew. “Reading About the Financial Crisis,” Journal of Economic Literature, 50 (2012): 151-178. Lo, Andrew. “Reconciling Efficient Markets with Behavioral Finance: The Adaptive Markets Hypothesis,” Journal of Investment Consulting 7 (2005): 21–44. Lo, Andrew, and Craig MacKinlay. A Non-Random Walk Down Wall Street (Princeton: Princeton University Press, 1999). Loewenstein, George, and Peter Ubel. “Economics Behaving Badly,” New York Times, July 14, 2010. Lofgren, Mike. “Revolt of the Rich,” American Conservative, September 2012. Lohmann, Larry. “Carbon Trading: A Critical Dialogue,” Development Dialogue no. 48, September 2006.


pages: 460 words: 107,712

A Devil's Chaplain: Selected Writings by Richard Dawkins

Albert Einstein, Alfred Russel Wallace, Boeing 747, Buckminster Fuller, butterfly effect, Claude Shannon: information theory, complexity theory, Desert Island Discs, double helix, Douglas Hofstadter, epigenetics, experimental subject, Fellow of the Royal Society, gravity well, Gregor Mendel, Necker cube, out of africa, Peoples Temple, phenotype, placebo effect, random walk, Richard Feynman, Silicon Valley, stem cell, Stephen Hawking, the scientific method

A belief in the ubiquity of gradualistic evolution does not necessarily commit us to Darwinian natural selection as the steering mechanism guiding the search through genetic space. It is highly probable that Motoo Kimura is right to insist that most of the evolutionary steps taken through genetic space are unsteered steps. To a large extent the trajectory of small, gradualistic steps actually taken may constitute a random walk rather than a walk guided by selection. But this is irrelevant if – for the reasons given above – our concern is with adaptive evolution as opposed to evolutionary change per se. Kimura himself rightly insists9 that his ‘neutral theory is not antagonistic to the cherished view that evolution of form and function is guided by Darwinian selection’.

Further, the theory does not deny the role of natural selection in determining the course of adaptive evolution, but it assumes that only a minute fraction of DNA changes in evolution are adaptive in nature, while the great majority of phenotypically silent molecular substitutions exert no significant influence on survival and reproduction and drift randomly through the species. The facts of adaptation compel us to the conclusion that evolutionary trajectories are not all random. There has to be some nonrandom guidance towards adaptive solutions because nonrandom is what adaptive solutions precisely are. Neither random walk nor random saltation can do the trick on its own. But does the guiding mechanism necessarily have to be the Darwinian one of nonrandom survival of random spontaneous variation? The obvious alternative class of theory postulates some form of nonrandom, i.e. directed, variation. Nonrandom, in this context, means directed towards adaptation.

He looks at the actual course of evolution and argues that such apparent progress as can in general be detected is artefactual (like the baseball statistic). Cope’s rule of increased body size, for example, follows from a simple ‘drunkard’s walk’ model. The distribution of possible sizes is confined by a left wall, a minimal size. A random walk from a beginning near the left wall has nowhere to go but up the size distribution. The mean size has pretty well got to increase, and it doesn’t imply a driven evolutionary trend towards larger size. As Gould convincingly argues, the effect is compounded by a human tendency to give undue weight to new arrivals on the geological scene.


pages: 367 words: 97,136

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation by Sebastien Page

Andrei Shleifer, asset allocation, backtesting, Bernie Madoff, bitcoin, Black Swan, Bob Litterman, book value, business cycle, buy and hold, Cal Newport, capital asset pricing model, commodity super cycle, coronavirus, corporate governance, COVID-19, cryptocurrency, currency risk, discounted cash flows, diversification, diversified portfolio, en.wikipedia.org, equity risk premium, Eugene Fama: efficient market hypothesis, fixed income, future of work, Future Shock, G4S, global macro, implied volatility, index fund, information asymmetry, iterative process, loss aversion, low interest rates, market friction, mental accounting, merger arbitrage, oil shock, passive investing, prediction markets, publication bias, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, robo advisor, seminal paper, shareholder value, Sharpe ratio, sovereign wealth fund, stochastic process, stochastic volatility, stocks for the long run, systematic bias, systematic trading, tail risk, transaction costs, TSMC, value at risk, yield curve, zero-coupon bond, zero-sum game

However, Marra shows that for US stocks, most sophisticated models, whether of the historical or ARCH classes, barely outperform the random walk model. The differences in model effectiveness don’t look statistically significant. Other issues with sophisticated models include publication bias (only the good results get published), as well as a related, important issue: the possibility that these models may overfit the in-sample data. It’s hard to argue that one specific model should perform consistently better than to simply extrapolate recent volatility. Aside from a slight advantage for volatility estimates derived from options prices, Poon and Granger find that across 93 academic studies, there’s no clear winner of the great risk forecasting horse race.

In a 2005 review of the literature on how to forecast risk, Ser-Huang Poon and Clive Granger summarize 93 published papers on the topic. Think of Poon and Granger’s article as the summary of a giant, multiyear, multiauthor horse race to find the best model. They compare the effectiveness of historical, ARCH, stochastic volatility, and option-implied models. Historical models include the random walk model, which I used in my example on US stocks when I simply assumed that next month’s volatility would be the same as this month’s (plus or minus some unpredictable noise term). This model is perhaps the easiest to implement, and Poon and Granger conclude that it’s very tough to beat. To clarify, this model doesn’t assume volatility is random.

., 226–229 Public market equivalent (PME), 221–223, 229 Public pensions, 219 Publication bias, 91–92 Q Group conferences, 7 Qian, Edward, 213–214 Quantitative analysis, judgment and, 84–85 Quantitative data analysis, 2–3 Quantitative easing (QE), 17 Quantitative investing, momentum in models for, 70 Quantitative value-at-risk models, 165 Random walk model, 91 Real estate: CAPM expected returns for, 20 diversification with, 128–130 private, 129–130 Real estate investment trusts (REITS), 18, 240 Real returns, inflation and, 11, 13 “Regime Shifts” (Kritzman, Turkington, and Page), 156, 157 Regime-switching dynamic correlation (RDSC) model, 140 Relative returns: on dashboards, 64–66 and persistence of higher moments, 112, 117–119 on stocks vs. bonds, 10–11, 17–19, 112, 117–119 Relative valuation: and CAPE, 27–28 macro factors confirming signals, 67–68 shorter-term signals of, 56–59 Resampling, 208 Retirement planning, 187–194, 249–253 Return forecasting, 1–3, 83–87, 267, 272 equilibrium, 5–23 momentum, 69–82 paradox of, 73 rules of thumb for, 86–87 shorter-term macro signals, 61–68 shorter-term valuation signals, 45–59 valuation, 25–42 “Return of the Quants” (Dreyer et al.), 94 “The Revenge of the Stock Pickers” (Lynch et al.), 233 Rich, Don, 168–169 Richardson, Matthew, 115 Ringgenberg, Matthew C., 236 Risk aversion, 189, 204 Risk factor diversification, 130–131, 135, 176–177 Risk factors: asset classes vs., 173–184 crowding of, 184 in portfolio construction, 174 in scenario analysis, 162–165 Risk factors models, 178–179 Risk forecasting, 89–92, 267–268, 272–273 basic parameter choices for, 144–145 CAPM definition of, 10 correlation forecasts, 139–143 correlations, 121–136 exposure to loss in, 143–144 fat tails, 147–157 goal of, 178–179 longer-term, 111–119 models of, 89–92 risk-based investing, 93–109 rules of thumb for, 170–171 scenario analysis, 157–168 within-horizon risk in, 168–170 “Risk Management for Hedge Funds” (Lo), 150 Risk parity: and implicit return assumptions, 2 managed volatility vs., 105–106 in portfolio optimization, 212–215 Risk Parity Fundamentals (Qian), 213–214 Risk predictability tests, 112–119 Risk premiums, 179–184 backtest data for, 182–184 beta, 179–180 for bonds, 40 and currency carry trade, 131 diversification across, 182 low-risk anomaly, 180–181 and risk factors, 179–180 and Sharpe ratios, 150, 151 strategies for, 182–184 volatility, 102–104, 181–182 when rates are low, 12 Risk regimes, 131, 154–157, 168, 204 Risk tolerance, 149–150 Risk-based investing, 93–109 combination of strategies for, 104 covered call writing, 102–104 managed volatility backtests, 95–101 Q&A about, 105–109 (See also Managed volatility) Risk-free rate, 11 Roll, Richard, 62, 67 Roll down, 40–41 Ross, Stephen A., 62, 67 Rossi, Marco, 131 Rules of thumb: for portfolio construction, 243–244 for return forecasting, 86–87 for risk forecasting, 170–171 Samonov, Mikhail, 71–75 Sample bias, 223 Samuelson, Paul A., 186–187, 197–198 Sapra, Steve, 132 Satchell, Stephen, 212 Scenario analysis: in asset allocation, 134 and asset class changes over time, 158–162 defensive use of, 157–167 defining scenarios in, 158 factor-based, 162–165 forward-looking scenarios in, 165–168 offensive use of, 167–168 Scherer, Bernd, 2, 117 Schoar, Antoinette, 221, 222 Seasholes, Mark, 31 Sentiment, 69, 131–132 Sharpe, Bill, 6, 7, 9, 13, 151 Sharpe ratios, 150 Sharps, Rob, 228 Shiller, Robert, 13, 14, 25–26 “The Shiller CAPE Ratio” (Siegel), 26 Shive, Sophie, 235 Shkreli, Martin, 238 Shorter-term investments, macro factors for, 63–66 Shorter-term valuation signals, 45–59 for relative valuation between stocks and bonds and across bond markets, 56–59 for tactical asset allocation, 45–59 Shriver, Charles, 57, 62, 94 Siegel, Jeremy, 12–14, 25, 26 Simonato, Jean-Guy, 143 Single-period portfolio optimization, 194–195, 197–215, 268 issues with concentrated and unstable solutions, 207–210 mean-variance optimization, 198, 203–207 and risk parity, 212–215 and usefulness of optimizers, 211–212 Size of measurement errors, 148 Skewness, 118 of call options, 118 mean reversion of, 118–119 persistence of, 117–119 positive vs. negative, 207 and risk forecasting, 144–145 (See also Negative skewness) “Skulls, Financial Turbulence, and Risk Management” (Kritzman and Li), 205 Smart betas, 179, 235 S.M.O.O.T.H. fund, 224–225 Smoothing bias, 128–130 Sovereign wealth funds, 37, 128–130, 194 S&P 500: in March 2018, 12 P/E ratio of, 30–31 realized one-month volatility on, 103–104 recent earnings on, 27 sector weights in, 159 and tech bubble, 163 Spread duration, 40–42 Stock market: used as market portfolio, 17–18 valuation changes in, 31–34 Stock picking, 233–243 “The Stock-Bond Correlation” (Johnson et al.), 132–133 Stocks: beta and relative returns of bonds and, 10–11 CAPM and returns on, 5–14, 20 correlation of bonds and, 132–134 of emerging markets, 159–160 and human capital, 189–190 international equity diversion, 125–126 in market portfolio, 17–19 P/E ratio and real return for, 12–13 P/E ratio vs.


pages: 443 words: 51,804

Handbook of Modeling High-Frequency Data in Finance by Frederi G. Viens, Maria C. Mariani, Ionut Florescu

algorithmic trading, asset allocation, automated trading system, backtesting, Bear Stearns, Black-Scholes formula, book value, Brownian motion, business process, buy and hold, continuous integration, corporate governance, discrete time, distributed generation, fear index, financial engineering, fixed income, Flash crash, housing crisis, implied volatility, incomplete markets, linear programming, machine readable, mandelbrot fractal, market friction, market microstructure, martingale, Menlo Park, p-value, pattern recognition, performance metric, power law, principal–agent problem, random walk, risk free rate, risk tolerance, risk/return, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process

See also Volatility index (VIX) pVIX cVIX spread, 106 Qiu, Hongwei, xiv, 97 Q-learning algorithm, 65 Quadratic covariation formula, 244 Quadratic covariation-realized covariance estimator, 266 Quadratic utility function, 286 Quadratic variation, estimate of, 224 Quadrinomial tree method, 99–100 volatility index convergence and, 105 vs. CBOE procedure, 100–101 Quantile–quantile (QQ) plots, 80 of empirical CDF, 136 of high-frequency tranche prices, 92, 94 of tranche prices, 83–84 ‘‘Quantile type’’ rule, 30 Quantum mechanics, 385 Quote-to-quote returns, 258, 260 Random variables, 334–336 Random walk, 126 Rare-event analysis, 32–33 Rare-event detection, 28, 30–32 Rare events detecting and evaluating, 29–35 equity price and, 44 trades profile and, 42, 43 Rare-events distribution, 41–44 peaks in, 42 Real daily integrated covariance, regressing, 281 Real integrated covariance regressions, results of, 282–285 Realized covariance (RC), 269 estimator for, 280 measures of, 272 Realized covariance plus leads and lags (RCLL), 266 estimator for, 280, 290 Realized covariance–quadratic variation estimator, 244 Realized variance, 12 Realized volatility, microstructure noise and, 274 Index Realized volatility estimator, 253–254, 256 results of, 276–279 Realized volatility estimator performance, ranking, 279 Realized-volatility-type measures, 275 Real-valued functions, 350, 351, 388–389 Refresh time, 267 Refresh time procedure, 244 Refresh time synchronization method, 268 Regime-switching default correlation, 81–84 Regime-switching default correlation model, 76 Regime-switching model, drawback of, 84–85 ‘‘Regret-free’’ prices, 238 Regular asynchronous trading, 264 Regular nonsynchronous trading, 268 Regular synchronous trading, 268 Relative risk process, 296 Rellich’s theorem, 398 Representative ADT algorithm, 52–53, 54.

Additionally, the combination of Adaboost and the BSC can be used as a semiautomated strategic planning system that continuously updates itself for board-level decisions of directors or for investment decisions of portfolio managers. REFERENCES Acharya VV, John K, Sundaram RK. On the optimality of resetting executive stock options. J Financ Econ 2000;57:65–101. Alexander S. Price movements in speculative markets: trends or random walks. Ind Manag Rev 1961;2:7–26. Algoet PH, Cover TM. Asymptotic optimality and asymptotic equipartition properties of log-optimum investment. Ann Probab 1988;16:876–898. Allen F, Karjalainen R. Using genetic algorithms to find technical trading rules. J Financ Econ 1999;51:245–271.

Therefore, using federal funds effective rate at 0.15%, the forward 112 CHAPTER 5 Construction of Volatility Indices index level F1 for the near term options and forward index level F2 for the next term options are F1 = Strike price + erT1 (Call price − Put price) = 1025 + e0.0015×0.026626712 (13.25 − 13.9) = 1024.35 F2 = Strike price + erT2 (Call price − Put price) = 1025 + e0.0015×0.103339041 (29.15 − 30.35) = 1023.80 We also obtain K0 , the strike price immediately below F , which is $1020 for both expirations. Then we select call and put options that have strike prices greater and smaller, respectively, than K0 (it is 1020 here) and nonzero bid price. After encountering two consecutive options with a bid price of zero, do not select any other option. Note that the prices of the options are calculated using the mid-point of the bid-ask spread.


Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage by Zdravko Markov, Daniel T. Larose

Firefox, information retrieval, Internet Archive, iterative process, natural language processing, pattern recognition, random walk, recommendation engine, semantic web, sparse data, speech recognition, statistical model, William of Occam

c 0.726 b 0.413 50 CHAPTER 2 HYPERLINK-BASED RANKING PAGERANK The hyperlinks are not only ways to propagate the prestige score of a page to pages to which it links, they are also paths along which web users travel from one web page to another. In this respect, the popularity (or prestige) of a web page can be measured in terms of how often an average web user visits it. To estimate this we may use the metaphor of the “random web surfer,” who clicks on hyperlinks at random with uniform probability and thus implements the random walk on the web graph. Assume that page u links to Nu web pages and page v is one of them. Then once the web surfer is at page u, the probability of visiting page v will be 1/Nu . This intuition suggests a more sophisticated scheme of propagation of prestige through the web links also involving the out-degree of the nodes.

Consider, for example, two pages that point to each other but do not point to other pages. Such an isolated loop is called a rank sink. If pointed to from an outside page, it accumulates rank but never distributes it to other nodes. To deal with the rank sink situation, we return to the random surfer model. As we have already noted, computing page rank is based on the idea of a random walk on the web graph, but the random surfer may get trapped into a rank sink. To avoid this situation we try to model the behavior of a real web surfer who gets bored running into a loop and jumps to some other web page outside the rank sink. For this purpose we introduce a rank source E, a vector over all web pages, which defines the probability distribution of jumping to a web page at random.

The parameter d implements the normalization step and also affects the rate of convergence positively. The alternative approach would be just to add E to R and then normalize (R ← R/R1 ). As defined, the PageRank algorithm implements the random surfer model, where: r The rank vector R defines the probability distribution of a random walk on the graph of the Web. r With some low probability the surfer jumps to a random page chosen according to the distribution E. The source of rank E is usually chosen as a uniform vector with a small norm (e.g., E1 = 0.15). The way it affects the model of the random surfer is that the jumps to a random page happen more often if the norm of E is larger.


pages: 543 words: 147,357

Them And Us: Politics, Greed And Inequality - Why We Need A Fair Society by Will Hutton

Abraham Maslow, Alan Greenspan, Andrei Shleifer, asset-backed security, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Blythe Masters, Boris Johnson, bread and circuses, Bretton Woods, business cycle, capital controls, carbon footprint, Carmen Reinhart, Cass Sunstein, centre right, choice architecture, cloud computing, collective bargaining, conceptual framework, Corn Laws, Cornelius Vanderbilt, corporate governance, creative destruction, credit crunch, Credit Default Swap, debt deflation, decarbonisation, Deng Xiaoping, discovery of DNA, discovery of the americas, discrete time, disinformation, diversification, double helix, Edward Glaeser, financial deregulation, financial engineering, financial innovation, financial intermediation, first-past-the-post, floating exchange rates, Francis Fukuyama: the end of history, Frank Levy and Richard Murnane: The New Division of Labor, full employment, general purpose technology, George Akerlof, Gini coefficient, Glass-Steagall Act, global supply chain, Growth in a Time of Debt, Hyman Minsky, I think there is a world market for maybe five computers, income inequality, inflation targeting, interest rate swap, invisible hand, Isaac Newton, James Dyson, James Watt: steam engine, Japanese asset price bubble, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, knowledge worker, labour market flexibility, language acquisition, Large Hadron Collider, liberal capitalism, light touch regulation, Long Term Capital Management, long term incentive plan, Louis Pasteur, low cost airline, low interest rates, low-wage service sector, mandelbrot fractal, margin call, market fundamentalism, Martin Wolf, mass immigration, means of production, meritocracy, Mikhail Gorbachev, millennium bug, Money creation, money market fund, moral hazard, moral panic, mortgage debt, Myron Scholes, Neil Kinnock, new economy, Northern Rock, offshore financial centre, open economy, plutocrats, power law, price discrimination, private sector deleveraging, proprietary trading, purchasing power parity, quantitative easing, race to the bottom, railway mania, random walk, rent-seeking, reserve currency, Richard Thaler, Right to Buy, rising living standards, Robert Shiller, Ronald Reagan, Rory Sutherland, Satyajit Das, Savings and loan crisis, shareholder value, short selling, Silicon Valley, Skype, South Sea Bubble, Steve Jobs, systems thinking, tail risk, The Market for Lemons, the market place, The Myth of the Rational Market, the payments system, the scientific method, The Wealth of Nations by Adam Smith, three-masted sailing ship, too big to fail, unpaid internship, value at risk, Vilfredo Pareto, Washington Consensus, wealth creators, work culture , working poor, world market for maybe five computers, zero-sum game, éminence grise

Happily ignoring the accumulated wisdom of Russell, Knight, Keynes and Newton, from the 1960s onwards, a group of mathematical economists hypothesised that the financial markets were different. There is abundant data about the movement of the prices of financial assets, although actually defining the universe of data proved much more problematic in practice. If you make the assumptions that financial markets are efficient containing all the information that they can, and that consequently all price movements are independent of each other and cannot be related to each other or the past, then important conclusions follow. Financial prices will move wholly randomly, as likely to go up as down – the ‘random walk’. If this is true then, as mentioned earlier, financial data will correspond to the law of large numbers and follow the same rules that dictate the distribution of, say, tall, average and short people, dice rolls and flips of a coin.

What Weatherstone wanted to know was how much money the bank would lose if it were hit by a big event outside the normal distribution of events. Such events are statistically improbable but still possible. But would they present too much risk, and bring down the whole bank? This led to the development of mathematically computed value at risk (VaR), which was based on the same assumptions about random walks, efficient markets and bell curves that had been used when pricing derivatives. The VaR figure is the maximum amount a financial institution might lose on any given day with a probability of 95 per cent or higher. Dick Fuld, the CEO of Lehman Brothers, could comfort himself throughout 2007 and even the first half of 2008 that his bank was exposed to less than $100 million of VaR on any given day (between 95 and 99 per cent confidence level).

When the mathematician Benoit Mandelbrot began developing his so-called fractal mathematics and power laws in the early 1960s, arguing that the big events outside the normal distribution are the ones that need explaining and assaulting the whole edifice of mathematical theory and the random walk, MIT’s Professor Paul Cootner (the great random walk theorist) exclaimed: ‘surely, before consigning centuries of work to the ash pile, we should like some assurance that all our work is truly useless’. Mandelbrot withdrew from economics to ask the same questions in the natural sciences.38 Forty-five years later, we have the assurance that Cootner demanded.


pages: 111 words: 1

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb

Alan Greenspan, Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, behavioural economics, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, financial engineering, fixed income, global village, hedonic treadmill, hindsight bias, junk bonds, Kenneth Arrow, Linda problem, Long Term Capital Management, loss aversion, mandelbrot fractal, Mark Spitznagel, Market Wizards by Jack D. Schwager, mental accounting, meta-analysis, Michael Milken, Myron Scholes, PalmPilot, Paradox of Choice, Paul Samuelson, power law, proprietary trading, public intellectual, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, risk free rate, road to serfdom, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Tragedy of the Commons, Turing test, Yogi Berra

We can change the barrel to contain 500 holes, a matter that would decrease the probability of death, and see the results. Monte Carlo simulation methods were pioneered in martial physics in the Los Alamos laboratory during the A-bomb preparation. They became popular in financial mathematics in the 1980s, particularly in the theories of the random walk of asset prices. Clearly, we have to say that the example of Russian roulette does not need such apparatus, but many problems, particularly those resembling real-life situations, require the potency of a Monte Carlo simulator. Monte Carlo Mathematics It is a fact that “true” mathematicians do not like Monte Carlo methods.

The two greatest minds to me, Einstein and Keynes, both started their intellectual journeys with it. Einstein wrote a major paper in 1905, in which he was almost the first to examine in probabilistic terms the succession of random events, namely the evolution of suspended particles in a stationary liquid. His article on the theory of the Brownian movement can be used as the backbone of the random walk approach used in financial modeling. As for Keynes, to the literate person he is not the political economist that tweed-clad leftists love to quote, but the author of the magisterial, introspective, and potent Treatise on Probability. For before his venturing into the murky field of political economy, Keynes was a probabilist.

While early economic models excluded randomness, Arthur explained how “unexpected orders, chance meetings with lawyers, managerial whims . . . would help determine which ones achieved early sales and, over time, which firms dominated.” MATHEMATICS INSIDE AND OUTSIDE THE REAL WORLD A mathematical approach to the problem is in order. While in conventional models (such as the well-known Brownian random walk used in finance) the probability of success does not change with every incremental step, only the accumulated wealth, Arthur suggests models such as the Polya process, which is mathematically very difficult to work with, but can be easily understood with the aid of a Monte Carlo simulator. The Polya process can be presented as follows: Assume an urn initially containing equal quantities of black and red balls.


pages: 405 words: 109,114

Unfinished Business by Tamim Bayoumi

Alan Greenspan, algorithmic trading, Asian financial crisis, bank run, banking crisis, Basel III, battle of ideas, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Berlin Wall, Big bang: deregulation of the City of London, book value, Bretton Woods, British Empire, business cycle, buy and hold, capital controls, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, credit crunch, currency manipulation / currency intervention, currency peg, Doha Development Round, facts on the ground, Fall of the Berlin Wall, financial deregulation, floating exchange rates, full employment, Glass-Steagall Act, Greenspan put, hiring and firing, housing crisis, inflation targeting, junk bonds, Just-in-time delivery, Kenneth Rogoff, liberal capitalism, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, market bubble, Martin Wolf, moral hazard, oil shale / tar sands, oil shock, price stability, prisoner's dilemma, profit maximization, quantitative easing, race to the bottom, random walk, reserve currency, Robert Shiller, Rubik’s Cube, Savings and loan crisis, savings glut, technology bubble, The Great Moderation, The Myth of the Rational Market, the payments system, The Wisdom of Crowds, too big to fail, trade liberalization, transaction costs, value at risk

The short-term prediction is that day-to-day movements in prices of stocks and bonds are unpredictable except to the extent that they reflect new information. Clearly, if General Motors announces profits that are higher than investors expect then the price of its shares will increase. However, it is equally likely that the profit announcement will disappoint and shares will go down. Hence, before the announcement, the direction of the share price is unpredictable—it is as likely to rise as to fall. This is often called the random walk theory, as the same properties are exhibited by a random walk in which, while taking a step forward, a person—usually assumed to be drunk—is as likely to also lurch to the left as to the right.

The booms primarily involved excessive borrowing, much of which was used to buy houses. The resulting increase in house prices, however, had little impact on the measures of consumer price inflation that the central banks focused on. In the United States, the consumer price index used rents rather than new house prices to estimate the cost of housing as this is (correctly) seen as more direct measure of the price of shelter. As rents did not take off in the same way that house prices did, the impact of the housing bubble on consumer price inflation was muted. In the European Union, house prices had no direct impact on consumer price inflation as, in the absence of a uniform way of measuring dwelling costs across the member countries, the consumer price index used by the ECB excluded any measure of housing costs.

., (i), (ii) North Atlantic crisis and Basel rules, (i) causes, (i) and currency unions, (i) and debt flows, (i) and economic models, (i) effects and consequences, (i), (ii), (iii), (iv) ends European banking boom, (i) European monetary union effect on, (i) and fall in confidence in experts, (i) financial boom and bust, (i), (ii) misery index, (i) origins (August 2007), (i), (ii), (iii) output losses, (i), (ii) responses to, (i), (ii), (iii), (iv) responsibility for, (i) and speculative ventures, (i) unpreparedness, (i), (ii), (iii) as watershed event, (i) Norway invited to join European Economic Community, (i) in Scandinavian monetary union, (i) Obama, Barack, (i) Office of the Comptroller of the Currency (OCC; US), (i), (ii), (iii) oil prices, (i), (ii) Organisation for Economic Cooperation and Development (OECD), (i) output losses, (i), (ii) volatility, (i) Outright Monetary Transaction (OMT, Euro area), (i), (ii), (iii) Padoa-Schioppa, Tommaso, (i), (ii) Parvest Dynamic ABS, (i) petrodollars, (i), (ii) Philippines, (i) physics: parallel with economic models, (i) Plaza Agreement (1985), (i), (ii), (iii) Pöhl, Karl Otto, (i), (ii), (iii) Pompidou, Georges, (i) Portugal borrowing interest rate, (i) commercial loans, (i) connected firms in, (i) in currency union periphery, (i) in Euro area, (i) European aid to, (i) excessive borrowing, (i) in Exchange Rate Mechanism, (i) expansion in bank assets, (i) financial crisis in, (i), (ii), (iii) high interest rates, (i) product market improvements, (i) reduces fiscal deficit, (i) ten-year bonds, (i) pound sterling (UK currency) Bundesbank ceases to support, (i), (ii) devalued, (i) diminishing role, (i) leaves ERM, (i) prisoners’ dilemma, (i) public sector borrowers, (i) quantitative easing, (i) quantum mechanics, parallels to economics, (i), (ii) Quantitative Impact Studies (QISs), (i) Rajan, Raghuram, (i) random walk theory, (i) rational expectations, (i) Reagan, Ronald, (i), (ii), (iii), (iv) Regulation Q see United States of America Reigal Neal Interstate Branching Efficiency Act (US, 1997), (i) renmimbi (Chinese currency): depreciation against dollar (1994), (i) repurchase agreements (repos) broker-dealers exploit, (i) collateral, (i), (ii), (iii), (iv) expansion, (i) and foreign borrowing, (i) freeze, (i) fund housing bubble, (i) liquidity, (i) market shrinks in US, (i) as source of investment bank funding, (i) Ricardian equivalence, (i) Ricardian offset, (i) risk models, (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv) risk-weighted assets, (i), (ii), (iii) Rochard, Michel, (i) Rome, Treaty of (1957), (i), (ii), (iii), (iv), (v), (vi) Roosevelt, Franklin D., (i) Roubini, Nouriel, (i) Royal Bank of Scotland (RBS; UK bank), (i) Russia exchange rate collapse (1998), (i) joins WTO, (i) safe haven bankruptcy protection (US), (i) Sanio, Jochen, (i) Santander (Spanish bank) assets expanded, (i) capitalization, (i) international scope, (i) as mega-bank, (i) takeovers, (i) Sants, Hector, (i) Savings and Loans (US), (i), (ii) Scandinavia: monetary union, (i) Schmidt, Helmut, (i), (ii), (iii) Schoales, Myron, (i) Securities and Exchange Commission (SEC; US) and mortgage-backed securities, (i) registers hedge funds, (i) and regulation, (i), (ii), (iii), (iv) Release 47683 widens repurchase agreement collateral, (i) and repo market, (i), (ii), (iii), (iv) securitization and mortgages, (i), (ii), (iii), (iv), (v), (vi) private label, (i) in US, (i), (ii), (iii), (iv), (v), (vi) Security Pacific Corporation (US bank), (i) shadow banks see United States of America share prices: fluctuations and predictions, (i), (ii) Shiller, Robert, (i), (ii), (iii) Silva-Herzog, Jesus, (i) silver: in US money supply, (i) single currency benefits, (i) effect on trade, (i) and macroeconomic shocks, (i) see also Euro area Single European Act (1986), (i), (ii), (iii), (iv), (v) Smith, Adam, (i), (ii) Smithsonian Agreement, (i), (ii) snake currency arrangement (Europe), (i), (ii) Société Générale (French bank): expansion, (i), (ii), (iii) South Korea, (i), (ii), (iii), (iv) Spain borrowing interest rate, (i) caja savings banks, (i) commercial loans, (i) in currency union periphery, (i) ejected from Exchange Rate Mechanism, (i) excessive borrowing, (i) in Exchange Rate Mechanism, (i) expansion in bank assets, (i) and ESM funding to restructure banking system, (i) financial crisis in, (i), (ii), (iii) high interest rates, (i) housing, (i), (ii) included in Euro area, (i) local governments in, (i) reduces fiscal deficit, (i) successful effect of reforms, (i) ten-year bonds, (i) Stability and Growth Pact (SGP, Euro area), (i), (ii), (iii), (iv), (v) Strasbourg summit (of European leaders, 1990), (i), (ii) Strauss-Kahn, Dominique, (i) stressed value-at-risk models (SVARs), (i) Suez (French bank), (i) supply and demand, law of, (i) Sweden in Basel Committee, (i) and currency fluctuations, (i) in Scandinavian monetary union, (i) Switzerland in financial crisis, (i) trade with EMU members, (i) taxes cuts, (i), (ii), (iii), (iv) favor debt over equity, (i) kept low, (i) little effect on private spending, (i) TCW (US bank), (i) Texas: house prices fall, (i) Thailand, (i), (ii), (iii), (iv) Thatcher, Margaret, (i), (ii), (iii), (iv), (v), (vi) trade: affected by single currency, (i) trade balance, (i) Travelers Group (US financial institution), (i) Trichet, Jean-Claude, (i) Trump, Donald elected President, (i) and fiscal stimulus, (i) looser view on bank regulation, (i) proposes tax cuts, (i) UBS (Swiss bank), (i) UniCredit (Italian bank), (i), (ii), (iii) United Kingdom (Britain) bank assets reduced since 2008, (i) banking expansion, (i), (ii) banking system, (i) bond markets, (i) central bank independence, (i) common capital standard agreed with US, (i) core Euro banks expand into, (i) and currency fluctuations, (i) favors larger bank capital buffers, (i) favours EU-wide bank regulator, (i) financial crisis (1866), (i) foreign banks in, (i) foreign investments in, (i) high inflation, (i), (ii) invited to join European Economic Community, (i) joins Exchange Rate Mechanism, (i) large outflows, (i) leaves European Union, (i) leaves Exchange Rate Mechanism, (i), (ii) ‘light touch’ regulation, (i), (ii), (iii), (iv), (v) in North Atlantic financial crisis (2008), (i) opts out of Maastricht Treaty, (i) owns US assets, (i), (ii) product market, (i) rebate from EEC budget, (i) resists monetary union, (i), (ii) scale of banking, (i) separated commercial and investment (merchant) banks, (i) trade with EMU members, (i) see also pound sterling United States of America accepts Basel 3 framework for large banks, (i) accounting practices, (i) adopts new leverage ratio, (i) aggregate spending, (i) anchor regions and peripheries in currency union, (i) and Asian crisis, (i) assets held by European banks, (i), (ii) bank assets reduced since 2008, (i) bank deposits migrate, (i) bank failures and prompt corrective action, (i) bank mergers, (i) bank size compared with Europe, (i) bankers’ morality, (i) banking expansion, (i) banking regulation, (i), (ii), (iii), (iv), (v), (vi), (vii) and Basel 2 accord, (i) in Basel Committee, (i) bond markets, (i) bond yields fall, (i) business cycles, (i) champions internal risk models, (i) common capital standard agreed with UK, (i) consumer price index, (i) core Euro banks expand into, (i) as crisis country, (i) currency as international standard, (i) currency union in, (i), (ii), (iii) debt outflows, (i) deregulation, (i), (ii) devaluation, (i) effect of break-up of Bretton Woods on, (i) effect of post-crisis changes, (i), (ii) Euro area lends to, (i), (ii) European universal banks in, (i), (ii) favors larger bank capital buffers, (i) federal support for banks, (i) federal tax system, (i) financial boom, (i), (ii) financial reform in, (i), (ii) financial system (2002), (i), (ii) floating exchange rates, (i) Flow of Funds data, (i), (ii) fractured banking system, (i) and gold market, (i) high tech boom collapses, (i) house prices, (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x), (xi), (xii), (xiii), (xiv), (xv) imposes surcharge on foreign imports, (i) improved monetary policy, (i) inflation fluctuates, (i), (ii), (iii), (iv), (v), (vi) integrated banking system, (i) interest rates limited, (i), (ii), (iii) investment bank expansion, (i), (ii) investment bank regulation, (i), (ii), (iii) labor market flexibility, (i) and Latin American debt crisis, (i), (ii) misery index, (i) modest recovery from crisis, (i) national (interstate) banks, (i), (ii), (iii), (iv) national price movements, (i) and oil prices, (i) output volatility, (i) policy coordination fades, (i) post 2002 financial boom, (i) product market, (i) recessions (1985–2005), (i) regulation of shadow banks, (i) and repo market, (i) response to crisis, (i) responsibility for macroprudential policies, (i) and risk measures, (i), (ii) securitization, (i), (ii) separates commercial and investment banking, (i), (ii), (iii) shadow banks develop, (i), (ii), (iii), (iv), (v), (vi), (vii), (viii), (ix), (x) and small bank regulation, (i) trade balance, (i), (ii) trade with EMU members, (i) unprepared for financial crisis, (i) United States Federal Reserve Bank belief in market discipline of investment banks, (i), (ii), (iii), (iv), (v) and business cycle, (i) conducts stress tests, (i) cooperation of monetary and fiscal policy, (i) eases rates, (i), (ii) easy financing conditions, (i) emergency funding, (i), (ii) faith in investors’ judgment, (i) helps stabilize markets, (i) and house price boom, (i) and inflation rates, (i) monetary policy, (i) as proposed model for European Central Bank, (i) provides safety net, (i), (ii) regulates mortgage lending standards, (i) and regulation of investment banks, (i), (ii) Regulation Q, (i) regulatory function and practice, (i), (ii), (iii), (iv) response to crisis, (i) and risk models, (i), (ii), (iii) and tax cuts, (i) urges reform of Basel (i), (ii) warns about loans to Latin America, (i) value-at-risk models (VARs), (i) Venezuela, (i) Versailles Treaty (1919), (i) Vietnam War, (i) Volcker, Paul, (i), (ii) rule, (i) Wachovia Corporation (US bank), (i) Wall Street: reform, (i) waterfall investment structures, (i) welfare payments, (i) Wells Fargo (US bank), (i), (ii) Werner Commission Report (Europe) (1970), (i), (ii), (iii) West Germany economic growth, (i) in European Coal and Steel Community, (i) see also Germany White, Bill, (i) White, Harry Dexter, (i) won (S.


pages: 339 words: 94,769

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

If you are walking in a mountainous region and want to get home, always walking downhill will most likely get you to the next valley but not necessarily over the other mountains that lie around it and between you and home. For that, you either need a mental model (i.e., a map) of the topology, so you know where to ascend to get out of the valley, or you need to switch between gradient descent and random walks so you can bounce your way out of the region. Which is, in fact, exactly what the mosquito does in following my scent: It descends when it’s in my plume and random-walks when it has lost the trail or hit an obstacle. AI So that’s nature. What about computers? Traditional software doesn’t work that way—it follows deterministic trees of hard logic: “If this, do that.”

Mosquitoes are closer to plants that follow the sun than to guided missiles. Yet by applying this simple “follow your nose” rule quite literally, they can travel through a house to find you, slip through cracks in a screen door, even zero in on the tiny strip of skin you left exposed between hat and shirt collar. It’s just a random walk, combined with flexible wings and legs that let the insect bounce off obstacles and an instinct to descend a chemical gradient. But “gradient descent” is much more than bug navigation. Look around you and you’ll find it everywhere, from the most basic physical rules of the universe to the most advanced artificial intelligence.

What factors do we want the algorists to have in their often hidden procedures? Education? Income? Employment history? What one has read, watched, visited, or bought? Prior contact with law enforcement? How do we want algorists to weight those factors? Predictive analytics predicated on mechanical objectivity comes at a price. Sometimes it may be a price worth paying; sometimes that price would be devastating for the just society we want to have. More generally, as the convergence of algorithms and Big Data governs a greater and greater part of our lives, it would be well worth keeping in mind these two lessons from the history of the sciences: Judgment is not the discarded husk of a now pure objectivity of self-restraint.


pages: 312 words: 35,664

The Mathematics of Banking and Finance by Dennis W. Cox, Michael A. A. Cox

backpropagation, barriers to entry, Brownian motion, call centre, correlation coefficient, fixed income, G4S, inventory management, iterative process, linear programming, meta-analysis, Monty Hall problem, pattern recognition, random walk, traveling salesman, value at risk

Three possible approaches are: r A subjective approach, or ‘guess work’, which is used when an experiment cannot be easily r repeated, even conceptually. Typical examples of this include horse racing and Brownian motion. Brownian motion represents the random motion of small particles suspended in a gas or liquid and is seen, for example, in the random walk pattern of a drunken man. The classical approach, which is usually adopted if all sample points are equally likely (as is the case in the rolling of a dice as discussed above). The probability may be measured with certainty by analysing the event. Using the same mathematical notation, a mathematical definition of this is: Prob(A) = Number of events classifiable as A Total number of possible events A typical example of such a probability is a lottery.

Index a notation 103–4, 107–20, 135–47 linear regression 103–4, 107–20 slope significance test 112–20 variance 112 abscissa see horizontal axis absolute value, notation 282–4 accuracy and reliability, data 17, 47 adaptive resonance theory 275 addition, mathematical notation 279 addition of normal variables, normal distribution 70 addition rule, probability theory 24–5 additional variables, linear programming 167–70 adjusted cash flows, concepts 228–9 adjusted discount rates, concepts 228–9 Advanced Measurement Approach (AMA) 271 advertising allocation, linear programming 154–7 air-conditioning units 182–5 algorithms, neural networks 275–6 alternatives, decisions 191–4 AMA see Advanced Measurement Approach analysis data 47–52, 129–47, 271–4 Latin squares 131–2, 143–7 linear regression 110–20 projects 190–2, 219–25, 228–34 randomised block design 129–35 sampling 47–52, 129–47 scenario analysis 40, 193–4, 271–4 trends 235–47 two-way classification 135–47 variance 110–20, 121–7 anonimised databases, scenario analysis 273–4 ANOVA (analysis of variance) concepts 110–20, 121–7, 134–47 examples 110–11, 123–7, 134–40 formal background 121–2 linear regression 110–20 randomised block design 134–5, 141–3 tables 110–11, 121–3, 134–47 two-way classification 136–7 appendix 279–84 arithmetic mean, concepts 37–45, 59–60, 65–6, 67–74, 75–81 assets classes 149–57 reliability 17, 47, 215–18, 249–60 replacement of assets 215–18, 249–60 asymptotic distributions 262 ATMs 60 averages see also mean; median; mode concepts 37–9 b notation 103–4, 107–20, 132–5 linear regression 103–4, 107–20 variance 112 back propagation, neural networks 275–7 backwards recursion 179–87 balance sheets, stock 195 bank cashier problem, Monte Carlo simulation 209–12 Bank for International Settlements (BIS) 267–9, 271 banks Basel Accord 262, 267–9, 271 failures 58 loss data 267–9, 271–4 modelling 75–81, 85, 97, 267–9, 271–4 profitable loans 159–66 bar charts comparative data 10–12 concepts 7–12, 54, 56, 59, 205–6, 232–3 discrete data 7–12 examples 9–12, 205–6, 232–3 286 Index bar charts (cont.) narrative explanations 10 relative frequencies 8–12 rules 8–9 uses 7–12, 205–6, 232–3 base rates, trends 240 Basel Accord 262, 267–9, 271 bathtub curves, reliability concepts 249–51 Bayes’theorem, probability theory 27–30, 31 bell-shaped normal distribution see normal distribution bi-directional associative memory 275 bias 1, 17, 47–50, 51–2, 97, 129–35 randomised block design 129–35 sampling 17, 47–50, 51–2, 97, 129–35 skewness 41–5 binomial distribution concepts 55–8, 61–5, 71–2, 98–9, 231–2 examples 56–8, 61–5, 71–2, 98–9 net present value (NPV) 231–2 normal distribution 71–2 Pascal’s triangle 56–7 uses 55, 57, 61–5, 71–2, 98–9, 231–2 BIS see Bank for International Settlements boards of directors 240–1 break-even analysis, concepts 229–30 Brownian motion 22 see also random walks budgets 149–57 calculators, log functions 20, 61 capital Basel Accord 262, 267–9, 271 cost of capital 219–25, 229–30 cash flows adjusted cash flows 228–9 future cash flows 219–25, 227–34, 240–1 net present value (NPV) 219–22, 228–9, 231–2 standard deviation 232–4 central limit theorem concepts 70, 75 examples 70 chi-squared test concepts 83–4, 85, 89, 91–5 contingency tables 92–5 examples 83–4, 85, 89, 91–2 goodness of fit test 91–5 multi-way tables 94–5 tables 84, 91 Chu Shi-Chieh’s Ssu Yuan Y Chien 56 circles, tree diagrams 30–5 class intervals concepts 13–20, 44–5, 63–4, 241–7 histograms 13–20, 44–5 mean calculations 44–5 mid-points 44–5, 241–7 notation 13–14, 20 Sturges’s formula 20 variance calculations 44–5 classical approach, probability theory 22, 27 cluster sampling 50 coin-tossing examples, probability theory 21–3, 53–4 collection techniques, data 17, 47–52, 129–47 colours, graphical presentational approaches 9 combination, probability distribution (density) functions 54–8 common logarithm (base 10) 20 communications, decisions 189–90 comparative data, bar charts 10–12 comparative histograms see also histograms examples 14–19 completed goods 195 see also stock . . . conditional probability, concepts 25–7, 35 confidence intervals, concepts 71, 75–81, 105, 109, 116–20, 190, 262–5 constraining equations, linear programming 159–70 contingency tables, concepts 92–5 continuous approximation, stock control 200–1 continuous case, failures 251 continuous data concepts 7, 13–14, 44–5, 65–6, 251 histograms 7, 13–14 continuous uniform distribution, concepts 64–6 correlation coefficient concepts 104–20, 261–5, 268–9 critical value 105–6, 113–20 equations 104–5 examples 105–8, 115–20 costs capital 219–25, 229–30 dynamic programming 180–82 ghost costs 172–7 holding costs 182–5, 197–201, 204–8 linear programming 167–70, 171–7 sampling 47 stock control 182–5, 195–201 transport problems 171–7 trend analysis 236–47 types 167–8, 182 counting techniques, probability distribution (density) functions 54 covariance see also correlation coefficient concepts 104–20, 263–5 credit cards 159–66, 267–9 credit derivatives 97 see also derivatives Index credit risk, modelling 75, 149, 261–5 critical value, correlation coefficient 105–6, 113–20 cumulative frequency polygons concepts 13–20, 39–40, 203 examples 14–20 uses 13–14 current costs, linear programming 167–70 cyclical variations, trends 238–47 data analysis methods 47–52, 129–47, 271–4 collection techniques 17, 47–52, 129–47 continuous/discrete types 7–12, 13–14, 44–5, 53–5, 65–6, 72, 251 design/approach to analysis 129–47 errors 129–47 graphical presentational approaches 1–20, 149–57 identification 2–5, 261–5 Latin squares 131–2, 143–7 loss data 267–9, 271–4 neural networks 275–7 qualities 17, 47 randomised block design 129–35 reliability and accuracy 17, 47 sampling 17, 47–52 time series 235–47 trends 5, 10, 235–47 two-way classification analysis 135–47 data points, scatter plots 2–5 databases, loss databases 272–4 debentures 149–57 decisions alternatives 191–4 Bayes’theorem 27–30, 31 communications 189–90 concepts 21–35, 189–94, 215–25, 228–34, 249–60 courses of action 191–2 definition 21 delegation 189–90 empowerment 189–90 guesswork 191 lethargy pitfalls 189 minimax regret rule 192–4 modelling problems 189–91 Monty Hall problem 34–5, 212–13 pitfalls 189–94 probability theory 21–35, 53–66, 189–94, 215–18 problem definition 129, 190–2 project analysis guidelines 190–2, 219–25, 228–34 replacement of assets 215–18, 249–60 staff 189–90 287 steps 21 stock control 195–201, 203–8 theory 189–94 degrees of freedom 70–1, 75–89, 91–5, 110–20, 136–7 ANOVA (analysis of variance) 110–20, 121–7, 136–7 concepts 70–1, 75–89, 91–5, 110–20, 136–7 delegation, decisions 189–90 density functions see also probability distribution (density) functions concepts 65–6, 67, 83–4 dependent variables, concepts 2–5, 103–20, 235 derivatives 58, 97–8, 272 see also credit . . . ; options design/approach to analysis, data 129–47 dice-rolling examples, probability theory 21–3, 53–5 differentiation 251 discount factors adjusted discount rates 228–9 net present value (NPV) 220–1, 228–9, 231–2 discrete data bar charts 7–12, 13 concepts 7–12, 13, 44–5, 53–5, 72 discrete uniform distribution, concepts 53–5 displays see also presentational approaches data 1–5 Disraeli, Benjamin 1 division notation 280, 282 dynamic programming complex examples 184–7 concepts 179–87 costs 180–82 examples 180–87 principle of optimality 179–87 returns 179–80 schematic 179–80 ‘travelling salesman’ problem 185–7 e-mail surveys 50–1 economic order quantity see also stock control concepts 195–201 examples 196–9 empowerment, staff 189–90 error sum of the squares (SSE), concepts 122–5, 133–47 errors, data analysis 129–47 estimates mean 76–81 probability theory 22, 25–6, 31–5, 75–81 Euler, L. 131 288 Index events independent events 22–4, 35, 58, 60, 92–5 mutually exclusive events 22–4, 58 probability theory 21–35, 58–66, 92–5 scenario analysis 40, 193–4, 271–4 tree diagrams 30–5 Excel 68, 206–7 exclusive events see mutually exclusive events expected errors, sensitivity analysis 268–9 expected value, net present value (NPV) 231–2 expert systems 275 exponent notation 282–4 exponential distribution, concepts 65–6, 209–10, 252–5 external fraud 272–4 extrapolation 119 extreme value distributions, VaR 262–4 F distribution ANOVA (analysis of variance) 110–20, 127, 134–7 concepts 85–9, 110–20, 127, 134–7 examples 85–9, 110–20, 127, 137 tables 85–8 f notation 8–9, 13–20, 26, 38–9, 44–5, 65–6, 85 factorial notation 53–5, 283–4 failure probabilities see also reliability replacement of assets 215–18, 249–60 feasibility polygons 152–7, 163–4 finance selection, linear programming 164–6 fire extinguishers, ANOVA (analysis of variance) 123–7 focus groups 51 forward recursion 179–87 four by four tables 94–5 fraud 272–4, 276 Fréchet distribution 262 frequency concepts 8–9, 13–20, 37–45 cumulative frequency polygons 13–20, 39–40, 203 graphical presentational approaches 8–9, 13–20 frequentist approach, probability theory 22, 25–6 future cash flows 219–25, 227–34, 240–1 fuzzy logic 276 Garbage In, Garbage Out (GIGO) 261–2 general rules, linear programming 167–70 genetic algorithms 276 ghost costs, transport problems 172–7 goodness of fit test, chi-squared test 91–5 gradient (a notation), linear regression 103–4, 107–20 graphical method, linear programming 149–57, 163–4 graphical presentational approaches concepts 1–20, 149–57, 235–47 rules 8–9 greater-than notation 280–4 Greek alphabet 283 guesswork, modelling 191 histograms 2, 7, 13–20, 41, 73 class intervals 13–20, 44–5 comparative histograms 14–19 concepts 7, 13–20, 41, 73 continuous data 7, 13–14 examples 13–20, 73 skewness 41 uses 7, 13–20 holding costs 182–5, 197–201, 204–8 home insurance 10–12 Hopfield 275 horizontal axis bar charts 8–9 histograms 14–20 linear regression 103–4, 107–20 scatter plots 2–5, 103 hypothesis testing concepts 77–81, 85–95, 110–27 examples 78–80, 85 type I and type II errors 80–1 i notation 8–9, 13–20, 28–30, 37–8, 103–20 identification data 2–5, 261–5 trends 241–7 identity rule 282 impact assessments 21, 271–4 independent events, probability theory 22–4, 35, 58, 60, 92–5 independent variables, concepts 2–5, 70, 103–20, 235 infinity, normal distribution 67–72 information, quality needs 190–4 initial solution, linear programming 167–70 insurance industry 10–12, 29–30 integers 280–4 integration 65–6, 251 intercept (b notation), linear regression 103–4, 107–20 interest rates base rates 240 daily movements 40, 261 project evaluation 219–25, 228–9 internal rate of return (IRR) concepts 220–2, 223–5 examples 220–2 interpolation, IRR 221–2 interviews, uses 48, 51–2 inventory control see stock control Index investment strategies 149–57, 164–6, 262–5 IRR see internal rate of return iterative processes, linear programming 170 j notation 28–30, 37, 104–20, 121–2 JP Morgan 263 k notation 20, 121–7 ‘know your customer’ 272 Kohonen self-organising maps 275 Latin squares concepts 131–2, 143–7 examples 143–7 lead times, stock control 195–201 learning strategies, neural networks 275–6 less-than notation 281–4 lethargy pitfalls, decisions 189 likelihood considerations, scenario analysis 272–3 linear programming additional variables 167–70 concepts 149–70 concerns 170 constraining equations 159–70 costs 167–70, 171–7 critique 170 examples 149–57, 159–70 finance selection 164–6 general rules 167–70 graphical method 149–57, 163–4 initial solution 167–70 iterative processes 170 manual preparation 170 most profitable loans 159–66 optimal advertising allocation 154–7 optimal investment strategies 149–57, 164–6 returns 149–57, 164–6 simplex method 159–70, 171–2 standardisation 167–70 time constraints 167–70 transport problems 171–7 linear regression analysis 110–20 ANOVA (analysis of variance) 110–20 concepts 3, 103–20 equation 103–4 examples 107–20 gradient (a notation) 103–4, 107–20 intercept (b notation) 103–4, 107–20 interpretation 110–20 notation 103–4 residual sum of the squares 109–20 slope significance test 112–20 uncertainties 108–20 literature searches, surveys 48 289 loans finance selection 164–6 linear programming 159–66 risk assessments 159–60 log-normal distribution, concepts 257–8 logarithms (logs), types 20, 61 losses, banks 267–9, 271–4 lotteries 22 lower/upper quartiles, concepts 39–41 m notation 55–8 mail surveys 48, 50–1 management information, graphical presentational approaches 1–20 Mann–Whitney test see U test manual preparation, linear programming 170 margin of error, project evaluation 229–30 market prices, VaR 264–5 marketing brochures 184–7 mathematics 1, 7–8, 196–9, 219–20, 222–5, 234, 240–1, 251, 279–84 matrix plots, concepts 2, 4–5 matrix-based approach, transport problems 171–7 maximum and minimum, concepts 37–9, 40, 254–5 mean comparison of two sample means 79–81 comparisons 75–81 concepts 37–45, 59–60, 65–6, 67–74, 75–81, 97–8, 100–2, 104–27, 134–5 confidence intervals 71, 75–81, 105, 109, 116–20, 190, 262–5 continuous data 44–5, 65–6 estimates 76–81 hypothesis testing 77–81 linear regression 104–20 normal distribution 67–74, 75–81, 97–8 sampling 75–81 mean square causes (MSC), concepts 122–7, 134–47 mean square errors (MSE), ANOVA (analysis of variance) 110–20, 121–7, 134–7 median, concepts 37, 38–42, 83, 98–9 mid-points class intervals 44–5, 241–7 moving averages 241–7 minimax regret rule, concepts 192–4 minimum and maximum, concepts 37–9, 40 mode, concepts 37, 39, 41 modelling banks 75–81, 85, 97, 267–9, 271–4 concepts 75–81, 83, 91–2, 189–90, 195–201, 215–18, 261–5 decision-making pitfalls 189–91 economic order quantity 195–201 290 Index modelling (cont.) guesswork 191 neural networks 275–7 operational risk 75, 262–5, 267–9, 271–4 output reviews 191–2 replacement of assets 215–18, 249–60 VaR 261–5 moments, density functions 65–6, 83–4 money laundering 272–4 Monte Carlo simulation bank cashier problem 209–12 concepts 203–14, 234 examples 203–8 Monty Hall problem 212–13 queuing problems 208–10 random numbers 207–8 stock control 203–8 uses 203, 234 Monty Hall problem 34–5, 212–13 moving averages concepts 241–7 even numbers/observations 244–5 moving totals 245–7 MQMQM plot, concepts 40 MSC see mean square causes MSE see mean square errors multi-way tables, concepts 94–5 multiplication notation 279–80, 282 multiplication rule, probability theory 26–7 multistage sampling 50 mutually exclusive events, probability theory 22–4, 58 n notation 7, 20, 28–30, 37–45, 54–8, 103–20, 121–7, 132–47, 232–4 n!

Pascal’s triangle, concepts 56–7 payback period, concepts 219, 222–5 PCs see personal computers people costs 167–70 perception 275 permutation, probability distribution (density) functions 55 personal computers (PCs) 7–9, 58–60, 130–1, 198–9, 208–10, 215–18, 253–5 pictures, words 1 pie charts concepts 7, 12 critique 12 examples 12 planning, data collection techniques 47, 51–2 plot, concepts 1, 10 plus or minus sign notation 279 Poisson distribution concepts 58–66, 72–3, 91–2, 200–1, 231–2 examples 58–65, 72–3, 91–2, 200–1 net present value (NPV) 231–2 normal distribution 72–3 stock control 200–1 suicide attempts 62–5 uses 57, 60–5, 72–3, 91–2, 200–1, 231–2 291 population considerations, sampling 47, 49–50, 75–95, 109, 121–7 portfolio investments, VaR 262–5 power notation 282–4 predictions, neural networks 276–7 presentational approaches concepts 1–20 good presentation 1–2 management information 1–20 rules 8–9 trends 5, 10, 235–47 price/earnings ratio (P/E ratio), concepts 222 principle of optimality, concepts 179–87 priors, concepts 28–30 Prob notation 21–35, 68–70, 254–5 probability distribution (density) functions see also normal distribution binomial distribution 55–8, 61–5, 71–2, 98–9, 231–2 combination 54–8 concepts 53–95, 203, 205, 231–2, 257–60 continuous uniform distribution 64–6 counting techniques 54 discrete uniform distribution 53–5, 72 examples 53–5 exponential distribution 65–6, 209–10, 252–5 log-normal distribution 257–8 net present value (NPV) 231–2 permutation 55 Poisson distribution 58–66, 72–3, 91–2, 200–1, 231–2 probability theory addition rule 24–5 Bayes’theorem 27–30, 31 classical approach 22, 27 coin-tossing examples 21–3, 53–4 concepts 21–35, 53–66, 200–1, 203, 215–18, 231–2 conditional probability 25–7, 35 decisions 21–35, 53–66, 189–94, 215–18 definitions 22 dice-rolling examples 21–3, 53–5 estimates 22, 25–6, 32–5, 75–81 event types 22–4 examples 25–35, 53–5 frequentist approach 22, 25–6 independent events 22–4, 35, 58, 60, 92–5 Monty Hall problem 34–5, 212–13 multiplication rule 26–7 mutually exclusive events 22–4, 58 notation 21–2, 24–30, 54–5, 68–9, 75–6, 79–81, 83–5, 99–104, 121–2, 131–5, 185–7, 254–5 overlapping probabilities 25 simple examples 21–2 subjective approach 22 292 Index probability theory (cont.) tree diagrams 30–5 Venn diagrams 23–4, 28 problems, definition importance 129, 190–2 process costs 167–70 production runs 184–7 products awaiting shipment 195 see also stock . . . profit and loss accounts, stock 195 profitable loans, linear programming 159–66 projects see also decisions alternatives 191–4, 219–25 analysis guidelines 190–2, 219–25, 228–34 break-even analysis 229–30 courses of action 191–2 evaluation methods 219–25, 227, 228–34 finance issues 164–6 guesswork 191 IRR 220–2, 223–5 margin of error 229–30 net present value (NPV) 219–22, 228–9, 231–2 P/E ratio 222 payback period 219, 222–5 returns 164–6, 219–25, 227–34 sponsors 190–2 quality control 61–4 quality needs, information 190–4 quartiles, concepts 39–41 questionnaires, surveys 48, 50–1 questions, surveys 48, 51–2, 97 queuing problems, Monte Carlo simulation 208–10 quota sampling 50 r! notation 54–5 r notation 104–20, 135–47 random numbers, Monte Carlo simulation 207–8 random samples 49–50 random walks see also Brownian motion concepts 22 randomised block design ANOVA (analysis of variance) 134–5, 141–3 concepts 129–35 examples 130–1, 140–3 parameters 132–5 range, histograms 13–20 ranks, U test 99–102 raw materials 195 see also stock . . . reciprocals, numbers 280–4 recursive calculations 56–8, 61–2, 179–87 regrets, minimax regret rule 192–4 relative frequencies, concepts 8–12, 14–20 relevance issues, scenario analysis 272, 273–4 reliability bathtub curves 249–51 concepts 17, 47, 215–18, 249–60 continuous case 251 data 17, 47 definition 251 examples 249 exponential distribution 252–5 obsolescence 215–18 systems/components 215–18, 249–60 Weibull distribution 255–7, 262 reorder levels, stock control 195–201 replacement of assets 215–18, 249–60 reports, formats 1 residual sum of the squares, concepts 109–20, 121–7, 132–47 returns dynamic programming 179–80 IRR 220–2, 223–5 linear programming 149–57, 164–6 net present value (NPV) 219–22, 228–9, 231–2 optimal investment strategies 149–57, 164–6 P/E ratio 222 payback period 219, 222–5 projects 164–6, 219–25, 227–34 risk/uncertainty 227–34 risk adjusted cash flows 228–9 adjusted discount rates 228–9 Basel Accord 262, 267–9, 271 concepts 28–30, 159–66, 227–34, 261–5, 267–9, 271–4 definition 227 loan assessments 159–60 management 28–30, 159–66, 227–34, 261–5, 267–9, 271–4 measures 232–4, 271–4 net present value (NPV) 228–9, 231–2 operational risk 27–8, 75, 262–5, 267–9, 271–4 profiles 268–9 scenario analysis 40, 193–4, 271–4 sensitivity analysis 40, 264–5, 267–9 uncertainty 227–34 VaR 261–5 RiskMetrics 263 rounding 281–4 sample space, Venn diagrams 23–4, 28 sampling see also surveys analysis methods 47–52, 129–47 bad examples 50–1 bias 17, 47–50, 51–2, 97, 129–35 cautionary notes 50–2 Index central limit theorem 70, 75 cluster sampling 50 comparison of two sample means 79–81 concepts 17, 47–52, 70, 75–95, 109, 121–7, 129–47 costs 47 hypothesis testing 77–81, 85–95 mean 75–81 multistage sampling 50 normal distribution 70–1, 75–89 planning 47, 51–2 population considerations 47, 49–50, 75–95, 109, 121–7 problems 50–2 questionnaires 48, 50–1 quota sampling 50 random samples 49–50 selection methods 49–50, 77 size considerations 49, 77–8, 129 stratified sampling 49–50 systematic samples 49–50 units of measurement 47 variables 47 variance 75–81, 83–9, 91–5 scaling, scenario analysis 272 scatter plots concepts 1–5, 103–4 examples 2–3 uses 3–5, 103–4 scenario analysis anonimised databases 273–4 concepts 40, 193–4, 271–4 likelihood considerations 272–3 relevance issues 272, 273–4 scaling 272 seasonal variations, trends 236–40, 242–7 security costs 167–70 selection methods, sampling 49–50, 77 semantics 33–4 sensitivity analysis, concepts 40, 264–5, 267–9 sets, Venn diagrams 23–4, 28 sign test, concepts 98–9 significant digits 281–4 simplex method, linear programming 159–70, 171–2 simulation, Monte Carlo simulation 203–14, 234 size considerations, sampling 49, 77–8, 129 skewness, concepts 41–5 slope significance test see also a notation (gradient) linear regression 112–20 software packages reports 1 stock control 198–9 293 sponsors, projects 190–2 spread, standard deviation 41–5 square root 282–4 SS see sum of the squares SSC see sum of the squares for the causes SST see sum of the squares of the deviations Ssu Yuan Y Chien (Chu Shi-Chieh) 56 staff decision-making processes 189–90 training needs 189 standard deviation see also variance cash flows 232–4 concepts 41–5, 67–81, 83, 97–8, 102, 104–20, 232–4 correlation coefficient 104–20 examples 42–5, 232–4 normal distribution 67–81, 83, 97–8, 102, 232–4 uses 41–5, 104–20, 232–4 standard terms, statistics 37–45 standardisation, linear programming 167–70 statistical terms 1, 37–45 concepts 1, 37–45 maximum and minimum 37–9, 40, 254–5 mean 37–45 median 37, 38–42 mode 37, 39, 41 MQMQM plot 40 skewness 41–5 standard deviation 41–5 standard terms 37–45 upper/lower quartiles 39–41 variance 41–5 statistics, concepts 1, 37–45 std(x) notation (standard deviation) 42–5 stock control concepts 195–201, 203–8 continuous approximation 200–1 costs 182–5, 195–201 economic order quantity 195–201 holding costs 182–5, 197–201, 204–8 lead times 195–201 Monte Carlo simulation 203–8 non-zero lead times 199–201 order costs 197–201 Poisson distribution 200–1 variable costs 197–201 volume discounts 199–201 stock types 195 stratified sampling 49–50 Sturges’s formula 20 subjective approach, probability theory 22 subtraction notation 279 successful products, tree diagrams 30–3 suicide attempts, Poisson distribution 62–5 294 Index sum of the squares for the causes (SSC), concepts 122–5, 133–47 sum of the squares of the deviations (SST), concepts 122–5 sum of the squares (SS) ANOVA (analysis of variance) 110–20, 121–7, 134–5 concepts 109–20, 121–7, 132, 133–47 supply/demand matrix 171–7 surveys see also sampling bad examples 50–1 cautionary notes 50–2 concepts 48–52, 97 interviews 48, 51–2 literature searches 48 previous surveys 48 problems 50–2 questionnaires 48, 50–1 questions 48, 51–2, 97 symmetry, skewness 41–5, 67–9, 75–6, 98–9 systematic errors 130 systematic samples 49–50 systems costs 167–70 t statistic concepts 75–81, 97–8, 114–20, 121, 123, 125, 127 tables 75, 81 tables ANOVA (analysis of variance) 110–11, 121–3, 134–47 chi-squared test 84, 91 contingency tables 92–5 F distribution 85–8 normal distribution 67, 74 t statistic 75, 81 tabular formats, reports 1 telephone surveys 50–2 text surveys 50–1 three-dimensional graphical representations 9 time constraints, linear programming 167–70 time series concepts 235–47 cyclical variations 238–47 mathematics 240–1 moving averages 241–7 seasonal variations 236–40, 242–7 Z charts 245–7 trade finance 164–6 training needs, communications 189 transport problems concepts 171–7 ghost costs 172–7 ‘travelling salesman’ problem, dynamic programming 185–7 tree diagrams examples 30–4 Monty Hall problem 34–5 probability theory 30–5 trends analysis 235–47 concepts 235–47 cyclical variations 238–47 graphical presentational approaches 5, 10, 235–47 identification 241–7 mathematics 240–1 moving averages 241–7 seasonal variations 236–40, 242–7 Z charts 245–7 true rank, U test 99–102 truncated normal distribution, concepts 259, 260 truncation concepts, scatter plots 2–3 Twain, Mark 1 two-tail test, hypothesis testing 77–81, 109 two-way classification analysis 135–47 examples 137–40 type I and type II errors examples 80–1 hypothesis testing 80–1 U test, concepts 99–102 uncertainty concepts 108–20, 227–34 definition 227 linear regression 108–20 net present value (NPV) 228–9, 231–2 risk 227–34 upper/lower quartiles, concepts 39–41 valuations, options 58, 97–8 value at risk (VaR) calculation 264–5 concepts 261–5 examples 262–3 extreme value distributions 262–4 importance 261 variable costs, stock control 197–201 variables bar charts 7–8 concepts 2–3, 7–12, 13–14, 27–35, 70, 103–20, 121–7 continuous/discrete types 7–12, 13–14, 44–5, 53–5, 65–6, 72, 251 correlation coefficient 104–20 data collection techniques 47 dependent/independent variables 2–5, 70, 103–20, 235 histograms 13–20 Index linear programming 149–70 linear regression 3, 103–20 scatter plots 2–5 sensitivity analysis 40, 264–5, 267–9 variance see also standard deviation ANOVA (analysis of variance) 110–20, 121–7, 134–47 chi-squared test 83–4, 85, 89, 91–5 comparisons 83–9 concepts 41–5, 65–6, 67, 75–81, 83–9, 91–5, 102, 104, 107, 110–20, 233–4, 263–5 continuous data 44–5 covariance 104–20, 263–5 examples 42–5 F distribution 85–9, 110–20, 127, 134–7 linear regression 104, 107, 110–20 var(x) notation (variance) 42–5, 233–4 VC see venture capital 295 Venn diagrams, concepts 23–4, 28 venture capital (VC) 149–57 vertical axis bar charts 8–9 histograms 14–20 linear regression 103–4, 107–20 scatter plots 2–5, 103 volume discounts, stock control 199–201 Weibull distribution concepts 255–7, 262 examples 256–7 ‘what/if’ analysis 33 Wilcoxon test see U test words, pictures 1 work in progress 195 Z charts, concepts 245–7 z notation 67–74, 102, 105, 234 zero factorial 54–5 Index compiled by Terry Halliday


pages: 289 words: 113,211

A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber

affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, behavioural economics, Black Swan, Black-Scholes formula, Bonfire of the Vanities, book value, butterfly effect, commoditize, commodity trading advisor, computer age, computerized trading, disintermediation, diversification, double entry bookkeeping, Edward Lorenz: Chaos theory, Edward Thorp, family office, financial engineering, financial innovation, fixed income, frictionless, frictionless market, Future Shock, George Akerlof, global macro, implied volatility, index arbitrage, intangible asset, Jeff Bezos, Jim Simons, John Meriwether, junk bonds, London Interbank Offered Rate, Long Term Capital Management, loose coupling, managed futures, margin call, market bubble, market design, Mary Meeker, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shock, Paul Samuelson, Pierre-Simon Laplace, proprietary trading, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk/return, Robert Shiller, Robert Solow, rolodex, Saturday Night Live, selection bias, shareholder value, short selling, Silicon Valley, statistical arbitrage, tail risk, The Market for Lemons, time value of money, too big to fail, transaction costs, tulip mania, uranium enrichment, UUNET, William Langewiesche, yield curve, zero-coupon bond, zero-sum game

Each stock was paired with another stock, so only company-specific information would affect the relative pricing of the pair. Any broader information would make both stocks move, and the relative value of the pair would remain unchanged. The company-specific effects could be diversified away by holding many pairs since they would be independent from one company to another. In this way Bamberger’s trading strategy gave obeisance to efficient markets. He assumed information would move prices as a random walk, but he constructed the portfolio in a way that negated the impact of that information, at least in a statistical sense.

In an age in which people are willing to invest money in virtual stocks, where by definition there are no prospects of earnings and where price appreciation is obtained through nothing short of an unsustainable bubble, it is not too hard to see how a real dot-com, with real prospects, no matter how dim, could attract investors. Market bubbles have been explained by the tendency of investors to follow trends and by the dynamics of crowd psychology—the need for people to be part of a successful herd. But neither trend-following strategies nor irrational crowd behavior is necessary to create market bubbles. Even if we assume as a starting point that the stock market is a random walk and 168 ccc_demon_165-206_ch09.qxd 7/13/07 2:44 PM Page 169 T H E B R AV E N E W W O R L D OF HEDGE FUNDS is governed by rational behavior, and even if we assert at the outset that all trades reflect the full consideration of the most up-to-date information, merely the fact that there are winners and losers will lead to booms and busts that have little to do with the rational application of information.1 The simplest market cycle is based on two psychological characteristics of investors.

Markets are efficient, which is to say that they react immediately and appropriately to 210 ccc_demon_207-242_ch10.qxd 2/13/07 1:47 PM COCKROACHES AND Page 211 HEDGE FUNDS all relevant information; when the news comes out, they adjust instantly. Since information coming into a market is by definition unknown and random, and since the market reacts fully and immediately to this new information, market prices move about randomly. From this comes the assertion that the market is a random walk. Full adherence to the efficient markets hypothesis leaves much of the financial industry in a paradoxical position. It is precisely the activity of the many people trying to track down information to make profitable trades that leads the markets to be efficient.


pages: 326 words: 97,089

Five Billion Years of Solitude: The Search for Life Among the Stars by Lee Billings

addicted to oil, Albert Einstein, Anthropocene, Apollo 11, Arthur Eddington, California gold rush, Colonization of Mars, cosmological principle, cuban missile crisis, dark matter, Dava Sobel, double helix, Eddington experiment, Edmond Halley, Ford Model T, full employment, Hans Moravec, hydraulic fracturing, index card, Isaac Newton, James Webb Space Telescope, Johannes Kepler, Kuiper Belt, Late Heavy Bombardment, low earth orbit, Magellanic Cloud, music of the spheres, Neil Armstrong, out of africa, Peter H. Diamandis: Planetary Resources, planetary scale, private spaceflight, profit motive, quantitative trading / quantitative finance, Ralph Waldo Emerson, RAND corporation, random walk, Search for Extraterrestrial Intelligence, Searching for Interstellar Communications, selection bias, Silicon Valley, space junk, synthetic biology, technological singularity, the scientific method, transcontinental railway

He would place magnets of various strengths and shapes strategically upon the square and give the pendulum a gentle bump; it would swing to and fro for long periods, kicking between magnetic fields with sufficient force to overcome the frictional loss of momentum from moving through the air. Its motions followed a chaotic random walk, never exactly repeating any given path. Laughlin savored the toy for how its complex behavior could unfold solely from the simple initial conditions of each magnet’s position and the strength and trajectory of an initiatory nudge. It reminded him of his struggles to predict the typical outcomes that emerged from the chaotic gravitational interactions of black holes, stars, and planets, and his efforts to squeeze faint signals from backgrounds of meaningless noise.

He sighed, cursed, produced a turkey sandwich from his bag, and ate it with resignation. “This seems excessive, even for the associate director,” Wiktorowicz said between bites. “It’s like the telescope just lost its mind. Maybe the ghost of that French guy with dysentery is trying to stick it to us.” “I think it just drifted into a rough mood and needed to cool off with a random walk,” Zachary said. “Cheer up, Sloane. We’re gonna help it find itself.” Laughlin and I excused ourselves to go watch the transit from the parking lot beneath the suddenly clear sky. As we left I glanced again at the netbook’s video feed from Mauna Kea. Wiktorowicz was staring dejectedly at the screen, slowly chewing another turkey sandwich.

He wanted me to be independent, and just didn’t think it was a good career choice.” Seager’s father valued practicality, but time and time again, he told her she must think big, set goals, and visualize herself reaching them. Otherwise, she should not expect success. Despite that advice, Seager often described her early path toward astronomy as an unfocused “random walk,” like that of a photon bouncing chaotically around the seething heart of a star. She appeased her father by first concentrating on physics, reasoning that would boost her chances of employment both within and outside of academia, but the more she learned, the less interest she could muster. “I believed you could perfectly describe everything with equations,” she said.


pages: 224 words: 13,238

Electronic and Algorithmic Trading Technology: The Complete Guide by Kendall Kim

algorithmic trading, automated trading system, backtesting, Bear Stearns, business logic, commoditize, computerized trading, corporate governance, Credit Default Swap, diversification, en.wikipedia.org, family office, financial engineering, financial innovation, fixed income, index arbitrage, index fund, interest rate swap, linked data, market fragmentation, money market fund, natural language processing, proprietary trading, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, short selling, statistical arbitrage, Steven Levy, transaction costs, yield curve

Aite Group Report 20050328, March 2005: 16–17. Lori Master, White Paper: ‘‘ECN Aggregators—Increasing Transparency and Liquidity in Equity Markets,’’ Random Walk Computing, Fall 2004: 6–8. Automating Trade and Order Flow 25 . Send blocks of 50,000 shares through a broker dealer to satisfy soft dollar agreements such as sell-side research, etc. . Utilize an algorithm such as Volume-Weighted Average Price (VWAP) and let the algorithm judge the patterns, and smart routing features will search for the best firm price available at the time of each order. 5. The executing trading desks would send back the execution information to the trader’s OMS.

Under NYSE Rule 80A, if the DJIA moves up or down 2% from the previous closing value, program trading orders to buy or sell the Standard & Poor’s 500 stocks as part of index arbitrage strategies must be entered with directions to have the order executions effected in a manner that stabilizes share prices. The collar restrictions are lifted if the DJIA returns to or within 1% of its previous closing value. The futures exchanges set the price limits that aim to lessen sharp price swings in contracts, such as stock index futures. A price limit does not stop trading in the futures, but prohibits trading at prices below the preset limit during a price decline. Intraday price limits are removed when preset times during the trading session, such as 10 minutes after the threshold, are reached. Daily price limits remain in effect for the entire trading session. Specific price limits are set by the exchanges for each stock index futures contract.

H high-touch trading trades in which prices are quoted over the phone. I implementation shortfall André Perold defines implementation shortfall as the difference in return between a theoretical portfolio and the implemented portfolio.3 In a paper portfolio, a portfolio manager looks at prevailing prices, in relation to execution prices in an actual portfolio. Implementation shortfall measures the price distance between the final, realized trade price, and a pre-trade decision price. implicit cost the price at which an investor or money manager can purchase an asset (the dealer’s asking price) and the price at which you can sell the same asset at the same point in time (the dealer’s bid price).


pages: 681 words: 64,159

Numpy Beginner's Guide - Third Edition by Ivan Idris

algorithmic trading, business intelligence, Conway's Game of Life, correlation coefficient, data science, Debian, discrete time, en.wikipedia.org, functional programming, general-purpose programming language, Khan Academy, p-value, random walk, reversible computing, time value of money

Print the minimum and maximum of the outcome, just to make sure we don't have any strange outliers: for i in range(1, len(cash)): if outcome[i] < 5: cash[i] = cash[i - 1] - 1 elif outcome[i] < 10: cash[i] = cash[i - 1] + 1 else: raise AssertionError("Unexpected outcome " + outcome) print(outcome.min(), outcome.max()) As expected, the values are between 0 and 9 . In the following diagram, you can see the cash balance performing a random walk: What just happened? We did a random walk experiment using the binomial() functon from the NumPy random module (see headortail.py ): from __future__ import print_function import numpy as np import matplotlib.pyplot as plt cash = np.zeros(10000) cash[0] = 1000 np.random.seed(73) outcome = np.random.binomial(9, 0.5, size=len(cash)) for i in range(1, len(cash)): if outcome[i] < 5: cash[i] = cash[i - 1] - 1 elif outcome[i] < 10: cash[i] = cash[i - 1] + 1 else: raise AssertionError("Unexpected outcome " + outcome) print(outcome.min(), outcome.max()) plt.plot(np.arange(len(cash)), cash) plt.title('Binomial simulation') plt.xlabel('# Bets') plt.ylabel('Cash') plt.grid() plt.show() Hypergeometric distribution The hypergeometric distributon models a jar with two types of objects in it.

On-balance volume Volume is a very important variable in investng; it indicates how big a price move is. The on-balance volume indicator is one of the simplest stock price indicators. It is based on the close price of the current and previous days and the volume of the current day. For each day, if the close price today is higher than the close price of yesterday, then the value of the on-balance volume is equal to the volume of today. On the other hand, if today's close price is lower than yesterday's close price, then the value of the on-balance volume indicator is the diference between the on-balance volume and the volume of today. However, if the close price did not change, then the value of the on-balance volume is zero.

Let's see it in acton: print("mean =", np.mean(c)) As a result, we get the following printout: mean = 351.037666667 Time-weighted average price In fnance, tme-weighted average price (TWAP) is another average price measure. Now that we are at it, let's compute the TWAP too. It is just a variaton on a theme really. The idea is that recent price quotes are more important, so we should give recent prices higher weights. The easiest way is to create an array with the arange() functon of increasing values from zero to the number of elements in the close price array. This is not necessarily the correct way. In fact, most of the examples concerning stock price analysis in this book are only illustratve.


pages: 239 words: 69,496

The Wisdom of Finance: Discovering Humanity in the World of Risk and Return by Mihir Desai

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, AOL-Time Warner, assortative mating, Benoit Mandelbrot, book value, Brownian motion, capital asset pricing model, Carl Icahn, carried interest, Charles Lindbergh, collective bargaining, corporate governance, corporate raider, discounted cash flows, diversified portfolio, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, follow your passion, George Akerlof, Gordon Gekko, greed is good, housing crisis, income inequality, information asymmetry, Isaac Newton, Jony Ive, Kenneth Rogoff, longitudinal study, Louis Bachelier, low interest rates, Monty Hall problem, moral hazard, Myron Scholes, new economy, out of africa, Paul Samuelson, Pierre-Simon Laplace, principal–agent problem, Ralph Waldo Emerson, random walk, risk/return, Robert Shiller, Ronald Coase, short squeeze, Silicon Valley, Steve Jobs, Thales and the olive presses, Thales of Miletus, The Market for Lemons, The Nature of the Firm, The Wealth of Nations by Adam Smith, Tim Cook: Apple, tontine, transaction costs, vertical integration, zero-sum game

Aside from an interesting reversal of conventional wisdom, the story of Bachelier’s discovery is also the story of the two most important risk management strategies—options and diversification. Bachelier’s ability to describe the movement of stock prices mathematically as “random walks” provided the foundation for him to crudely price the option contracts that were then trading in Paris and had traded since the seventeenth century in Amsterdam. Myron Scholes and Robert Merton would win the Nobel Prize in 1997 for a pricing formula that corresponds to (and considerably improves upon) the mostly forgotten logic laid down by Bachelier. And Bachelier’s ability to describe stock prices moving about at random ultimately gave rise to portfolio theory by putting forward the notion that it was hopeless to try to beat the market—the best you could do was hold a diversified portfolio.

Princeton, NJ: Princeton University Press, 2006; Bernstein, Jeremy. “Bachelier.” American Journal of Physics 73, no. 5 (2005): 395; Pearle, Philip, Brian Collett, Kenneth Bart, David Bilderback, Dara Newman, and Scott Samuels. “What Brown Saw and You Can Too.” American Journal of Physics 78, no. 12 (2010): 1278; and Holt, Jim. “Motion Sickness: A Random Walk from Paris to Wall Street.” Lingua Franca, December 1997. The discussion of options relies on Aristotle, Politics. Vol. 1. Translated by H. Rackham. Cambridge, MA: Harvard University Press, 1944; de la Vega, Joseph. Confusion de Confusiones. Edgeton, CT: Martino Fine Books, 2013; Frock, Roger.

Review of Economics and Statistics 47 (1965): 13–37; Sharpe, William F. “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk.” Journal of Finance 19, no. 3 (September 1964): 425–42; Treynor, J. L. “Toward a Theory of Market Value of Risky Assets.” MS, 1962. Final version in Asset Pricing and Portfolio Performance, 15–22. Edited by Robert A. Korajczyk. London: Risk Books, 1999; and Roll, Richard. “A Critique of the Asset Pricing Theory’s Tests Part I: On Past and Potential Testability of the Theory.” Journal of Financial Economics 4, no. 2 (1977): 129–76; Merton, Robert C. “An Intertemporal Capital Asset Pricing Model.” Econometrica 41 (September 1973): 867–87.


pages: 604 words: 161,455

The Moral Animal: Evolutionary Psychology and Everyday Life by Robert Wright

agricultural Revolution, Andrei Shleifer, Apollo 13, Asian financial crisis, British Empire, centre right, cognitive dissonance, cotton gin, double entry bookkeeping, double helix, Easter island, fault tolerance, Francis Fukuyama: the end of history, Garrett Hardin, George Gilder, global village, Great Leap Forward, invention of gunpowder, invention of movable type, invention of the telegraph, invention of writing, invisible hand, John Nash: game theory, John von Neumann, Marshall McLuhan, Multics, Norbert Wiener, planetary scale, planned obsolescence, pre–internet, profit motive, Ralph Waldo Emerson, random walk, Richard Thaler, rising living standards, Robert Solow, Silicon Valley, social intelligence, social web, Steven Pinker, talking drums, technological determinism, the medium is the message, The Wealth of Nations by Adam Smith, trade route, Tragedy of the Commons, your tax dollars at work, zero-sum game

Gould recommends that we wake up and smell the coffee, confront the harsh prospect that “we are, whatever our glories and accomplishments, a momentary cosmic accident that would never arise again if the tree of life could be replanted from seed and regrown under similar conditions.” And he doesn’t mean “we” narrowly—not just Homo sapiens. If you replayed evolution on this planet, the chances of getting any species smart enough to reflect on itself are “extremely small.” RANDOM WALKS Before seeing what is wrong with Gould’s claim, we need to first see that it isn’t as sweeping as it sounds. You might think that when he says progress is not a general evolutionary trend, he is saying that evolution doesn’t tend to produce more and more complex forms of life over time. But he isn’t.

Skirting the sidewalk’s south side is a brick wall, and on the sidewalk’s north side is a curb and a street. Will the drunk eventually veer off the curb, into the street? Probably. Does this mean he has a “northerly directional tendency”? No. He’s just as likely to veer south as north. But when he veers south the wall bounces him back to the north. He is taking a “random walk” that just seems to have a directional tendency. If you get enough drunks and give them enough time, one will eventually get all the way to the other side of the street (notwithstanding traffic fatalities involving other, less lucky drunks). That’s us: the lucky species that, through millions of year of random motion, happened to get to the far north.

Again, as with Gould’s emphasis on stagnant “modal” complexity, one might ask how much this argument really matters for philosophical purposes. The question behind this whole exercise, remember, is whether the evolution of something as smart and complex as us was very likely. If the combination of a “random walk” and a “wall of zero complexity” leads people to conclude that the answer is yes, then, well, their answer is yes. If, as Gould fears, people are inclined to take a “yes” answer as evidence of higher purpose, they probably aren’t going to be too picky about the exact type of “yes.” God, they will say, works in strange and wondrous ways.


pages: 263 words: 20,730

Exploring Python by Timothy Budd

c2.com, centre right, duck typing, functional programming, general-purpose programming language, Guido van Rossum, higher-order functions, index card, random walk, sorting algorithm, web application

Maintain a value that indicates the current location of the drunk, and at each step of the simulation move either right or left. Display the value of the array after each step. 17. The two dimensional variation on the random walk starts in the middle of a grid, such as an 11 by 11 array. At each step the drunk has four choices: up, down, left or right. Earlier in the chapter we described how to create a two-dimensional array of numbers. Using this data type, write a simulation of the two-dimensional random walk. 18. One list is equal (==) to another if they have the same length and the corresponding elements are equal. It is perhaps surprising that lists can also be compared with the relational operators, such as <.

Compute the sum of the two dice, and record the number of times each value appears. After the loop, print the array of sums. You can initialize the array using the idiom shown earlier in this chapter: times = [0] * 12 # make an array of 12 elements, initially zero 16. A classic problem that can be solved using an array is the random walk. Imagine a drunken man standing on the center square of a sidewalk consisting of 11 squares. At each step the drunk can elect to go either right or left. How long will it be until he reaches the end of the sidewalk, and how many times will he have stood on each square? To solve the problem, represent the number of times the drunk has stood on a square as an array.

One way might be to simply represent the information on each product in three successive lines of text, such as the following: Toast O’Matic 42 12.95 Kitchen Chef Blender 193 47.43 Did you immediately understand that there are currently 42 Toast O’Matics in the inventory and that they cost $12.95 each? Compare the description just given to the following XML encoding of the same data: <inventory> <product> <name>Toast O’Matic</name> <onHand>42</onHand> <price>12.95</price> </product> <product> <name>Kitchen Chef Blender</name> <onHand>193</onHand> <price>47.43</price> </produce> </inventory> This example illustrates both the advantages and the disadvantages of the XML format. The advantage is that the information is more self-documenting. It is clear what each field represents. You can read the information and immediately know what it means.


The Economics Anti-Textbook: A Critical Thinker's Guide to Microeconomics by Rod Hill, Anthony Myatt

American ideology, Andrei Shleifer, Asian financial crisis, bank run, barriers to entry, behavioural economics, Bernie Madoff, biodiversity loss, business cycle, cognitive dissonance, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Ricardo: comparative advantage, different worldview, electricity market, endogenous growth, equal pay for equal work, Eugene Fama: efficient market hypothesis, experimental economics, failed state, financial innovation, full employment, gender pay gap, Gini coefficient, Glass-Steagall Act, Gunnar Myrdal, happiness index / gross national happiness, Home mortgage interest deduction, Howard Zinn, income inequality, indoor plumbing, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, liberal capitalism, low interest rates, low skilled workers, market bubble, market clearing, market fundamentalism, Martin Wolf, medical malpractice, military-industrial complex, minimum wage unemployment, moral hazard, Paradox of Choice, Pareto efficiency, Paul Samuelson, Peter Singer: altruism, positional goods, prediction markets, price discrimination, price elasticity of demand, principal–agent problem, profit maximization, profit motive, publication bias, purchasing power parity, race to the bottom, Ralph Nader, random walk, rent control, rent-seeking, Richard Thaler, Ronald Reagan, search costs, shareholder value, sugar pill, The Myth of the Rational Market, the payments system, The Spirit Level, The Wealth of Nations by Adam Smith, Thorstein Veblen, ultimatum game, union organizing, working-age population, World Values Survey, Yogi Berra

The other form of evidence exploited the fact that asset prices should reflect all publicly available information, and will change only in response to new and unexpected information. But since no one can predict what is by definition unexpected, the markets are inherently unpredictable. As a result, every stock price will follow a random walk – at each moment its next movement is just as likely to be up as down. The random-walk prediction was extensively tested in the late 1960s and 1970s and it proved very hard to refute. It implies that the ‘experts’ running mutual funds should do no better at stock picking than they might by throwing darts at a dartboard. As Stiglitz (2003: 61) points out, this conclusion has been supported by numerous studies.

If the elasticity is equal to one (a so-called ‘unit-elastic’ demand curve) then a fare change would have no effect on total revenue. 1.3 The supply curve The supply curve describes the relationship between the quantity of a good supplied and its own price, ceteris paribus. It too is a frontier, showing the minimum price that sellers are willing to accept for any given quantity. Generally speaking, as the price of a product increases, the quantity supplied goes up. The 47 3  |  How markets work GASOLINE Price Surplus Price S S Price P1 P* P* P0 Shortage D 300 500 Quantity 250 D 600 Quantity figure 3.2 Movement towards equilibrium responsiveness of quantity supplied to a change in price is measured by the price elasticity of supply. es = % change in quantity supply % change in price The six key shift factors on the supply side are: the weather (especially important for agricultural products); changes in the prices of goods related in production; changes in input prices (or prices of ‘factors of production’); changes in technology; changes in the number (and size) of firms in the industry; and changes in expectations about future prices.

The 47 3  |  How markets work GASOLINE Price Surplus Price S S Price P1 P* P* P0 Shortage D 300 500 Quantity 250 D 600 Quantity figure 3.2 Movement towards equilibrium responsiveness of quantity supplied to a change in price is measured by the price elasticity of supply. es = % change in quantity supply % change in price The six key shift factors on the supply side are: the weather (especially important for agricultural products); changes in the prices of goods related in production; changes in input prices (or prices of ‘factors of production’); changes in technology; changes in the number (and size) of firms in the industry; and changes in expectations about future prices. Comparing the demand shift factors with the supply shift factors we see only one identical item: expectations of future prices. 1.4 Market equilibrium When prices are free to fluctuate, market forces move the actual price (and quantity) towards the equilibrium price (and quantity). The left-hand diagram of Figure 3.2 shows that at a price P1, which is above the equilibrium price, P*, there is an excess supply (or surplus) equal to 200 units per period.


pages: 386 words: 122,595

Naked Economics: Undressing the Dismal Science (Fully Revised and Updated) by Charles Wheelan

affirmative action, Alan Greenspan, Albert Einstein, Andrei Shleifer, barriers to entry, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, Boeing 747, Bretton Woods, business cycle, buy and hold, capital controls, carbon tax, Cass Sunstein, central bank independence, classic study, clean water, collapse of Lehman Brothers, congestion charging, creative destruction, Credit Default Swap, crony capitalism, currency manipulation / currency intervention, currency risk, Daniel Kahneman / Amos Tversky, David Brooks, demographic transition, diversified portfolio, Doha Development Round, Exxon Valdez, financial innovation, fixed income, floating exchange rates, George Akerlof, Gini coefficient, Gordon Gekko, Great Leap Forward, greed is good, happiness index / gross national happiness, Hernando de Soto, income inequality, index fund, interest rate swap, invisible hand, job automation, John Markoff, Joseph Schumpeter, junk bonds, Kenneth Rogoff, libertarian paternalism, low interest rates, low skilled workers, Malacca Straits, managed futures, market bubble, microcredit, money market fund, money: store of value / unit of account / medium of exchange, Network effects, new economy, open economy, presumed consent, price discrimination, price stability, principal–agent problem, profit maximization, profit motive, purchasing power parity, race to the bottom, RAND corporation, random walk, rent control, Richard Thaler, rising living standards, Robert Gordon, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Sam Peltzman, school vouchers, seminal paper, Silicon Valley, Silicon Valley startup, South China Sea, Steve Jobs, tech worker, The Market for Lemons, the rule of 72, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, transaction costs, transcontinental railway, trickle-down economics, urban sprawl, Washington Consensus, Yogi Berra, young professional, zero-sum game

Second, the most effective critics of the efficient markets theory think the average investor probably can’t beat the market and shouldn’t try. Andrew Lo of MIT and A. Craig MacKinlay of the Wharton School are the authors of a book entitled A Non-Random Walk Down Wall Street in which they assert that financial experts with extraordinary resources, such as supercomputers, can beat the market by finding and exploiting pricing anomalies. A BusinessWeek review of the book noted, “Surprisingly, perhaps, Lo and MacKinlay actually agree with Malkiel’s advice to the average investor. If you don’t have any special expertise or the time and money to find expert help, they say, go ahead and purchase index funds.”8 Warren Buffett, arguably the best stock picker of all time, says the same thing.9 Even Richard Thaler, the guy beating the market with his behavioral growth fund, told the Wall Street Journal that he puts most of his retirement savings in index funds.10 Indexing is to investing what regular exercise and a low-fat diet are to losing weight: a very good starting point.

So, faced with the prospect of giving up consumption in the present for plodding success in the future, we eagerly embrace faster, easier methods—and are then shocked when they don’t work. This chapter is not a primer on personal finance. There are some excellent books on investment strategies. Burton Malkiel, who was kind enough to write the foreword for this book, has written one of the best: A Random Walk Down Wall Street. Rather, this chapter is about what a basic understanding of markets—the ideas covered in the first two chapters—can tell us about personal investing. Any investment strategy must obey the basic laws of economics, just as any diet is constrained by the realities of chemistry, biology, and physics.

In other words, 55 percent of the mutual funds that claim to have some special stock-picking ability did worse over two decades than a simple index fund, our modern equivalent of a monkey throwing a towel at the stock pages. If you had invested $10,000 in the average actively managed equity fund in 1973, when Malkiel’s heretical book A Random Walk Down Wall Street first came out, it would be worth $355,091 today (many editions later). If you had invested the same amount of money in an S&P 500 index fund, it would now be worth $364,066. Data notwithstanding, the efficient markets theory is obviously not the most popular idea on Wall Street.


pages: 256 words: 67,563

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships by Camilla Pang

autism spectrum disorder, backpropagation, bioinformatics, Brownian motion, correlation does not imply causation, data science, deep learning, driverless car, frictionless, job automation, John Nash: game theory, John von Neumann, Kickstarter, Nash equilibrium, neurotypical, phenotype, random walk, self-driving car, stem cell, Stephen Hawking

Its chronology dates back to the Roman philosopher Lucretius, who wrote about how dust particles move through light, two millennia before the same sight captivated five-year-old me. But although the essence of Brownian motion is about unpredictable movement – the progress of each particle is even known as a random walk – that’s not the whole story. Microscopically, every particle is doing its own sweet thing, buffeted this way and that by the liquid or gas molecules that surround it. But change your perspective to the macroscopic – the big picture – and you see something quite different. Through this zoomed-out lens, randomness starts to give way to a pattern.

actin (proteins) 33, 34 adaptor proteins 38, 39–40, 43, 46 ADHD (attention deficit hyperactivity disorder) x, xiv, 44 acceptance of 29, 119, 196, 197 boredom and xi, 85, 87, 163 brainwaves and 98–100 childhood and 87 decision making and 17, 101, 141, 185 diagnosis of 92, 101 fear and 77, 85, 86 goals and 131, 133, 139 gradient descent and 139, 141 harmony/amplitude and 92–3, 93, 98–106, 102, 105 information processing and xi, 13, 98–9 insomnia and 70 learning rate and 141 memory and 185–6 overthinking and 16, 102 panic induced by 77 patience and 131, 133, 139, 225 reading and 163–5 superpower 29 time perception and 13, 98, 131, 133, 139, 141 affinities (single interactions) 183 ageing, human 148–9 agent-based modelling (ABM) 210–14, 215, 216, 219 algorithms, computer decision making and xii, 1–24, 8, 15, 128–30, 134, 138–41, 143, 146, 156–60, 158, 187, 188–93, 189, 195, 199, 202, 203–204 fuzzy logic and 156–60 gradient descent algorithm 125, 138–41, 143, 191 human mind and 1, 4 limitations of 3 neural networks and 187, 188–93, 189, 195, 198, 202, 203 supervised learning and 4, 6, 23 unstructured, ability to be 2 unsupervised learning and 4, 5, 6, 10, 18, 21 see also machine learning alienation 98 alpha keratins 32 alpha waves 98 amino acids 31–2 amplitude 90–95, 96, 99, 100–104 anxiety ADHD and see ADHD ASD and see ASD Asperger’s syndrome and see Asperger’s syndrome attacks 14, 70–71, 73, 83, 142 colour and 70–71, 73, 102, 127 crowds and 14, 70, 114–15, 121 decision making and 12, 14, 16, 20, 51–2, 59, 61, 81, 83, 142 fear/light and 70–71, 72–4, 74, 76, 77, 78–9, 80, 81, 82–5 GAD (generalized anxiety disorder) x–xi, 74, 197 goals and 124, 134, 135, 137, 138, 142–3 harmony and 98–9, 102, 108 information processing and xi, 83 loud noises and 70, 87, 198 memory and 186, 204 night terrors 70 order and 48, 51–2, 59, 61 smell and 12, 14, 70, 102, 127, 160, 164–5, 199, 201, 204 as a strength 29, 82–4, 142–3 superpower 29 texture and 70 arguments 38, 154, 156–60, 162, 183 artificial intelligence (AI) 3, 156, 186, 187, 188, 189, 215 ASD (autism spectrum disorder) acceptance of 197 Asperger’s syndrome and xiii–xiv Bayes’ theorem and 155 crowds and 115 decision making and 6, 10, 12, 16 empathy and 145 explained x–xi fear and 70–71, 77, 80, 85, 86 memory and 194 order and 50, 51–2, 58 superpower 29 Asperger’s syndrome xiii–xiv autism and xiii–xiv Bayes’ theorem and 151–2 clubbing/music festivals and 153, 201 empathy and 145 fear/light and 71–2 meeting people and 151 memory and 194–5 politeness and 206 atom Brownian motion and 112, 114 chemical bonds and/atomic compounds 165, 166, 167–71, 172, 173, 174, 176, 177, 178, 180, 181, 184 crowds and 107, 111, 112, 114 light and 75 autism see ASD (autism spectrum disorder) avidity 183 backpropagation 191, 199 Bayes’ theorem 151–6, 159, 162, 206 bee colonies 36 beta waves 98 bias 153, 160, 162, 192, 196, 197, 202, 204 bioinformatics 209, 220 blood sugar levels 38, 65 bonds, chemical 165–84 bond length 173 covalent 168, 169, 170, 171, 173, 182, 183, 184 electromagnetic force 175–6 evolution of over time 180–83 four fundamental forces 174–80, 179 gravitational force 174–5 hydrophobic effect 171–3 ionic 169–71, 170, 173, 176, 180, 181, 184 strong (nuclear) force 176–7 tan(x) curve and 163–5, 164 valency and 173–4 weak (nuclear) force 177–80, 182 boredom xi, 41, 85, 87, 158, 163, 186, 192 box thinking 5–12, 8, 17, 19–20, 23, 24 brainwaves 98–100 Brown, Robert 112 Brownian motion 112–14, 113, 115 cancer xii, 4, 45–7, 85, 118, 149, 219, 220 carbon dioxide 168 cars braking 157 driverless 189, 190–91, 202 category failure 22–3 cell signalling 37, 38–42 cellular evolution 146, 147–50, 148, 151 stem cells 146, 147–8, 148, 149, 150 chaos 13–15, 17, 21, 29, 48, 60 chemical bonds see bonds, chemical childhood: ADHD and 87–8 box thinking and 8, 10 fear and 83, 109, 121 neurodiversity at school 25–8 time perception and 126–8 tree thinking and 10, 21 Civilization V (video game) 108 compound, atomic 167–71, 172, 180, 181 conditional probability 153 conflict resolution 157 connecting with others 163–84 avidity and 183 chemical bonds and 165–84, 170 four fundamental forces and 174–80, 179 fraying and decomposing connections 180–84 tan(x) curve and 163–5, 164 see also bonds, chemical consensus behaviours, understanding and modelling 110–18 covalent bonds 168, 169, 170, 171, 173, 182, 183, 184 crowds 12, 14, 26, 70, 107–21, 113, 201 anxiety and 14, 70, 114–15, 121 atom and 107, 111, 112, 114 Brownian motion and 112, 113, 115 consensus and 110–18 decision making and 108, 110, 111, 112 differences/diversity and 111, 116–17, 118 diversity and 117–18 dust particle movement and 107–108, 111, 112, 113, 116 ergodic theory and 115–20 full stop and 107–108 individuality and 115–21 Newton’s second law (force = mass × acceleration) and 114 stereotypes and 117 random walk 113 stochastic (randomly occurring) process and 115–16 data inputs 190 dating 144, 207, 221–2 apps 161, 193 decision making box thinking and 5–12, 8, 17, 19–20, 24 crowds and 108, 110, 111, 112 equilibrium and 66, 67, 68, 69 error, learning to embrace 21–4 fear and 71, 81 feedback loop and 199, 202, 203–204 fuzzy logic and 146, 156–60, 158 game theory and 215–18, 220 goals and 128–30, 134, 137, 138–41, 142, 143, 191 gradient descent algorithm and 138–41, 143, 191 homology and 220 how to decide 17–21 machine learning and xii, 1–24, 128–30, 134, 138–41, 143, 146, 156–60, 158, 187, 188–93, 189, 195, 199, 202, 203–204 memory and 187, 199, 203–204 network theory and 134, 138 neural networks and 187, 188–93, 189, 195, 198, 202, 203 probability and 146 proteins and 28, 36, 37, 38, 39, 42, 43, 46 tree, thinking like a and 5–7, 10–24, 15 deep learning 187, 188, 189–90 see also neural networks delta waves 98 denial 78, 83–4 depression 100–103 differences/diversity, understanding/ respecting 25–47, 226 cancer and 45–7, 118 chemical bonds and 165–6, 168, 169–71, 181–3 collaboration/success and 45–7 crowds and 111, 116–17, 118 empathy and 149 ergodicity and 118, 120 evolution and xii, 31, 45–7, 118, 120, 146, 147, 148, 148, 149 fuzzy logic and 157, 162 game theory and 219 harmony and 104–105 hierarchy and 36 homology and 221–2 human survival and 118, 120 order and 61 probability and 154, 155 proteins and 28–30, 31, 34, 36–8, 39, 42, 43, 45, 46, 47 see also neurodiversity diffusion 113 dipole 176 DNA 31–2, 148 driverless cars 189, 190–91, 202 dust particle movement 107–108, 111, 112, 113, 116 electromagnetic force 175–6 electrons 131, 167–8, 169, 171, 173, 178, 181, 182, 183 electron transfer 169, 176 Elements of Physical Chemistry, The 52 Elton John 48, 108 empathy xiii, 36, 60, 61, 62, 68, 106, 144–62, 206, 226 arguments and 154, 157–60, 162 ASD and 145 autism and 145 bias and 153, 160, 162 cellular evolution and 146, 147–50, 148, 151 difference, respecting and 149 difficulty of 145–6 evolution and 161–2 eye contact and 149 fuzzy logic and 146, 156–60, 158 individuality and 118–20 non-verbal indicators 149 probability/Bayes’ theorem and 146, 151–6, 159, 162, 206 proteins and 38, 39, 45, 46 relationships and 144–62 ENFJ personality, Myers–Briggs Type Indicator 39 ENFP personality, Myers–Briggs Type Indicator 39 ENTJ personality, Myers–Briggs Type Indicator 41 ENTP personality, Myers–Briggs Type Indicator 40–41 entropy 48–9, 54–6, 57–8, 90 equilibrium achieving 64–7 Bayes’ theorem and 155 feedback and 202 fuzzy logic and 156 game theory and/Nash equilibrium 215, 216, 217 harmonic motion and 89, 90, 90–91 interference and 94, 95, 96 perfection and 50 resonance 97 ergodic theory 115–20 error, learning to embrace 21–4 ESTJ personality, Myers–Briggs Type Indicator 39 ESTP personality, Myers–Briggs Type Indicator 41 evolutionary biology xii chemical bonds and 180–84 diversity/difference and xii, 31, 45–7, 118, 120, 146, 147, 148, 148, 149 empathy and 146, 147–50, 148, 151 fear and 83, 84 order and 69 proteins and 29, 31, 35, 46–7, 118, 146, 161–2 relationships and 161–2, 166, 180–84 exercise, physical 9, 63, 66, 81, 85, 185, 201, 226 expectations, realistic 57–9 extroversion 37 eye contact 77, 80, 83–4, 149 fear xii, 62, 70–86, 109, 114, 115, 121, 142, 172, 197, 198, 201, 208 ASD and 70–71 Asperger’s and 71–2 denial of 83–4 eye contact and 77, 80, 83–4, 149 FOMO (fear of missing out) 19, 127, 131, 137, 138 function of 71 inspiration, turning into 82–3 light and 72–86, 74 as a strength 82–4 transparency and 78, 81–2 feedback/feedback loops 187–205 backpropagation and 191, 199 biases and 192, 196, 197, 202 memory and 187, 188, 191–205 neural networks and 187, 188, 191–4, 195, 198, 202, 203 positive and negative 200–202 re-engineering human 187, 191–3, 194–205 see also memory Ferguson, Sir Alex 31 ‘fighting speech’ 158 fire alarms, fear of 71 fractals 11 full stop 107–108 fundamental forces, the four 174–80 fuzzy logic 146, 156–60, 158, 162 GAD (generalized anxiety disorder) x–xi, 74, 197 game theory xii, 157, 209, 215–19, 222 gamma waves 98 gene sequences 31 Gibbs free energy 55–6, 65 goals, achieving 122–43 anxiety, positive results of 142–3 childhood and 126–8 difficulty of 141–2 fear of missing out (FOMO) and 127, 131, 137, 138 gradient descent algorithm and 138–41, 143, 191 Heisenberg’s Uncertainty Principle and 125–6, 128, 131–2, 133, 143 learning rate and 141 momentum thinking and 129, 130–31, 130 network theory and 132–8, 136 observer effect and 131 perfect path and 141 position thinking and 129–30, 129, 131 quantum mechanics/spacetime and 122–5, 123, 128, 131, 136 present and future focus 125–32 topology and 134, 138 wave packets and 128–9 gradient descent algorithm 125, 138–41, 143, 191 gravitational force 174–5 haematopoiesis 147 harmony, finding 87–106 ADHD and 92, 93, 98–106, 102, 105 amplitude and 90–93, 94, 95, 96, 99, 100, 101–102 depression and 100–103 harmonic motion 88, 89–93, 90, 93, 96, 103 ‘in phase’, being 95, 97 interference, constructive and 94–7, 95 oscillation and 88–94, 102 pebble skimming and 87–8 resonance and 96–7 superposition and 94–5 synchronicity and 88, 97 wave theory and 88–9, 90–106, 90, 93, 95, 105 Hawking, Stephen 122, 127, 136 A Brief History of Time 67, 122–3, 134–5 healthy, obsession with being 63 Heisenberg, Werner 125–6, 128, 133, 143 hierarchy 36, 213 hierarchy of needs, Maslow’s 140 Hobbes, Thomas 108 Leviathan 218, 219 homeostasis 65–6 Homo economicus (economic/ self-interested man/person) 218 Homo reciprocans (reciprocating man/person who wants to cooperate with others in pursuit of mutual benefit) 218 homology 219–22 hydrogen bonding 171, 181 hydrophobic effect 171–3 imitation, pitfalls of 62–3 immune system 5, 34, 45, 147, 161 individuality, crowds and 115–21 INFJ personality, Myers–Briggs Type Indicator 42 ‘in phase’, being 67, 95, 97, 104, 224 insomnia 70 Instagram 21, 72, 99 interference, wave theory and 94–6, 95, 97, 103 INTJ personality, Myers–Briggs Type Indicator 42 introversion 30, 36, 37, 42, 171 ionic bonds 169–71, 170, 173, 176, 180, 184 ISTP personality, Myers–Briggs Type Indicator 39–40 keratin 32 kinase proteins 38, 39, 40–42, 43, 45, 46 k-means clustering 18, 20 learning rate 141 l’homme moyen (average man/person whose behaviour would represent the mean of the population as a whole) 108 light Asperger’s syndrome and 71–2 cones 122–5, 123, 127, 132, 135, 136, 136 fear and 70–86, 74 prism and 74–5, 76, 77, 78–82, 85, 91 refraction and 72–4, 75, 76, 77–82, 83, 85, 91 speed of 74–5, 76, 82, 123 transparency and 78–9, 81–2 waves 74–86, 74 loud noises, fear of 70, 87, 198 Lucretius 112 machine learning backpropagation 191, 199 basics of 3–5 clustering and 5, 10, 16, 18, 19, 20, 22 data inputs 190 decision making and xii, 1–24, 8, 15, 128–30, 134, 138–41, 143, 146, 156–60, 158, 187, 188–93, 189, 195, 198, 199, 202, 203–4 deep learning 187, 188, 189–90 feature selection 18–20 fuzzy logic 146, 156–60, 158, 162 games and 3, 190 goals and 138–41 gradient descent algorithm 138–41, 143, 191 k-means clustering 18, 20 memory and 185–205, 189 noisy data and 22 neural networks 187, 188–93, 189, 195, 198, 202, 203 supervised learning 4, 6, 23 unsupervised learning and 4, 5, 6, 10, 18, 21 Manchester United 31 Maslow, Abraham: hierarchy of needs 140 meltdowns xi, 12, 14, 23, 25, 61, 77, 115, 155 memory xii, 7, 11, 127, 226 ADHD and 185 feedback loops and 187, 188, 191–205 neural networks and 187, 188–93, 189, 195, 198, 202, 203 power/influence of in our lives 186–7 training 187, 194–205 mistakes, learning from 185–205 backpropagation and 191, 199 biases and 192, 196, 197, 202 feedback/feedback loops and 187, 188, 191–205 memory and 185–7, 188, 191, 192–3, 194–205 neural networks and 187, 188–93, 189, 195, 198, 202, 203 mitosis (division) 148–9 momentum thinking 129, 130–31, 130 morning routine 14, 16 motion Brownian 112–14, 113, 115 harmonic 88, 89–93, 90, 93 Myers–Briggs Type Indicator 37, 39–42 ENFJ personality 39 ENFP personality 39 ENTJ personality 41 ENTP personality 40–41 ESTJ personality 39 ESTP personality 41 INTJ personality 42 ISTP personality 39–40 myosin 33–4 Nash equilibrium 215–16, 217 Nash, John 215 nervous tics x, 25 network theory 125, 132–8, 136, 143 Neumann, John von 215 neurodiversity xi, 85, 208–209 Newton’s second law (force = mass × acceleration) 114 night terrors 70 noble gases 167, 171 noise-cancelling headphones 71, 95–6 noisy data 22 non-verbal indicators 149 nuclear proteins 38, 41–2, 43 neural networks 187, 188–93, 189, 195, 198, 202, 203 obsessive compulsive disorder (OCD) box thinking and 8, 8 dating and 197 fear/light and 74 order and 51 observer effect 114, 131 orange, fear of colour 70–71 order and disorder 48–69 anxiety and 48, 51, 59, 61 ASD and 50, 51–2, 58 competing visions of 60–64 disordered orderly person 50–54 distribution of energy in layers of order 58 entropy (increasing disorder) 48–9, 54–6, 57–8 equilibrium and 64–7 order and disorder – cont’d.

perfection and 54, 57–9, 62, 65, 67–9 thermodynamics and 48–50, 52–3, 54–60, 58, 63, 64–7, 68 oscillation 75, 88–94, 90, 93 overfitting your model 193 Pang, Lydia v, 1, 109, 136–7, 149–50, 220 Pang, Peter v, 87, 122, 127, 201, 204 Pang, Sonia v, ix, 8, 9, 34, 48, 49, 51, 52, 59, 60, 62, 70, 72, 74, 107, 108, 121, 127, 142, 195, 198, 206, 207, 218 panic attacks 199, 201 patience 60, 111, 132–3, 139, 140, 155, 225 pebble skimming 87–8 peer pressure 25–6, 30, 44, 45, 63, 118, 138 perfection/perfectionism 21, 54, 57–9, 62, 65, 67, 68–9, 88, 93, 137, 140, 141, 223 see also order and disorder photonics 72 playground swing 89–93, 96, 103 polarities 170, 171, 172, 174, 175, 176, 181 politeness/etiquette 206–23 agent-based modelling (ABM) and 210–14, 215, 216 dating and 207, 221–2 game theory and 209, 215–19 homology and 219–22 pollen 112, 113 position thinking 129–30, 129, 131 probability 137, 146, 159, 161, 162, 206 empathy and 151–6 proteins xii, 27–8, 167 adaptor 38, 39–40, 43, 46 difference/diversity and 28–30, 31, 34, 36–8, 39, 42, 43, 45, 46, 47 four stages of 31–5, 33 function of 31 homology and 220–21 kinase 38, 39, 40–42, 43, 45, 46 model of teamwork 27–31 nuclear 38, 41–2, 43 personalities and teamwork 35–47, 38, 171 receptor 38–9, 38, 41, 42–3, 46, 172 quantum mechanics 72, 122–6, 123, 128–30, 129, 131, 142 Quetelet, Adolphe 108 random walk 113 realism filter 82 receptor proteins 38–9, 38, 41, 42–3, 46, 172 refraction 72, 73, 74, 75, 76, 77, 79, 80, 81, 82, 83, 91 resonance, wave theory and 94, 96–7, 103, 105–106 routine 14, 16, 41, 51, 52, 85, 115, 155, 195, 198 shavasana (corpse) pose 130 smell anxiety and 12, 14, 70, 102, 109, 121, 155, 160, 164–5, 199, 201 box thinking and 7 synaesthesia and 73, 127 social media 19, 21, 137 sodium chloride (NaCl) 169, 180 spacetime 122–5, 123 statistics 22, 120, 161 stem cells 146, 147–8, 148, 149, 150 stereotypes 117 stochastic (randomly occurring) process 115–16 strong (nuclear) force 176–7 subclones 118 superposition 94 synaesthesia 73 synchronicity 88, 97 tan(x) curve 163–5, 164 teamwork protein model of 27–31 protein personalities and 35–47, 38, 171 teenagers 26, 44, 49, 63–4 telomere 148 thermodynamics xii, 48–54 equilibrium 64–7 first law of 54 order and 48–69, 58 second law of 48–9, 54–6, 57–8, 90 theta waves 98 tidiness 48–50, 51, 52–3, 56, 59, 60–61, 62, 68 see also order and disorder time perception 13, 122–5, 128, 131, 133, 139, 141 topology 125, 134, 138 trees, thinking in 6–7, 10–24, 15 decision making and 17–21 error, learning to embrace 21–4 Uncertainty Principle, Heisenberg’s 125–6, 128, 131–2, 133, 142, 143 valency 171, 173–4, 181 washing machines 157, 159 water (H2O) 171 waves, light 74–86, 74 wave theory 88–106 harmonic motion and 90, 90–93 interference and 94–6, 95 resonance 96–7 wave packets 128–31, 129, 130 wavelengths, interacting with other 98–106, 102, 105 weak (nuclear) force 177–9 weightings, neural networks and 190, 191, 192, 193, 196, 199, 200, 202 yoga 52, 130 THIS IS JUST THE BEGINNING Find us online and join the conversation Follow us on Twitter twitter.com/penguinukbooks Like us on Facebook facebook.com/penguinbooks Share the love on Instagram instagram.com/penguinukbooks Watch our authors on YouTube youtube.com/penguinbooks Pin Penguin books to your Pinterest pinterest.com/penguinukbooks Listen to audiobook clips at soundcloud.com/penguin-books Find out more about the author and discover your next read at penguin.co.uk PENGUIN BOOKS UK | USA | Canada | Ireland | Australia India | New Zealand | South Africa Penguin Books is part of the Penguin Random House group of companies whose addresses can be found at global.penguinrandomhouse.com.


pages: 611 words: 130,419

Narrative Economics: How Stories Go Viral and Drive Major Economic Events by Robert J. Shiller

agricultural Revolution, Alan Greenspan, Albert Einstein, algorithmic trading, Andrei Shleifer, autism spectrum disorder, autonomous vehicles, bank run, banking crisis, basic income, behavioural economics, bitcoin, blockchain, business cycle, butterfly effect, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, central bank independence, collective bargaining, computerized trading, corporate raider, correlation does not imply causation, cryptocurrency, Daniel Kahneman / Amos Tversky, debt deflation, digital divide, disintermediation, Donald Trump, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fake news, financial engineering, Ford Model T, full employment, George Akerlof, germ theory of disease, German hyperinflation, Great Leap Forward, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, implied volatility, income inequality, inflation targeting, initial coin offering, invention of radio, invention of the telegraph, Jean Tirole, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, litecoin, low interest rates, machine translation, market bubble, Modern Monetary Theory, money market fund, moral hazard, Northern Rock, nudge unit, Own Your Own Home, Paul Samuelson, Philip Mirowski, plutocrats, Ponzi scheme, public intellectual, publish or perish, random walk, Richard Thaler, Robert Shiller, Ronald Reagan, Rubik’s Cube, Satoshi Nakamoto, secular stagnation, shareholder value, Silicon Valley, speech recognition, Steve Jobs, Steven Pinker, stochastic process, stocks for the long run, superstar cities, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, theory of mind, Thorstein Veblen, traveling salesman, trickle-down economics, tulip mania, universal basic income, Watson beat the top human players on Jeopardy!, We are the 99%, yellow journalism, yield curve, Yom Kippur War

., 65 psychoanalysis and narrative, 15, 16, 280 Psychological Economics (Katona), 66 psychological impact of opinion leaders, 127 Psychologie des foules (The Crowd) (Le Bon), 59, 119 psychology and narrative, 15, 65–67, 78, 287 The Psychology of Suggestion (Sidis), 121 PTSD (post-traumatic stress disorder), 57 Public Opinion Research Archive, 284 purchasing power theory of wages, 188 push a button, 179, 200 Putin, Vladimir, 103 qualitative research, 281 quarantines, 19–20 quasi-controlled experiments, 73 questionnaire surveys, 285–86 Rand, Ayn, 50 random events with major effects, 40, 75, 99–100 randomness of which narratives go viral, 31, 40, 64–65, 286 random walk theory of speculative prices, xiii rational expectations models, 277, 295, 301n13 Reagan, Ronald, xii, 42, 51–52, 153 Reagan administration, tax cuts by, 48, 51 real business cycle model, 24–25, 27f real estate boom in 2000s: automation narratives and, 205; Trump University and, 226. See also housing booms real estate narratives, 212. See also home price narratives real estate speculation: in second half of twentieth century, 213. See also home price narratives Rebel Without a Cause (film), 148 recessions: in 1949, 256, 264; in 1950s and 1960s, 199, 200–201, 264; in 1957–58, 201, 264; in 1973–75, 239, 256–57; in 1980 and 1981–82, 204; in 2001, ended after terrorist attack, 82–83, 307n17; biggest in US since 1973, 112; causes listed by economic historians, 112; consumer confidence narrative and, 115; economists’ reluctance to mention narratives underlying, 276; narrative infecting fraction of population and, 29; as narratives in themselves, 112; not successfully forecast, xiv, 301n6; popular belief in periodic nature of, 124–25; popular stories affecting, xii; reasons for hesitating to spend during, 75; self-fulfilling prophecy in forecasts of, 123–24, 125 reciprocity, human patterns of, 36 recovery in medical model, 18, 20–21, 23, 289; economic analogy to, 21 recovery rates: differences in, 89; difficulty of predicting, 41; models from epidemiology and, 20, 21, 23–24, 290; new technology leading to changes in, 273, 275; varying through time, 295.

Savvy marketers and promoters then amplify them in an attempt to profit from them. In addition to popular narratives, there are also professional narratives, shared among communities of intellectuals, that contain complex ideas that subtly affect broader social behavior. One such professional narrative, the random walk theory of speculative prices, holds that prices in the stock market incorporate all information, thus implying that attempts to beat the market are futile. This narrative has an element of truth to it, as professional narratives generally do, though there is now a professional literature that finds imperfections not predicted by the theory.

For example, one distorted narrative states that a buy-and-hold strategy in the domestic stock market is the best investment decision. That narrative conflicts with the professional canon, despite the popular idea that the buy-and-hold strategy comes from scholarly research. Like the popular interpretation of the random walk, some distorted narratives have an economic impact for generations. As with any kind of historical reconstruction, we cannot go back in time with a sound recorder to capture the conversations that created and spread the narratives, so we have to rely on indirect sources. However, we can now capture the arc of contemporary narratives through social media and other tools, such as Google Ngrams.


How I Became a Quant: Insights From 25 of Wall Street's Elite by Richard R. Lindsey, Barry Schachter

Albert Einstein, algorithmic trading, Andrew Wiles, Antoine Gombaud: Chevalier de Méré, asset allocation, asset-backed security, backtesting, bank run, banking crisis, Bear Stearns, Black-Scholes formula, Bob Litterman, Bonfire of the Vanities, book value, Bretton Woods, Brownian motion, business cycle, business process, butter production in bangladesh, buy and hold, buy low sell high, capital asset pricing model, centre right, collateralized debt obligation, commoditize, computerized markets, corporate governance, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency risk, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, fixed income, full employment, George Akerlof, global macro, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, Ivan Sutherland, John Bogle, John von Neumann, junk bonds, linear programming, Loma Prieta earthquake, Long Term Capital Management, machine readable, margin call, market friction, market microstructure, martingale, merger arbitrage, Michael Milken, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Reminiscences of a Stock Operator, Richard Feynman, Richard Stallman, risk free rate, risk-adjusted returns, risk/return, seminal paper, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, subscription business, systematic trading, technology bubble, The Great Moderation, the scientific method, too big to fail, trade route, transaction costs, transfer pricing, value at risk, volatility smile, Wiener process, yield curve, young professional

Burton Malkiel at Princeton later popularized these views in A Random Walk Down Wall Street, published in 1973. Academic analyses of the burgeoning amount of available data seemed to support market efficiency. Computer-enabled dissections of actual market prices suggested that price changes followed a random walk. Furthermore, Michael Jensen, one of Fama’s doctoral students, analyzed mutual fund performance from 1945 to 1964 and found that professional managers had not outperformed the market. If one could not predict security prices, active management was futile. The solution seemed to be to shift the emphasis from security selection to constructing portfolios that offered the market’s return with the market’s risk.

For those who argue that it is the particular securities, not the factor exposure that generates the sought-for returns, I suggest ranking three or more securities as investment candidates in each investment sector considered. Wait a couple of months and correlate those rankings against the performance observed over those months. Do this for a number of sectors for a number of time periods and you will develop both a sense of humility and an appreciation of the random walk hypotheses. Articles During my travels, I have coauthored three articles, the essence of which is not included in the stories above. In 1998, Andre Perold and I coauthored “The Free Lunch in Currency Hedging: Implications for Investment Policy and Performance Standards.”11 We argued that, because currency boasts a long-term expected return that is close to zero, the sizable effects of currency risk can be removed with minimal transaction costs without the portfolio suffering much of a reduction in long-term return.

Pure returns also tend to be much less volatile than their naı̈ve counterparts, because they capture more signal and less noise. Consider a naı̈ve analysis of returns to low price/book. As most utilities have low-price/book ratios, a naı̈ve return to low price/book will be affected by events such as oil-price shocks, which are relevant to the pricing of utility stocks but not necessarily to the pricing of other stocks with lowprice/book ratios. By contrast, a pure return to price/book controls for the noise introduced by industry-related effects. By providing a clearer picture of the precise relationships between stock price behavior, company fundamentals, and economic conditions, disentangling improves return predictability.


pages: 484 words: 136,735

Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis by Anatole Kaletsky

"World Economic Forum" Davos, Alan Greenspan, bank run, banking crisis, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Black Swan, bond market vigilante , bonus culture, Bretton Woods, BRICs, business cycle, buy and hold, Carmen Reinhart, classic study, cognitive dissonance, collapse of Lehman Brothers, Corn Laws, correlation does not imply causation, creative destruction, credit crunch, currency manipulation / currency intervention, currency risk, David Ricardo: comparative advantage, deglobalization, Deng Xiaoping, eat what you kill, Edward Glaeser, electricity market, Eugene Fama: efficient market hypothesis, eurozone crisis, experimental economics, F. W. de Klerk, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, foreign exchange controls, full employment, geopolitical risk, George Akerlof, global rebalancing, Goodhart's law, Great Leap Forward, Hyman Minsky, income inequality, information asymmetry, invisible hand, Isaac Newton, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kickstarter, laissez-faire capitalism, long and variable lags, Long Term Capital Management, low interest rates, mandelbrot fractal, market design, market fundamentalism, Martin Wolf, military-industrial complex, Minsky moment, Modern Monetary Theory, Money creation, money market fund, moral hazard, mortgage debt, Nelson Mandela, new economy, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, paradox of thrift, Pareto efficiency, Paul Samuelson, Paul Volcker talking about ATMs, peak oil, pets.com, Ponzi scheme, post-industrial society, price stability, profit maximization, profit motive, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, rising living standards, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, seminal paper, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, special drawing rights, statistical model, systems thinking, The Chicago School, The Great Moderation, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, Vilfredo Pareto, Washington Consensus, zero-sum game

The assumption that financial markets were “efficient” also meant that, in the absence of new and genuinely unpredictable information, financial market movements would be meaningless random fluctuations, equivalent to tossing a coin or a drunken sailor’s random walk. This chaotic-sounding view was actually reassuring to investors and bankers. For if market movements were really just random coin tosses, they would be highly predictable over longer periods, in the same way that the profits of a lottery or the takings of a casino can be reliably predicted. Specifically, the coin tossing or random walk analogies could be shown by simple mathematics to imply what statisticians call a Normal, or Gaussian, probability distribution over any reasonable period of time.

If the Efficient Market Hypothesis had been valid, fairly simple and logically irrefutable mathematical calculations could have been used to show that most of the financial crises of the past twenty years were literally impossible. For example, if the daily fluctuations on Wall Street had really followed a random walk, the odds of a one-day movement greater than 25 percent would be about one in three trillion. In reality, however, at least three such statistically “impossible” events occurred during just twenty years when EMH was the dominant financial orthodoxy: in the stock market during the 1987 crash, in bonds and currencies in 1994, and in interest rate arbitrage in 1998, when Russia defaulted and the Long Term Capital Management hedge fund sensationally collapsed.

Even if the exhaustion of global oil supplies happens sooner than expected and sends energy prices sharply higher, this will not produce inflation unless wages rise in tandem. If labor’s bargaining power remains weak, rising oil prices will simply reduce the amount of money people have to spend on other goods and services. Thus, dwindling oil supplies will lead to big shifts in relative prices between oil and other goods but not to an increase in the average price level of all goods. The same will be true if energy prices rise substantially, as they probably will, to promote investment in more secure and less polluting energy sources.


pages: 263 words: 84,410

Tulipomania: The Story of the World's Most Coveted Flower & the Extraordinary Passions It Aroused by Mike Dash

fixed income, Ponzi scheme, random walk, South Sea Bubble, spice trade, trade route, tulip mania

The history of the tulip to the present day Krelage, Drie Eeuwen Bloembollen-export, pp. 15–18. Craze for dahlias Bulgatz, Ponzi Schemes, pp. 108–09. During this episode there was even talk of the propagation of blue dahlias—as much a botanical impossibility as the black tulip. Craze for gladioli Posthumus, “Tulip Mania in Holland,” p. 148. Chinese spider lily mania Malkiel, Random Walk down Wall Street, pp. 82–83. Florida land boom Bulgatz, Ponzi Schemes, pp. 46–75. BIBLIOGRAPHY Unpublished Material Municipal Archives, Haarlem Notarial registers, vols. 120–50 Burial registers, vols. 70–76 Index to Heerenboek Manuscript entitled Aanteekeningen van C. J. Gonnet Betreffende de Dovestalmanege in de Grote Houstraat, de Schouwburg op het Houtplein, het Stadhuis in de Frase Tijd, Haarlemse Plateelbakkers en Plateelbakkerijen en de Tulpomanie van 1637–1912 Stadsbibliotheek, Haarlem Chrispijn van de Passe, Een Cort Verhael van den Tulipanen ende haere Oefeninghe… (contemporary pamphlet, n.p., n.d., c. 1620?)

Entrepreneurs and Entrepreneurship in Modern Times: Merchants and Industrialists Within the Orbit of the DutchStaple Market. The Hague, 1995. Mackay, Charles. Memoirs of Extraordinary Popular Delusions and the Madness of Crowds. Ware: Wordsworth Editions, 1995. Malcolm, Noel. Kosovo: A Short History. London: Macmillan, 1998. Malkiel, Burton. A Random Walk down Wall Street. New York: W.W. Norton,1996. Mansel, Philip. Constantinople: City of the World’s Desire, 1453–1924. London: John Murray, 1995. Martels, Z. R. M. W. von. Augerius Gislenius Busbequius: Leven en Werk van de Keizerlijke Gezant aan het hof van Süleyman de Grote. Unpublished Ph.D. diss., University of Groningen, 1989.

Wood-backed slates were given to both the buyer and the seller, and the florist who wished to buy would jot down the price he was prepared to pay on his slate; but he would choose a sum well below the actual value of the bulbs he wanted. The seller would name his own price on another slate, and naturally that would be exorbitantly high. The two bids would then be passed to intermediaries nominated by the principals, and they would mutually agree on what they considered a fair price. This sum would fall somewhere between the two prices written on the slates, but certainly not necessarily in the middle. The compromise price would then be scrawled on the slates, and the boards would be passed back to the florists.


pages: 332 words: 81,289

Smarter Investing by Tim Hale

Albert Einstein, asset allocation, buy and hold, buy low sell high, capital asset pricing model, classic study, collapse of Lehman Brothers, corporate governance, credit crunch, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, eurozone crisis, fiat currency, financial engineering, financial independence, financial innovation, fixed income, full employment, Future Shock, implied volatility, index fund, information asymmetry, Isaac Newton, John Bogle, John Meriwether, Long Term Capital Management, low interest rates, managed futures, Northern Rock, passive investing, Ponzi scheme, purchasing power parity, quantitative easing, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, South Sea Bubble, technology bubble, the rule of 72, time value of money, transaction costs, Vanguard fund, women in the workforce, zero-sum game

You would, in this case, not expect to achieve continuing superior profits from investment decisions that you make because any short-term market-beating investment ideas, reflecting the mispricing of assets, would be quickly spotted by all the other smart professionals and the misalignment between price and value would disappear. If a market is efficient, you would conclude that it should be difficult to beat it, particularly after all costs are taken into account. Rex Sinquefield, an economist and proponent of index investing, provides his own humorous slant on the debate: ‘So who still believes that markets don’t work? Apparently it is only the North Koreans, the Cubans and the active managers.’ This theory is known as the Efficient Market Hypothesis or EMH and is eloquently described in the seminal text A Random Walk Down Wall Street by Burton G. Malkiel, which I would recommend you to read if you want to pursue this topic further.

Keane, S. (2000) Index funds in a bear market, a monograph published by Glasgow University in association with Virgin Direct. Laise, E. (2009) ‘Best stock fund of the decade: CEM Focus’, Wall Street Journal, 31 December (www.wsj.com). LeBaron, D. Vaitlingam, R. and Pitchford, M. (1999) The Ultimate Book of Investment Quotations. Oxford: Capstone. Malkiel, B. G. (1999) A random walk down Wall Street. New York: W. W. Norton. Malkiel, B. G. (2000) ‘Are markets efficient? Yes even if they make errors’, The Wall Street Journal, 28 December, p. A10. Rhodes, M. (2000) Past imperfect? The performance of UK equity managed funds. London: FSA, FSA Occasional Paper Series 9 (www.fas.gov.uk/pubs.occpapers/op09.pdf).

It suggests that all known relevant information is incorporated in the current price of a security. Collectively, all the individual shares aggregate to form the market, which as a consequence is efficiently priced. Intuitively, the more analysts, journalists, brokers and lenders dig around companies, the more likely it is that all information is known about them. Some research estimates that new information is fully priced within sixty minutes (Chordia et al., 2003). As such, in an efficient market it is hard to find securities that are anomalously priced. The price of the security will only move again on any news that is unanticipated. Price movements are therefore random in their nature.


pages: 502 words: 132,062

Ways of Being: Beyond Human Intelligence by James Bridle

Ada Lovelace, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Anthropocene, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big Tech, Black Lives Matter, blockchain, Californian Ideology, Cambridge Analytica, carbon tax, Charles Babbage, cloud computing, coastline paradox / Richardson effect, Computing Machinery and Intelligence, corporate personhood, COVID-19, cryptocurrency, DeepMind, Donald Trump, Douglas Hofstadter, Elon Musk, experimental subject, factory automation, fake news, friendly AI, gig economy, global pandemic, Gödel, Escher, Bach, impulse control, James Bridle, James Webb Space Telescope, John von Neumann, Kickstarter, Kim Stanley Robinson, language acquisition, life extension, mandelbrot fractal, Marshall McLuhan, microbiome, music of the spheres, negative emissions, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, peer-to-peer, planetary scale, RAND corporation, random walk, recommendation engine, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley ideology, speech recognition, statistical model, surveillance capitalism, techno-determinism, technological determinism, technoutopianism, the long tail, the scientific method, The Soul of a New Machine, theory of mind, traveling salesman, trolley problem, Turing complete, Turing machine, Turing test, UNCLOS, undersea cable, urban planning, Von Neumann architecture, wikimedia commons, zero-sum game

Just as a deep dive into our evolutionary history – deploying the most finely tuned, sense-making apparatuses we have devised – reveals not a single answer to the question of life but a chaotic multiplicity of beings, so our closest approach to mathematical truths about the universe involves aligning ourselves with the most chaotic, the most unpredictable, the most random processes we can comprehend. The genius of Monte Carlo was to recognize that the most efficient search of a complex territory is the random walk. Its results inform many of our computational processes today. Notably, Monte Carlo gives us the ability to sift the overwhelming abundance of information available to us on the internet, as the web-crawling bots of search engines spider their way, at random, across the complex territory of the infosphere, in order to draw statistical inferences about its contents.

Here, Cage used the I Ching to create a random list of specific intersections and urban locations around Chicago, which the audience was encouraged to visit in order to generate their own chance-driven experience of each spot’s unique sounds, sensations and encounters. Cage desired ‘to hold together extreme disparities’, as one finds these disparities held together in nature ‘or on a city street’. A Dip in the Lake is a composition for a random walk: computationally, the most efficient exploration of a complex and unknowable territory, and also the one most likely to produce interesting and stimulating encounters. John Cage’s score for A Dip in the Lake: Ten Quicksteps, Sixty-Two Waltzes, and Fifty-Six Marches for Chicago and Vicinity, 1978.

We can see these historical processes at work on the internet today. The first search engines were hand-curated lists of interesting places, essentially random accumulations of sites and tools ordered only by the passions and peccadilloes of those who assembled them. While Google still searches the web with automated random walks, its results are ordered by deeply partisan algorithms, with the top results sold off to the highest bidder. Google has almost a 90 per cent share of the world’s web searches, yet indexes only a tiny fraction of the visible web. Most searchers never look beyond the first page of results. There is little room for randomness in exploring the vast amount of information actually available to us.


Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman

cloud computing, crowdsourcing, en.wikipedia.org, first-price auction, G4S, information retrieval, John Snow's cholera map, Netflix Prize, NP-complete, PageRank, pattern recognition, power law, random walk, recommendation engine, second-price auction, sentiment analysis, social graph, statistical model, the long tail, web application

To increase the radius by 1, we examine each edge (u, v) and for each tail length for u we set it equal to the corresponding tail length for v if the latter is larger than the former. 10.10References for Chapter 10 Simrank comes from [8]. An alternative approach in [11] views similarity of two nodes as the propability that random walks from the two nodes will be at the same node. [3] combines random walks with node classification to predict links in a social-network graph. [16] looks at the efficiency of computing simrank as a personalized PageRank. The Girvan–Newman Algorithm is from [6]. Finding communities by searching for complete bipartite graphs appears in [9].

EXAMPLE 5.9One useful topic set is the 16 top-level categories (sports, medicine, etc.) of the Open Directory (DMOZ).6 We could create 16 PageRank vectors, one for each topic. If we could determine that the user is interested in one of these topics, perhaps by the content of the pages they have recently viewed, then we could use the PageRank vector for that topic when deciding on the ranking of pages.□ 5.3.2Biased Random Walks Suppose we have identified some pages that represent a topic such as “sports.” To create a topic-sensitive PageRank for sports, we can arrange that the random surfers are introduced only to a random sports page, rather than to a random page of any kind. The consequence of this choice is that random surfers are likely to be at an identified sports page, or a page reachable along a short path from one of these known sports pages.

N. Afrati, D. Fotakis, and J. D. Ullman, “Enumerating subgraph instances by map-reduce,” http://ilpubs.stanford.edu:8090/1020 [2]F.N. Afrati and J.D. Ullman, “Transitive closure and recursive Datalog implemented on clusters,” in Proc. EDBT (2012). [3]L. Backstrom and J. Leskovec, “Supervised random walks: predicting and recommending links in social networks,” Proc. Fourth ACM Intl. Conf. on Web Search and Data Mining (2011), pp. 635–644. [4]P. Boldi, M. Rosa, and S. Vigna, “HyperANF: approximating the neighbourhood function of very large graphs on a budget,” Proc. WWW Conference (2011), pp. 625–634


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Triumph of the Optimists: 101 Years of Global Investment Returns by Elroy Dimson, Paul Marsh, Mike Staunton

asset allocation, banking crisis, Berlin Wall, Black Monday: stock market crash in 1987, book value, Bretton Woods, British Empire, buy and hold, capital asset pricing model, capital controls, central bank independence, classic study, colonial rule, corporate governance, correlation coefficient, cuban missile crisis, currency risk, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, European colonialism, fixed income, floating exchange rates, German hyperinflation, index fund, information asymmetry, joint-stock company, junk bonds, negative equity, new economy, oil shock, passive investing, purchasing power parity, random walk, risk free rate, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, stocks for the long run, survivorship bias, Tax Reform Act of 1986, technology bubble, transaction costs, yield curve

The standard deviation of annual dividend changes was also identical in the two countries, although the United States was more volatile in the first half-century while the United Kingdom was more volatile from 1950 on. In both countries, annual dividend growth rates appear to have approximated a random walk. The serial correlation coefficients of -0.07 for the United States and -0.08 for the United Kingdom have a standard error of 0.10, and hence were not statistically significantly different from zero. Figure 11-4 shows the average (arithmetic mean) real dividend growth rate for each decade. For the United States, average growth was positive in all decades except for the period of the First World War.

Real dividends have generally grown more slowly than real GDP per capita, and real dividend growth does not appear, as is often assumed, to be positively correlated with GDP growth—if anything, the correlation is negative. The same finding applies to the correlation between GDP growth and total equity returns. Over time, the path of real dividend growth rates appears to approximate a random walk, and growth rates have been quite volatile. Dividend growth is interesting both in its own right and because it plays a key role in valuation models and financial research. For example, in the early literature on excess equity market volatility and speculative bubbles, researchers often assumed that future dividends could proxy for investors’ expectations (see Shiller, 1981) or that investors might expect a constant growth rate of dividends to continue indefinitely (e.g., Barsky and De Long, 1993). 162 Triumph of the Optimists: 101 Years of Global Investment Returns Similar assumptions are made in applying classic valuation models such as the Gordon model, where dividend growth is often linked to GDP growth.

., 198 Philippines, 20 Poland, 20, 21, 41, 67, 222 Portfolio risk, 108 338 Portugal, 20 Prescott, E., 180, 202 Primary market, 18–9 Privatization, 25, 26 Productivity, 43, 48, 97, 189, 223, 224 Purchasing Power Parity (PPP), 7, 91, 95–104, 219 Railroads, 19, 20, 24, 25, 26, 28, 37, 168 Rajan, R.G., 22 Ramaswamy, K., 140 Random walk, 153, 161 Ratzer, E., 294 Rau, P.R., 160, 161 Real exchange rates, 7, 91, 96–103, 105–8 Real interest rates, 68–73 see also interest rates, bond yields Real term premium, 74, 84–7 Recession, 50, 212 Regional exchanges, 20 Regional stock markets, 20 Regulated businesses, 216–7 Regularities, see anomalies Regulation, 3, 18, 163, 186, 216, 217 Reid, K., 141 Reinganum, M., 131 Reward-to-risk ratio, see Sharpe ratio Repurchases, 143, 149, 158–63, 177, 191 Risager, O., 244 Risk, 54–62 see also currency risk, default risk, market risk, portfolio risk, risk premium, volatility Risk aversion, 163, 179– 81, 188 Risk Measurement Service, 27 Risk premium, 4, 8–10, 34–44, 45, 53, 55, 61, 63, Triumph of the Optimists: 101 Years of Global Investment Returns 74, 81, 88, 89, 195–210, 211–9, 220–4 Risk premium, historical, 163–75 Risk premium, prospective, 176–94 Risk premium, relative to bills, 163–8 Risk premium, relative to bonds, 169–73 Risk-free rate puzzle, 202 Roden, D., 38 Romania, 20 Rose, H.B., xi Rosenberg, B., 141 Ross, S., 41, 211, 212 Rouwenhorst, K.G., 116, 117, 118, 123 Rowley, I., 145 Royal Dutch Shell, 29 Russia, 20, 21, 22, 41, 67, 222 Ryan, R., 156 Sallee, P., 249 San Francisco, 20 Sandez, M., 284 Schaefer, S.M., xi, 85 Scherbina, A., 177 Scheurkogel, A.E., 279 Schumann, C.G.W., 279 Schwartz, D., 135 Schwartz, E.S., 35 Schwartz, S.S., 199, 269 Schwert, G.W., 39, 70 Seasonality, 7, 8, 124, 135– 8, 223 Secondary market, 18–9 Second World War, 36, 58, 70, 71, 73, 76, 79, 94, 98, 116, 122, 152, 189, 195, 221, 224 Sectors, 4, 17, 23–28, 35, 36, 37, 138, 188, 299 Sell-in-May, 135, 138, September 11th 2001, 47, 58, 117, 168, 177, 178, 213 Shares, see equities Sharpe ratio, 105, 111–4, 208 Sharpe, K.P., 239 Sharpe, W.F., xi, 105, 111, 112, 113, 145, 180 Shell, 29 Shiller, R.J., 84, 158, 161, 176, 179 Shleifer, A., 141, 147, 180 Siegel, J.J., xi, 40, 126, 141, 156, 176, 195, 197, 201, 206, 222 Siegel, L.B., 206 Siegel’s constant, 195–202 Singapore, 20 Sinquefield, R., 88, 306 Size effect, xi, 4, 7, 8, 124– 38, 142, 144, 208, 223 Size premium, 8, 124–39, 142, 144 Slovenia, 20, 21 Small capitalization stocks, xi, 124–38, 144, 148, 212 see also size effect Small-cap reversal, 131–5 Smithers, A., 195 Solnik, B., 117, 118, 122 South Africa, 279–83 see also cross-country comparisons South Korea, 12, 15 Spain, 284–8 see also cross-country comparisons Spoerer, M., 254 Sri Lanka, 20, 21 Standard & Poors (S&P), 38, 239 Standard errors, 167, 168, 174, 182, 188 Stattman, D., 141 Stehle, R., xii, 254 Stewart, G.B., 181 Stock markets, 3–5, 11–4, 18–33, 40–4, 155–8, 188–94 Stock repurchases, see repurchases Stocks, see equities Stock-level data, 7, 38, 139 Stolper, G., 66 Suarez, J.L., 284 Index Success bias, 6, 34, 36–8, 42–4, 174, 197 Sui, J.A., 211 Sullivan, T., xii Summer effect, 135, 138 Surveys, 179, 185–7, 188 Survivorship bias, 34–8, 41, 142, 173–5, 202, 222, 299 Sweden, 289–93 see also cross-country comparisons Switzer, L., xii, 239 Switzerland, 294–8 see also cross-country comparisons Taiwan, 12, 20, 133, 161 Tax-loss selling, 135–8 Tax management, 205–6 Taxes, 9, 44, 46, 85, 104, 122, 135–8, 140, 158–62, 193, 205–7, 209, 212, 214, 218, 219, 254, 301 Taylor, A.M., 97, 99 Taylor, B., xii Technological change, 23–4, 189, 223–4 Technology, 23, 25, 26, 28, 199 Term premium, 74, 84–7 Terrorism, 3, 4, 58, 168, 210, 213 Thaler, R., 176 Thomas, J., 188 Thomas, W.A., 259 Time-of-the-day effect, 135 Timmermann, A., xii, 244 Transactions costs, 46, 189, 207 Treasury bills, see bills Treasury inflationprotected securities (TIPS), 74, 84–7, 90, 212 Treynor, J.L., xi, 207, 208 Triangles, 227, 228 Triumph of the Optimists, xi, 176, 224 Turkey, 20, 21, 22 Turn-of-the-year effect, 135–9 339 Twenty-first century, 17, 118, 119, 158, 184, 190, 195, 210 United Kingdom, 23–32, 36–8, 48–50, 63–5, 78, 84–7, 95, 126–9, 135–8, 142–5, 149–53, 190–3, 198–9, 299–305 see also cross-country comparisons United States, 23–32, 45– 7, 54–61, 63–5, 68–70, 74–8, 81–2, 84–9, 95, 124–6, 135–8, 139–42, 149–53, 158–61, 163–6, 169–71, 186–7, 190–3, 196–7, 306–10 see also cross-country comparisons Uppal, R., 117 Urquhart, M.C., 239 Uruguay, 20 US economy, 3, 35, 62, 166, 222 Valbuena, S.F., xii, 284 Valuation, 18, 139, 149, 155, 161, 162, 177–9, 191, 211–7 Value investing, 139–48 Value-growth effect, 139– 48 Value-growth premium, 139–48 Value stocks, 8, 139–48 van Nieuwerburgh, S., 234 van Schaik, F., xii, 274 Vandellos, J.A., 284 Velioti, A.M., xii Venezuela, 20, 21 Vermaelen, T., 160, 161 Violi, R., 264 Vishny, R., 141 Vodafone, 18, 23, 28, 30, 31, 218 Volatility, 54–62, 77–83, 91–9, 105–8, 114, 123, 144, 152, 161, 163, 178– 84, 187, 195–210, 219, 221 see also risk Wada, K., xii, 269 Wall Street Crash, 22, 47, 58, 116, 122, 224 Warnock, F.E., 120, 121 Weekend effect., 135 Weights, 24, 40, 279, 311 Weil, P., 202 Weisbach, M.S., 159 Welch, I., 185–7, 188 Westerfield, R.W., 211, 212 Weston, F., 211 Whelan, S., 259 White, E.N., 19 Williams, J.B., 139 Wilshire Associates, 46, 58, 306 Wilshire 5000, 46, 58, 306 Wilson, J.W., xii, 35, 39, 46, 306 Window-dressing, 135–6, Woodward, G.T., 85 World Bank, 12, 15, 93 World Index, 7, 10, 39–40, 108–14, 119, 123, 166, 167, 168, 171–5, 184–5, 187, 192, 193, 202, 216, 219, 220, 223, 311–5 World ex-US index, 109– 11 World markets, 5, 11–4, 32, 50–1, 123, 138 World Trade Center, see September 11th 2001 World wars, 36, 37, 44, 47, 58, 69, 70, 71, 73, 75, 76, 79, 93, 94, 98, 116, 122, 123, 152, 153, 189, 195, 221, 224 see also First World War and Second World War Wright, S., 195 Wydler, D., xii, 294 Xu, Y., 42, 57, 118 Yield, see bond yield and dividend yield Yield curve, 81 Yugoslavia, 20 Ziemba, W.T., 199, 269 Zingales, L., 22


Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Bear Stearns, behavioural economics, Berlin Wall, Bernie Madoff, book value, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, Cornelius Vanderbilt, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, flying shuttle, Glass-Steagall Act, Gordon Gekko, Henri Poincaré, Henry Singleton, high net worth, impact investing, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, John Bogle, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, low interest rates, managed futures, margin call, means of production, Menlo Park, merger arbitrage, Michael Milken, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Performance of Mutual Funds in the Period, Ponzi scheme, Post-Keynesian economics, price mechanism, principal–agent problem, profit maximization, proprietary trading, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Sand Hill Road, Savings and loan crisis, seminal paper, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, tail risk, technology bubble, Teledyne, The Wealth of Nations by Adam Smith, time value of money, tontine, too big to fail, transaction costs, two and twenty, underbanked, Vanguard fund, working poor, yield curve

Another place where this behavioral lens has been applied to financial markets beyond the equity premium puzzle is momentum. Recent work has looked at momentum in the markets by analyzing serial correlations through time. The idea is that a perfectly efficient market that incorporates all information in prices instantaneously should be a statistical random walk, and a random walk should not exhibit consistent correlations with itself, or “autocorrelations,” through time. Thus, detecting serial autocorrelations may undermine the notion of efficient markets. The behavioral school has proposed two explanations for these results. First, it could be that there are feedback effects whereby market participants see the market rising and decide to buy in; or equivalently, participants could see it falling and then sell their own positions, a reaction also known as the bandwagon effect.

So, they worked together to get to the bottom of it, only to find that much of the earlier work was rife with inconsistencies and ambiguity.12 They determined that new work was required, and that was precisely what they produced, publishing their results in the American Economic Review in 1958 with a paper entitled “The Cost of Capital, Corporation Finance and the Theory of Investment.”13 The result was the Modigliani-Miller theorem, which was among the work for which Merton Miller would win the Nobel Prize in Economics in 1990 and would help Modigliani win the 1985 prize (along with his work on the life-cycle hypothesis).14 The Modigliani-Miller theorem first established several conditions under which their results would hold: no taxes or bankruptcy costs, no asymmetric information, a random walk pricing process, and an efficient market. If these conditions hold, the value of a firm should be unaffected by the capital structure it adopts. In other words, the sum of the value of the debt and the value of the equity should remain constant regardless of how 234 Investment: A History that sum is distributed across debt and equity individually.

Having died of cancer in 1995, two years before the awarding of the prize (not generally awarded posthumously except in a few historical cases when the intended recipient died after being nominated), he was ineligible.23 The Emergence of Investment Theory 237 Where Drawer 1 Has Taken Us As asset pricing encompasses two fields, the theory of pricing of financial assets in general and the pricing of derivatives, there are two rather different answers to the question of where these achievements have taken us. When it comes to derivatives pricing, particularly the work kicked off by Merton, Samuelson, Black, and Scholes, as well as a flurry of important but less seminal work, the answer is fairly straightforward. The theory of derivatives pricing is largely resolved and can tell us quite successfully what the price of an option on a particular stock should be, given the stock’s price. It is not to say that there is nothing left to do, but rather that we now more or less know what a solution “should look like.” Typically, the modifications for derivatives pricing involve either alterations of some now well-known differential equations or, in the case where there is no explicit mathematical solution, the use of computer simulations.


pages: 266 words: 86,324

The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow

Albert Einstein, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Atul Gawande, behavioural economics, Brownian motion, butterfly effect, correlation coefficient, Daniel Kahneman / Amos Tversky, data science, Donald Trump, feminist movement, forensic accounting, Gary Kildall, Gerolamo Cardano, Henri Poincaré, index fund, Isaac Newton, law of one price, Monty Hall problem, pattern recognition, Paul Erdős, Pepto Bismol, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, Ronald Reagan, Stephen Hawking, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, V2 rocket, Watson beat the top human players on Jeopardy!

The molecules fly first this way, then that, moving in a straight line only until deflected by an encounter with one of their sisters. As mentioned in the Prologue, this type of path—in which at various points the direction changes randomly—is often called a drunkard’s walk, for reasons obvious to anyone who has ever enjoyed a few too many martinis (more sober mathematicians and scientists sometimes call it a random walk). If particles that float in a liquid are, as atomic theory predicts, constantly and randomly bombarded by the molecules of the liquid, one might expect them to jiggle this way and that owing to the collisions. But there are two problems with that picture of Brownian motion: first, the molecules are far too light to budge the visible floating particles; second, molecular collisions occur far more frequently than the observed jiggles.

Janet Maslin, “His Heart Belongs to (Adorable) iPod,” New York Times, October 19, 2006. 13. Hans Reichenbach, The Theory of Probability, trans. E. Hutton and M. Reichenbach (Berkeley: University of California Press, 1934). 14. The classic text expounding this point of view is Burton G. Malkiel, A Random Walk Down Wall Street, now completely revised in an updated 8th ed. (New York: W. W. Norton, 2003). 15. John R. Nofsinger, Investment Blunders of the Rich and Famous—and What You Can Learn from Them (Upper Saddle River, N.J.: Prentice Hall, Financial Times, 2002), p. 62. 16. Hemang Desai and Prem C.

The theory for which Bayes is known today came to light on December 23, 1763, when another chaplain and mathematician, Richard Price, read a paper to the Royal Society, Britain’s national academy of science. The paper, by Bayes, was titled “An Essay toward Solving a Problem in the Doctrine of Chances” and was published in the Royal Society’s Philosophical Transactions in 1764. Bayes had left Price the article in his will, along with £100. Referring to Price as “I suppose a preacher at Newington Green,” Bayes died four months after writing his will.3 Despite Bayes’s casual reference, Richard Price was not just another obscure preacher. He was a well-known advocate of freedom of religion, a friend of Benjamin Franklin’s, a man entrusted by Adam Smith to critique parts of a draft of The Wealth of Nations, and a well-known mathematician.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, global macro, high net worth, implied volatility, index arbitrage, index fund, Jim Simons, junk bonds, London Interbank Offered Rate, Long Term Capital Management, low interest rates, managed futures, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, random walk, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, subprime mortgage crisis, survivorship bias, tail risk, transaction costs, two-sided market, value at risk, yield curve

See Minimum acceptable return (MAR) ratio Marcus, Michael Margin Margin calls Marginal production loss Market bubbles Market direction Market neutral fund Market overvaluation Market panics Market price delays and inventory model of Market price response Market pricing theory Market psychology Market risk Market sector convertible arbitrage hedge funds and CTA funds hidden risk long-only funds market dependency past and future correlation performance impact by strategy Market timing skill Market-based risk Maximum drawdown (MDD) Mean reversion Mean-reversion strategy Merger arbitrage funds Mergers, cyclical tendency Metrics Minimum acceptable return (MAR) ratio and Calmar ratio Mispricing Mocking Monetary policy Mortgage standards Mortgage-backed securities (MBSs) Mortgages Multifund portfolio, diversified Mutual fund managers, vs. hedge fund managers Mutual funds National Futures Association (NFA) Negative returns Negative Sharpe ratio, and volatility Net asset valuation (NAV) Net exposure New York Stock Exchange (NYSE) Newsletter recommendation NINJA loans Normal distribution Normally distributed returns Notional funding October 1987 market crash Offsetting positions Option ARM Option delta Option premium Option price, underlying market price Option timing Optionality Out-of-the-money options Outperformance Pairs trading Palm Palm IPO Palm/3 Com Past high-return strategies Past performance back-adjusted return measures evaluation of going forward with incomplete information visual performance evaluation Past returns about and causes of future performance hedge funds high and low return periods implications of investment insights market sector past highest return strategy relevance of sector selection select funds and sources of Past track records Performance-based fees Portfolio construction principles Portfolio fund risk Portfolio insurance Portfolio optimization past returns volatility as risk measure Portfolio optimization software Portfolio rebalancing about clarification effect of reason for test for Portfolio risks Portfolio volatility Price aberrations Price adjustment timing Price bubble Price change distribution The price in not always right dot-com mania Pets.com subprime investment Pricing models Prime broker Producer short covering Professional management Profit incentives Pro-forma statistics Pro-forma vs. actual results Program sales Prospect theory Puts Quantitative measures beta correlation monthly average return Ramp-up period underperformance Random selection Random trading Random walk process Randomness risk Rare events Rating agencies Rational behavior Redemption frequency notice penalties Redemption liquidity Relative velocity Renaissance Medallion fund Return periods, high and low long term investment S&P performance Return retracement ratio (RRR) Return/risk performance Return/risk ratios vs. return Returns comparison measures relative vs. absolute objective Reverse merger arbitrage Risk assessment of for best strategy and leverage measurement vs. failure to measure measures of perception of vs. volatility Risk assessment Risk aversion Risk evaluation Risk management Risk management discipline Risk measurement vs. no risk measurement Risk mismeasurement asset risk vs. failure to measure hidden risk hidden risk evaluation investment insights problem source value at risk (VaR) volatility as risk measure volatility vs. risk Risk reduction Risk types Risk-adjusted allocation Risk-adjusted return Risk/return metrics Risk/return ratios Rolling window return charts Rubin, Paul Rubinstein, Mark Rukeyser, Louis S&P 500, vs. financial newsletters S&P 500 index S&P returns study of Sasseville, Caroline Schwager Analytics Module SDR Sharpe ratio Sector approach Sector funds Sector past performance Securities and Exchange Commission (SEC) Select funds, past returns and Selection bias Semistrong efficiency Shakespearian monkey argument Sharpe ratio back-adjusted return measures vs.

In the first instance, according to the efficient market hypothesis, price declines are responses to negative changes in fundamentals rather than selling begetting more selling, as was the case in portfolio insurance. In the second, the efficient market hypothesis asserts that the overall market price is always correct—a contention that makes an adjustment from a price overvaluation a self-contradiction. The efficient market hypothesis is inextricably linked to an underlying assumption that market price changes follow a random walk process (that is, price changes are normally distributed7). The assumption of a normal distribution allows one to calculate the probability of different-size price moves.

., calls with strike prices below the market price and puts with strike prices above the market price) are said to be in-the-money. Options that have no intrinsic value are called out-of-the-money options. Options with a strike price closest to the market price are called at-the-money options. An out-of-the-money option, which by definition has an intrinsic value equal to zero, will still have some value because of the possibility that the market price will move beyond the strike price prior to the expiration date. An in-the-money option will have a value greater than the intrinsic value because, if priced at the intrinsic value, a position in the option would always be preferred to a position in the underlying market.


pages: 1,544 words: 391,691

Corporate Finance: Theory and Practice by Pierre Vernimmen, Pascal Quiry, Maurizio Dallocchio, Yann le Fur, Antonio Salvi

"Friedman doctrine" OR "shareholder theory", accelerated depreciation, accounting loophole / creative accounting, active measures, activist fund / activist shareholder / activist investor, AOL-Time Warner, ASML, asset light, bank run, barriers to entry, Basel III, Bear Stearns, Benoit Mandelbrot, bitcoin, Black Swan, Black-Scholes formula, blockchain, book value, business climate, business cycle, buy and hold, buy low sell high, capital asset pricing model, carried interest, collective bargaining, conceptual framework, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, currency risk, delta neutral, dematerialisation, discounted cash flows, discrete time, disintermediation, diversification, diversified portfolio, Dutch auction, electricity market, equity premium, equity risk premium, Eugene Fama: efficient market hypothesis, eurozone crisis, financial engineering, financial innovation, fixed income, Flash crash, foreign exchange controls, German hyperinflation, Glass-Steagall Act, high net worth, impact investing, implied volatility, information asymmetry, intangible asset, interest rate swap, Internet of things, inventory management, invisible hand, joint-stock company, joint-stock limited liability company, junk bonds, Kickstarter, lateral thinking, London Interbank Offered Rate, low interest rates, mandelbrot fractal, margin call, means of production, money market fund, moral hazard, Myron Scholes, new economy, New Journalism, Northern Rock, performance metric, Potemkin village, quantitative trading / quantitative finance, random walk, Right to Buy, risk free rate, risk/return, shareholder value, short selling, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, Steve Jobs, stocks for the long run, supply-chain management, survivorship bias, The Myth of the Rational Market, time value of money, too big to fail, transaction costs, value at risk, vertical integration, volatility arbitrage, volatility smile, yield curve, zero-coupon bond, zero-sum game

Future information is, by definition, unpredictable, so changes in the price of a security are random. This is the origin of the random walk character of returns in the securities markets. Competition between financial investors is so fierce that prices adjust to new information almost instantaneously. At every moment, a financial instrument trades at a price determined by its return and its risk. Eugene Fama (1970) has developed the following three tests to determine whether a market is efficient: ability to predict prices; market response to specific events; impact of insider information on the market.

It sometimes requires strong nerves not to give in to the temptation to sell when prices collapse, as happened with stock markets in 1929, 1974, September 2001 and October–November 2008. Since 1900, UK stocks have delivered an average annual return after inflation of 5.3%. Yet, during 38 of those years the returns were negative, in particular in 1974, when investors lost 57% on a representative portfolio of UK stocks. Price trends of some financial assets since 2000 showing very different levels of volatility! Source: Factset If you are statistically inclined, you will recognise the “Gaussian” or “normal” distribution in this chart, showing the random walk of share prices underlying the theory of efficient markets.

Modigliani–Sutch theory monetary items, temporal method monetising assets secondary market money-market funds money, time value of Monte Carlo simulation mortgage loans motivation, management team MOU see memorandum of understanding multifactor models multinational companies multiple IRR multiple shares multiple voting rights shares multiples method menu of minority interests peer-group comparisons and pensions terminal value multiplier effect MVA see market value added national regulations natural disaster risks NAV see net asset value NDA see non-disclosure agreement necessity concept negative capital employed negative covenants negative net debt negative pledge, real estate loans negative signals negative value, cost of capital negative working capital negotiation bank loans choosing a strategy control over companies debt financial manager’s role financial security as contract outcomes of neoclassical theory net of accrued interest, bond quotes net asset value (NAV) cash flow value cash mutual funds shareholder’s equity shares net assets analysis net debt calculating capital employed cash flow/earnings approaches cash flow statement cost of covenant clauses excluding impact of leverage effect movements in cash negative value of working capital relationship net financial expense/(income) see also net income/(profit) net fixed assets net income/(profit) cash flow statement consolidated accounts inventories margin analysis operating profit payout ratio ratio to shareholders’ equity solvency and stability principle value creation write-backs see also net financial expense/(income) net operating profit after tax (NOPAT) net present value (NPV) discounting rate examination of financial securities interest rate investments IRR use real options value creation see also expanded net present value net profit see net income/(profit) net worth, earnings and “new” financial products new issue premium (NIP) new issues market (primary market) new projects option new shareholders share issue share proceeds NIP see new issue premium “no goodwill” nominal rate of return nominal rates, interest nominal value, bonds nominee agreements “non-cash” costs “non-current assets” see fixed assets non-disclosure agreement (NDA) non-investment grade ratings non-monetary items non-operating assets non-operating working capital non-recourse discounting non-recurrent items non-US companies, US listings non-voting shares NOPAT see net operating profit after tax normalised earnings concept normalised free cash flow normalised operating profitability normative analysis normative margin forecasting normative mimicry notional amount forward-forward rate interest rate swaps notional interest deductibility notional pooling NPV see net present value OBO see owner buyout off-balance-sheet commitments offerings certainty of purpose of retail investors types see also initial public offerings OGM see Ordinary General Meeting old shareholders see current shareholders one-tier board of directors ongoing needs, permanent working capital operating activities, cash flow from operating assets see also capital employed operating breakeven operating cash flow operating costs operating cycle assets capital employed cash flow earnings working capital operating flows operating leases operating leverage operating margin operating multiple operating outflows operating performance operating profit allocation company’s EPS relative to formation growth rate income statement formats leverage effect multiples ratio to capital employed see also earnings before interest and taxes operating revenues operating risks operating structure, listed companies operating working capital operational constraints, real estate operational opportunities operational real estate operational subsidiaries operations, company opportunity cost opportunity principle optimal capital structure optimal cash management optimal date, start-ups optimal debt ratio option to abandon option on future spreads option models, start-up date option-pricing models option premium, insurance option value, convertible bonds options analysing definition derivative market insurance and position management stock theoretical foundations time value value parameters warrants and see also real options options on options options theory financial decisions analysis financial securities firm analysis practical applications valuation of equity order book, IPO creation ordinary dividends ordinary full listing Ordinary General Meeting (OGM) ordinary shareholders ordinary shares organic growth, internal financing organisation theories organised markets, OTCs original equipment, market risk OTC markets see over-the-counter markets out of the money options outflows balance sheet financing investment and operating outlook rating outside shareholders outsourcing over-the-counter (OTC) markets overdrafts overlay banks overpayments, investments overproduction problem overstatement, inventories’ value owner buyout (OBO) ownership level, consolidation P&L see profit and loss P-to-P see public-to-private P/E see price to earnings ratio Pac-Man defence “paper” see also financial securities paper distribution by banks paper payment means parent company consolidated accounts dilution profit and losses parent company guarantee (PCG) pari passu clauses participating preference share past prices, efficient markets past situations, breakeven point patents path of wealth (POW) pay-to-play clause payables, managing payback period, investments paying agents payment in advance payment amount investments shares payment in cash, mergers/acquisitions payment clauses, dividends payment means/methods dividends financial systems impact of trade receivables payment periods receivables trade payables working capital payment received, dividends payment in shares payment speed, receivables payment systems, eurozone payment terms hybrid bonds suppliers payout ratio PBO see projected benefit obligation PBR see price to book ratio PCG see parent company guarantee pecking order theory peer comparison method penalties pensions percentage of completion method, construction contracts percentage control, consolidation percentage interest PERCS see preferred equity redemption cumulative stock perfect capital markets theory performance bonds periodicity of coupon payments “permanent financing” permanent working capital perpetuity growth rate to value of personal taxes personnel cost/expense pivoting shift, risk premium poison pills see strategic assets political risk pooling funds portfolio diversification portfolio management bonds options risk premium portfolio risk position, risk management positive cash balance positive covenants postponement of project POW see path of wealth power balance, working capital expressing power distribution structure power of shareholders PPA see purchase price allocation pre-emption rights, shareholder changes pre-emptive action pre-emptive subscription rights pre-marketing period, book-building preference shares preferential dividend preferred equity redemption cumulative stock (PERCS) preferred habitat theory preferred shares see preference shares premium convertible bonds minority discount and options valuation with see also risk premium prepaid costs prepaid interest, bonds present value index (PVI) present value (PV) calculation simplifications cash flows discounting financial securities tax savings see also net present value price arbitrage commodities company value effects exercise price financial securities issuance of bonds negotiating options security issue selling price maximisation underlying asset price to book ratio (PBR) price-driven competition price to earnings ratio (P/E) all-share transactions EPS growth investors and principle of startups value creation price increase analysis price information, financial systems price volatility, stock risk primary market, financial securities principal amount, loans priority periods, issue of shares private auctions private companies, stock exchange private equity funds private equity sponsors private negotiation, control over company private placements pro forma statements probability distribution process-specific production product-driven competition product lifecycle product trends production company’s market in income statement margin analysis production capacity production models production policy clauses productivity, personnel cost profit decreasing fast growth inflation see also operating profit profit before tax and non-recurring items profit and loss (P&L) “adjusted income” capital employed dilution see also income statement profit-generating capacity profitability analysis bankruptcy and competition and EBIT multiple leverage effect margins distinction standard plan structural value creation profitability divisions profitability indicators profitable investments program trading, mimicry project financing project-type organisation projected benefit obligation (PBO) promised return, bonds promissory notes property investors property, plant and equipment see tangible fixed assets proportional rates, interest proportionate method, consolidated accounts provisions for decommissioning/restoration of sites employee benefits/pensions not tax-deductable restructuring for valuation prudence principle psychology public offerings public tender offers public-to-private (P-to-P) LBOs transactions purchase method, goodwill purchase price allocation (PPA) purchases DPO ratio operating costs recession conditions “pure-play” companies put-call parity put options capital employed exercise price put warrants PV see present value PVI see present value index Pythagoras’s theorem qualified institutional buyers (QIBs) quick ratio, company finance R&D (research and development) costs random events response random walk returns ranking hybrid bonds ratchet clause, shareholders’ agreement rate of return accounting book rates capital employed cost of capital creditors/shareholders discounting financing sources internal shares start-ups value and see also required rate of return rate tunnel rating agencies ratings financial analysis impact of role of ratio analysis company finance limits of profitability scoring techniques working capital ratios all-share transactions buy-back impact credit scoring raw materials days of inventory changes operating breakeven RCF see revolving credit facility real estate equity capital finance financial criteria financing choice managing organisation ideals standard loan backing value creation real estate investment trusts (REITs) real versus financial assets real options categories contribution of evaluating value of young companies reasoning/reason main lines of personal taxes in terms of cash flows in terms of incremental flows in terms of opportunity Recasens, G.


Rockonomics: A Backstage Tour of What the Music Industry Can Teach Us About Economics and Life by Alan B. Krueger

"Friedman doctrine" OR "shareholder theory", accounting loophole / creative accounting, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, autonomous vehicles, bank run, behavioural economics, Berlin Wall, bitcoin, Bob Geldof, butterfly effect, buy and hold, congestion pricing, creative destruction, crowdsourcing, digital rights, disintermediation, diversified portfolio, Donald Trump, endogenous growth, Gary Kildall, George Akerlof, gig economy, income inequality, independent contractor, index fund, invisible hand, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kenneth Arrow, Kickstarter, Larry Ellison, Live Aid, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, moral hazard, Multics, Network effects, obamacare, offshore financial centre, opioid epidemic / opioid crisis, Paul Samuelson, personalized medicine, power law, pre–internet, price discrimination, profit maximization, random walk, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, Saturday Night Live, Skype, Steve Jobs, the long tail, The Wealth of Nations by Adam Smith, TikTok, too big to fail, transaction costs, traumatic brain injury, Tyler Cowen, ultimatum game, winner-take-all economy, women in the workforce, Y Combinator, zero-sum game

To use a baseball analogy, skill is required for a baseball player to hit a grand slam, but luck is also necessary to place three of the previous batters on the bases at the time of his at-bat. Harmonizing Good and Bad Luck in Your Portfolio The unpredictability of financial assets such as stocks is well known. Although the stock market does not exactly follow a random path, movements in prices may seem random. “A blindfolded monkey throwing darts at a newspaper’s financial pages,” Burton Malkiel wrote in his 1973 bestseller A Random Walk Down Wall Street, “could select a portfolio that would do just as well as one carefully selected by experts.”28 Experience has borne out this prediction. In 2016, for example, two-thirds of actively managed large-capitalization stock funds underperformed the S&P 500 large-cap index.29 And even when an actively managed fund does beat the overall market index in one year, the odds of it doing so again the next year are not very good.

The Ones Without Principals Are,” Quarterly Journal of Economics 116, no. 3 (2001): 901–32. 26. David Cho and Alan B. Krueger, “Rent Sharing Within Firms,” draft working paper, 2018. 27. This draws from an interview with Cliff Burnstein on Jul. 27, 2018, in New York City. 28. Burton Malkiel, A Random Walk down Wall Street: Including a Life-Cycle Guide to Personal Investing (New York: W. W. Norton, 1999). 29. Burton Malkiel, “Index Funds Still Beat ‘Active’ Portfolio Management,” Wall Street Journal, Jun. 5, 2017. 30. Chana Schoenberger, “Peter Lynch, 25 Years Later: It’s Not Just ‘Invest in What You Know,’ ” MarketWatch, Dec. 28, 2015. 31.

The figures shown each year are total box office revenue divided by total tickets sold. The price for 2018 is estimated based on a regression of the percentage change in the average price for all tours on the percentage change in the average price of the top 100 North American tours from 1996 to 2017, and the percentage change in the price of the top 100 tours in the first half of 2018. Inflation is measured by the CPI-RS, through the first half of 2018. Concert prices have increased especially rapidly relative to inflation since the late 1990s. From 1996 to 2018, the average concert ticket price rose 190 percent, while overall consumer prices rose by 59 percent.


pages: 519 words: 104,396

Priceless: The Myth of Fair Value (And How to Take Advantage of It) by William Poundstone

availability heuristic, behavioural economics, book value, Cass Sunstein, collective bargaining, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, Dr. Strangelove, East Village, en.wikipedia.org, endowment effect, equal pay for equal work, experimental economics, experimental subject, feminist movement, game design, German hyperinflation, Henri Poincaré, high net worth, index card, invisible hand, John von Neumann, Kenneth Arrow, laissez-faire capitalism, Landlord’s Game, Linda problem, loss aversion, market bubble, McDonald's hot coffee lawsuit, mental accounting, meta-analysis, Nash equilibrium, new economy, no-fly zone, Paul Samuelson, payday loans, Philip Mirowski, Potemkin village, power law, price anchoring, price discrimination, psychological pricing, Ralph Waldo Emerson, RAND corporation, random walk, RFID, Richard Thaler, risk tolerance, Robert Shiller, rolodex, social intelligence, starchitect, Steve Jobs, The Chicago School, The Wealth of Nations by Adam Smith, three-martini lunch, ultimatum game, working poor

Summers (now head of the National Economic Council for the Obama administration) was the first to make an extended case for what might now be called the coherent arbitrariness of stock prices. From day to day the market reacts promptly to the latest economic news. The resulting “random walk” of prices has been cited as proof that the market knows true values. Because stock prices already reflect everything known about a company’s future earnings, only the unpredictable stream of financial news, good and bad, can change prices. Summers astutely pointed out that this “proof” doesn’t hold water. The random walk is a prediction of the efficient market model, just as missing your train is a prediction of the Friday-the-13th-is-unlucky theory.

The researchers had them print up some catalogs with the sale prices but without any indication that they were discounted. As you’d expect, they saw higher sales when the sale prices were highlighted as such. Buyers didn’t know that $Y was a bargain price unless the catalog told them it was. Sale price markers were more powerful motivators than charm prices. Consumers were more likely to buy an item marked with the sale price on the left than with the charm price on the right. Anderson and Simester tried both gimmicks together, using sale-marked charm prices like “Reg $48 SALE $39.” This had the strongest effect of all.

Economics investigates how market forces affect prices paid. There is a quite different way of looking at things. Reserve prices can be thought of as a magnitude scale. For a buyer, prices are a numerical measure of desire to possess something. For a seller, prices measure desire to keep what one already has (including such all-important possessions as time, energy, and self-respect). In the common sense of everyday affairs, prices are one-dimensional, like marks on a ruler. For every commodity, there’s a single point on the scale. These points neatly order all the world’s stuff by price. The psychological reality of prices is not that simple.


pages: 819 words: 181,185

Derivatives Markets by David Goldenberg

Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, correlation coefficient, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial engineering, financial innovation, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, law of one price, locking in a profit, London Interbank Offered Rate, Louis Bachelier, margin call, market microstructure, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, price mechanism, random walk, reserve currency, risk free rate, risk/return, riskless arbitrage, Sharpe ratio, short selling, stochastic process, stochastic volatility, time value of money, transaction costs, volatility smile, Wiener process, yield curve, zero-coupon bond, zero-sum game

71; calculation of equilibrium forward prices 78; solution 86; pricing zero-coupon bond with face value equal to current forward price of underlying commodity 73; solution to 86; pricing zero-coupon bonds 72; solution to 86; settling a forward commitment 72; zero-coupon bond, pricing on basis of forward contract at compounded risk-free rate 73 consensus in risk-neutral valuation 598–9; with consensus 599; without consensus 599–601 consumption capital asset pricing model (CCAPM) 605 contango and backwardation 198–9 context in study of options markets 326–7 contingent claim pricing 514–17 continuation region 385 continuous compounding and discounting 69–71 continuous dividends from stocks, modeling yields from 93–4 continuous yields, modeling of 90–4 contract life, payments over 88 contract month listings 214, 215, 228 contract offerings 227–8 contract size 19, 214, 215, 227, 228 contract specifications 17, 18–19 contracts offered 257–8 convenience, risk-neutral valuation by 631–2 convenience yield 89 convergence of futures to cash price at expiration 189 convexity of option price 406 correlation effect 165–6 cost-of-carry 89; model of, spread and price of storage for 195 counterparty risk 11, 12–13, 140 covered call hedging strategy 419–27; economic interpretation of 426–7; protective put strategies, covered calls and 419; writes, types of 420–6 credit spreads 298–9 cumulative distribution function 544 currency futures 213–17; contract specifications 213–15; forward positions vs. futures positions 220; pricing vs. currency forward pricing 225; quote mechanism, future price quotes 216–17; risk management strategies using 217–24 currency spot and currency forwards 103–9 currency swaps, notional value of 274 current costs: of generating alternative payoffs 78; payoffs and 66; related strategies and, technique of going back and forth between 393 current price as predictor of future stock prices 531 daily price limits 228, 229 daily settlement process 144–51, 153; financial futures contracts and 216, 260 dealer intermediated plain vanilla swaps 284–93; arbitraging swaps market 292–3; asked side in 286; bid side in 285; dealer’s spread 286; example of 284–6; hedging strategy: implications of 291–2; outline of 288–90; plain vanilla swaps as hedge vehicles 286–92 dealer’s problem, finding other side to swap 294–8; asked side in 295; bid side in 295; credit spreads in spot market (AA-type firms) 296; dealer swap schedule (AA-type firms) 295; selling a swap 296; swap cash flows 298; synthetic floating-rate financing (AA-type firms) 297; transformation from fixed-rate to floating rate borrowing 297–8 decision-making: option concept in 324; process of, protection of potential value in 36–7 default in forward market contracting 11–12 deferred spot transactions 78–9 delayed exercise premium 331, 337 delivery dates 19 demutualization 139–40 derivative prices: co-movements between spot prices and 26; underlying securities and 66 directional trades 371–2 discounted option prices 527–8 discounted stock price process 524–5, 527–8, 530 discrete-time martingale, definition of 521 diversifiable risk 225 diversification, maximum effect of 419–20 dividend-adjusted geometric mean (for S&P 500) 227 dividend payments, effect on stock prices 94–8 dividend payout process 97, 111; connection between capital gains process and 111–13 dollar equivalency 227, 234, 239–40 dollar returns, percentage rates of 366 domestic economy (DE) 103–4, 105 dominance principle 372, 373; implications of 374–88 double expectations (DE) 534–5 duration for interest-rate swaps 300 dynamic hedging 473–506; BOPM as risk-neutral valuation relationship (RNVR) formula (N > 1) 490–3; hedging a European call option (N=2) 477–85; implementation of binomial option pricing model for (N=2) 485–90; multi-period BOPM model (N=3) 494; multi-period BOPM model (N > 1), path integral approach 493–500; numerical example of binomial option pricing model (N=2) 487–90; option price behavior (N=2) 476; path structure for multi-period BOPM model (N=3) 497; stock price behavior (N=2) 475–6; stock price evolution (N-period binomial process), summary of 499; value contributions for multi-period BOPM model (N=3) 498; see also binomial option pricing model (BOPM) economy-wide factors, risk and 225–6 effective date 293 effective payoff 220, 233 effective price, invoice price on delivery and 153–6 efficient market hypotheses (EMH) 517; features of 532; guide to modeling prices 529–33; option pricing in continuous time 558, 560, 561; semi-strong form of 531; strong form of 531, 532; weak form of 531 EFP eligibility 214 embedded leverage 79–80 endogenous variables 614–15 equilibrium forward prices 402; comparison with equilibrium futures prices 193–5; valuation of forward contracts (assets without dividend yield) 78 equilibrium (no-arbitrage) in full carrying charge market 190–3; classical short selling a commodity 192; Exchange Traded Funds (ETF) 191–2; formal arbitrage opportunity 192; non-interest carrying changes, arb without 192–3; setting up arb 190; unwinding arb 190–2 equity in customer’s account 145, 148 equivalent annual rate (EAR) 70 equivalent martingale measures (EMMs) 507–38; arithmetic Brownian motion (ABM) model of prices 530–1; computation of EMMs 529; concept checks: contingent claim pricing, working with 514; martingale condition, calculation of 525; option pricing, working with 514; two period investment strategy under EMM, proof for (t=0) 521; solution to 538; contingent claim pricing 514–17; concept check: interpretation of pricing a European call option 514; pricing a European call option 514–15; pricing any contingent claim 515–17; current price as predictor of future stock prices 531; discounted option prices 527–8; discounted stock price process 524–5, 527–8, 530; discrete-time martingale, definition of 521; double expectations (DE) 534–5; efficient market hypotheses (EMH) basis for modeling 517; features of 532; guide to modeling prices 529–33; semi-strong form of 531; strong form of 531, 532; weak form of 531; equivalent martingale representation of stock prices 524–6; examples of EMMs 517–21; exercises for learning development of 537; fair game, notion of 518–19; fundamental theorem of asset pricing (FTAP_1) 509, 511–12, 517, 528–9, 530, 532, 533; ‘independence,’ degrees of 536; investment strategy under, two-period example 519–21; key concepts 537; martingale properties 533–6; non-constructive existence theorem for 529; numeraire, concept of 524; option prices, equivalent martingale representation of 526–8; option pricing in continuous time 540; option price representation 543; physical probability measure, martingale hypothesis for 530; pricing states 509; primitive Arrow-Debreu (AD) securities, option pricing and 508–14; concept check: pricing ADu(ω) and ADd(ω) 514; exercise 1, pricing B(0,1) 510; exercise 2, pricing ADu(ω) and ADd(ω) 511–14; random variables 536; random walk model of prices 530–1; risk-averse investment 522; risk-neutral investment 521–2, 523; risk-neutral valuation 596–7; construction of 601–3; risk premiums in stock prices and 532–3; riskless bonds 509; Sharpe ratio 526; state-contingent financial securities 508; ‘state prices’ 509; stock prices and martingales 521–6; sub (super) martingale, definition of 524; summary of EMM approach 528–9; tower property (TP) 533–4; uncorrelated martingale increments (UCMI) 531, 535–6; wealth change, fair game expectation 520 Eurodollar (ED) deposit creation 253 Eurodollar (ED) futures 220–1, 245, 246, 249, 250, 252–64; ‘buying’ and ‘selling’ futures 256; cash settlement, forced convergence and 258–61; contract specifications for 254–5; forced conversion of 260; interest-rate swaps 278; strips of 280–1; lending (offering) 249–50; liabilities and 246; open positions, calculation of profits and losses on 262–4; placing 248–9; quote mechanism 256–8; spot Eurodollar market 245–54; taking 249; timing in 257 European call options: synthesis of: model-based option pricing (MBOP) 453–64; hedge ratio and dollar bond position, definition of (step 2) 455; implications of replication (step 4) 462–4; parameterization (step 1) 454; replicating portfolio, construction of 456–62; replication, pricing by 463; valuation at expiration 446; see also hedging a European call option in BOPM (N=2) European options 328, 333, 342, 357, 375, 398, 445, 553 European Put-Call Parity 416, 417, 418, 419, 426, 429; financial innovation with 401–5; implications of 394–400; American option pricing model, analogue for European options 396–8; European call option 394–6; European option pricing model, interpretation of 397–8; European put option 398–9; synthesis of forward contracts from puts and calls 399–400 exchange membership 139–40 exchange rate risks and currency futures positions 217–20; Lufthansa example 217–20 exchange rates, New York closing snapshot (April 7, 2014) 104 exchange rule in financial futures contracts 214, 228 exchange-traded funds (ETFs) 191–2, 226 exercise of options 328 exercise price 328, 336 exercises for learning development: binomial option pricing model (BOPM) 501–5; equivalent martingale measures (EMMs) 537; financial futures contracts 266–8; hedging with forward contracts 56–61; hedging with futures contracts 205–7; interest-rate swaps 315–16; market organization for futures contracts 158–9; model-based option pricing (MBOP) 469–71; option pricing in continuous time 590–3; option trading strategies 364–6, 431–3; options markets 341–2; rational option pricing (ROP) 409–12; risk-neutral valuation 634–5; spot, forward, and futures contracting 27–9; valuation of forward contracts (assets with dividend yield) 116–17; valuation of forward contracts (assets without dividend yield) 83–5 exit mechanism in forward market contracting 15–16 exogenous variables in risk-neutral valuation 614–15 expiration date in options markets 336 expiration month code 336 fair game, notion of 518–19 fancy forward prices 19, 25 Fed Funds Rate (FFR) 251 Federal Funds (FF) 249–50, 251, 252 Federal Reserve system (US) 249 financial engineering techniques 337–8 financial futures contracts 211–70; all-or-None (AON) orders 215; Bank of International Settlements (BIS) 246; basis risk 223, 237, 238; cross hedging and 244; block trade eligibility 214, 228; block trade minimum 214, 228; commentary 216–17; concept checks: backwardation and contango, markets in?

Commodity prices and stock prices have been intensively studied for a very long time. Many of the early empirical studies found little or no significant correlation between past changes in stock prices and subsequent price changes. This finding was also confirmed for futures prices. This suggested that empirical stock price processes meet the condition of ‘independent increments’. The first model of prices consistent with the empirical findings was the random walk model or what we will call, in continuous time, the arithmetic Brownian motion model (ABM). We will be discussing this model in detail in Chapter 16. Later, it was found that independence was too strong a condition, because the results of the empirical tests seemed only to establish the weaker uncorrelated increments property (see the Appendix, section 15.7, for this distinction).

increment of ABM process 555; shifted arithmetic Brownian motion (ABM) model of prices 541–2; reduced process 570; stochastic differential equations (SDEs) 553, 559, 562–3, 564, 566, 567–8, 570, 571, 583; stochastic integral equations (SIEs) 559, 560, 561, 564, 565–6, 567; stochastic processes 540–1, 543, 562, 587, 588; transition density function for shifted arithmetic Brownian motion 545–6; Wiener measure (and process) 540–1 option sellers 328 option trading strategies 345–67, 415–34; basic (naked) strategies 347–63; ‘calling away’ of stock 422; concept checks: covered call strategies, choice of 426; solution to 434; covered call write, upside potential of 422; solution to 433; cushioning calls 422; In-the-Money covered call writes 421; solution to 433; market for call options, dealing with profit potential and 354; solution to 367; payout present value on longing zero-coupon riskless bond 362; solution to 367; positions taken, definition of risk relative to 427; profit diagram for long call option, working on 418; rationalization of profits, short call positions 357; stock price fluctuations, dealing with 353; solution to 366–7; upside volatility in short positions, dealing with 359; covered call hedging strategy 419–27; economic interpretation of 426–7; covered call writes, types of 420–6; covered calls and protective put strategies 419; diversification, maximum effect of 419–20; Dollar Returns, percentage rates of 366; economic characteristics 358; European Put-Call Parity 416, 417, 418, 419, 426, 429; exercises for learning development of 364–6, 431–3; finite-maturity financial instruments, options as 354; generation of synthetic option strategies from European Put-Call Parity 416–18; In-the-Money covered call writes 421–4; key concepts 364, 431; long a European call option on the underlying 351–5; economic characteristics 353; long a European put option on the underlying 348, 357–9; economic characteristics 358; long a zero-coupon riskless bond and hold to maturity 348, 360–2; economic characteristic 361; long call positions, difference between long underlying positions and 354; long the underlying 347–9; economic characteristics 349; Merck stock price fluctuations 346–7; natural and synthetic strategies 416; natural stock, economic equivalence with synthetic stock 418; Out-of-the-Money covered call writes 424–6; potential price paths 346–7; profit diagrams 346–7; protective put hedging strategy 427–30; economic interpretation of 429–30; insurance, puts as 427–9; puts as insurance 427–9; short a European call option on the underlying 348, 355–7; economic characteristics 357; short a European put option on the underlying 348, 359–60; economic characteristics 360; short a zero-coupon riskless bond and hold to maturity 348, 362–3; economic characteristic 363; short the underlying 348, 349–51; economic characteristics 351; synthetic equivalents on basic (naked) strategies 416–18; synthetic strategies, natural strategies and 416 option valuation: binomial option pricing model (BOPM) 445–8; risk-neutral valuation 624–33; direct valuation by risk-averse investor 626–31; manipulations 624–6; for risk-neutral investors 631–3 options and options scenarios 323–6 Options Clearing Corporation (OCC) 328 options markets 323–44; American options 328; anticipation of selling 339; anticipatory buying 339–40; basic American call (put) option pricing model 332–4; buying back stock 339; CBOE (Chicago Board Options Exchange) 324–5, 334; asked price entries 335, 336; bid entries 335, 336; equity option specifications 343; exchange-traded option contracts 325; last sale entries 335, 336; Merck call options and price quotes 334–7; mini equity option specifications 344; net entries 335, 336; open interest entries 335, 336; volume entries 335, 336; concept checks: individual equity options, product specifications for 326; solution to 342; mini equity options, product specifications for 326; solution to 342; MRK OV-E price quote 337; option positions 331; option sales 332; solution to 342; option’s rights 331; payoff diagram construction 338; put option positions 332; context in study of 326–7; decision-making, option concept in 324; delayed exercise premium 331, 337; European options 328; exercise price 328, 336; exercises for learning development of 341–2; exercising options 328; expiration date 336; expiration month code 336; financial engineering techniques 337–8; immediate exercise value 330; implicit short positions 340; importance of options 323–4; In-the-Money calls 337; insurance features, options and 327; intrinsic value 326, 330, 333, 337; key concepts 341; learning options, framework for 326–7; leverage, options and 327; liquidity option 333; long and short positions, identification of 339–40; long positions 339–40; long vs. short positions 339–40; maturity dates 328; moneyness 329; naked (unhedged) positions 327; non-simultaneous price quote problem 334–6; option buyers 328; option market premiums 328; option sellers 328; options and options scenarios 323–6; Options Clearing Corporation (OCC) 328; options embedded in ordinary securities 324; options in corporate finance 324; payoff and profit diagrams 326, 338; plain vanilla put and call options, definitions and terminology for 327–32; put and call options 323–5, 327, 328, 329, 338; puts and calls, infrastructure for understanding about 337–8; reading option price quotes 334–7; real asset options 324; short positions 339–40; short sales, covering of 339; speculation on option prices 327; standard equity option 336; standard stock option 334; strategic, option-like scenarios 324; strike price 328; strike price code 336; time premium 326, 330–1, 333, 337; underlying assets or scenarios 327, 334; identification of long and short positions in 339–40; see also binomial option pricing model (BOPM); equivalent martingale measures (EMMs); model-based option pricing (MBOP) in real time; rational option pricing (ROP) order execution 125–6; futures contract definition and 126 order submission 125–6 orders, types of 127–34 Out-of-the-Money covered call writes 424–6 over the counter (OTC): markets 12–13, 14, 17 over-the-counter (OTC): bilateral agreements 278 overall profits (and losses) 144, 150, 151, 153, 156, 157 overnight averages 11 par swap rate 294, 301 parameterization 454, 477–8, 502 partial equilibrium (PE) 453; models of, risk-neutral valuation and 614 participants in futures market 122–5 path structures: in binomial process 440–2, 442–4; multi-period BOPM model (N=3) 497; thinking of BOPM in terms of paths 493–9 paying fixed 293; in interest rate derivatives (IRDs) 278–9; and receiving floating in commodity forward contracts 276 payoff and profit: diagrams of 326, 338; difference between 66 payoff position with forward contracts 37 payoff to long forward position in IBM 40 payoff to short forward position in IBM 43 payoffs per share: to naked long forward contract 68–9; to naked long spot position 67, 68–9 perfect negative correlation 166 perfect positive correlation 609–11 performance bonds (margins) 144–5, 148 physical probability: measure of, martingale hypothesis for 530; risk-neutralization of 604 pit trading, order flow process and 136–9 plain vanilla interest-rate swaps 274; dealer intermediated swaps 284–93; non-dealer intermediated swaps 281–4 plain vanilla put and call options, definitions and terminology for 327–32 portfolio price dynamics, replication of 457 portfolio theory, hedging as 165–8 portfolio variance, calculation of 179–81 position accountability 214, 215, 228, 229 preference-free risk-neutral valuation 598, 600 present and future spot prices 20–3 present value (PV): valuation of forward contracts (assets with dividend yield) 94; valuation of forward contracts (assets without dividend yield) 69, 75 price contingent claims with unhedgeable risks 599–601 price paths: ending at specific terminal price, numbers of 442–4; numbers of 440–2 price quotes: in forward markets 9–11; in futures markets 17–19; in spot markets 6–7 pricing a swap 294 pricing by arbitrage and FTAP2 597–8 pricing currency forwards 105 pricing European options under shifted arithmetic Brownian motion (ABM) with no drift 542–51; Bachelier option pricing formula, derivation of 547–51; fundamental theorems of asset pricing (FTAP) 542–3; transition density functions 543–7 pricing foreign exchange forward contracts using no-arbitrage 106–7 pricing mechanism, risk-neutral valuation and 596 pricing options: at expiration (BOPM) 445–6; at time t=0 (BOPM) 446–8; tools for (MBOP) 448–53; relationships between tools 450–3 pricing states 509 pricing zero-coupon, unit discount bonds in continuous time 69–73 primitive Arrow-Debreu (AD) securities, option pricing and 508–14; concept check, pricing ADu(ω) and ADd(ω) 514; exercise 1, pricing B(0,1) 510; exercise 2, pricing ADu(ω) and ADd(ω) 511–14 probability density function 544 profit diagrams 346–7 protection, market orders with 127–9 protective put hedging strategy 427–30; economic interpretation of 429–30; insurance, puts as 427–9 put and call options 323–5, 327, 328, 329, 338; infrastructure for understanding about 337–8 puts as insurance 427–9 quality spreads 299 random variables 536 random walk model of prices 530–1 randomness, state of nature and 23 rate of return of risky asset over small time interval, components of 555–6 rational option pricing (ROP) 369–414; adjusted intrinsic value (AIV) for a European call, definition of 375–6; adjusted time premium (ATP) 397; basic European option pricing model, interpretation of 397–8; certainty equivalent (CE) cash flow 397; concept checks: adjusted intrinsic value (AIV) for calls, calculation of 413; solution to 413; adjusted intrinsic value (AIV) for puts, calculation of 381; solution to 413; directional trades and relative trades, difference between 372; dominance principle and value of European call option 376; solution to 413; exercise price of options, working with 391; forward contracts, overpaying on 403; generalized forward contracts, current value on 404; rational option pricing (ROP) or model-based option pricing (MBOP) 407; short stock position, risk management of 399; solution to 413–14; working from strategies to current costs and back 393; solution to 413; continuation region 385; convexity of option price 406; current costs and related strategies, technique of going back and forth between 393; directional trades 371–2; dominance principle 372, 373; implications of 374–88; equilibrium forward price 402; European Put-Call Parity, financial innovation with 401–5; European Put-Call Parity, implications of 394–400; American option pricing model, analogue for European options 396–8; European call option 394–6; European option pricing model, interpretation of 397–8; European put option 398–9; synthesis of forward contracts from puts and calls 399–400; exercises for learning development of 409–12; financial innovation using European Put-Call Parity 401–5; American Put-Call Parity (no dividends) 403–5; generalized forward contracts 401–3; full replication of European call option (embedded insurance contract) 391–2; generalized forward price 402; key concepts 408–9; LBAC (lower bound for American call option on underlying, no dividends) 374–5; LBACD (lower bound for American call option on underlying, continuous dividends) 383–5; call on underlier with continuous, proportional dividends over life of option 384–5; call on underlier with no dividends over life of option 384; LBAP (lower bound for American put option on underlying, no dividends) 378–80; intrinsic value lower bound for American put, example of 379–80; LBAPD (lower bound for American put option on underlying, continuous dividends) 387–8; LBEC (lower bound for European call option on underlying, no dividends) 375–8; implications of 377–8; LBECD (lower bound for European call option on underlying, continuous dividends) 382–3; LBEP (lower bound for European put option on underlying, no dividends) 380–1; adjusted intrinsic value (AIV) for European put, definition of 380–1; LBEPD (lower bound for European put option on underlying, continuous dividends) 386–7; model-based option pricing (MBOP) 371, 398; model-independent vs. model-based option pricing 370–1; model risk 372; No-Arbitrage in Equilibrium (NAIE) 372, 405–6; partial replication of European call option (embedded forward contract) 388–91; postscript on 405–7; relative pricing trades vs. directional trades 371–2; risk-free arbitrage 373; static replication, principle of 393–4; static replication and European Put-Call Parity (no dividends) 388–94; current costs and related strategies, technique of going back and forth between 393; fully replicating European call option (embedded insurance contract) 391–2; partially replicating European call option (embedded forward contract) 388–91; working backwards from payoffs to costs to derive European Put-Call Parity 393–4; sub-replication 404; super-replication 404; working backwards from payoffs to costs to derive European Put-Call Parity 393–4 raw price change, present value of 243 reading option price quotes 334–7 real asset options 324 realization of daily value 149 realized daily cash flows, creation of 243 receiving floating 293 receiving variable in interest rate derivatives (IRDs) 279–80 recontracting future positions 149, 151 Registered Commodity Representatives (RCRs) 122–3 relative pricing 65–6 relative pricing trades vs. directional trades 371–2 relative risks of hedge portfolio’s return, analysis of 618–24; risk-averse investor in hedge portfolio, role of risk premia for 620–4; risk neutrality in hedge portfolio, initial look at 618–20 replicability: option pricing in continuous time 588; risk-neutral valuation 597–8, 600, 601, 603, 605, 606, 614, 615, 631, 633 replicating portfolio, construction of 478–84; concept check, interpretation of hedge ratio 482; down state, replication in 481; hedge ratio, interpretation of 482–3; replication over period 2 (under scenario 1) 479–82; replication under scenario 2 (over period 2) 484; scenarios 478–9; solving equations for ?


pages: 416 words: 106,532

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske, Jack Tatar

Airbnb, Alan Greenspan, altcoin, Alvin Toffler, asset allocation, asset-backed security, autonomous vehicles, Bear Stearns, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, book value, business cycle, business process, buy and hold, capital controls, carbon tax, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, fixed income, Future Shock, general purpose technology, George Gilder, Google Hangouts, high net worth, hype cycle, information security, initial coin offering, it's over 9,000, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, low interest rates, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, quantum cryptography, RAND corporation, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seminal paper, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, two and twenty, Uber for X, Vanguard fund, Vitalik Buterin, WikiLeaks, Y2K

Now, with Cryptoassets, they describe, as nobody has before, why every investor should incorporate bitcoin, ether, and new blockchain-based assets into their portfolios, and how to analyze these tokens in order to make the right investments. —TRAVIS SCHER, investment associate at Digital Currency Group Chris and Jack have written our generation’s A Random Walk Down Wall Street. This book is required reading for anyone looking to get involved with and profit from the cryptoassets boom. —PATRICK ARCHAMBEAU, VP of engineering at CoinDesk and cofounder of Lawnmower.io Chris and Jack have been fellow travelers in the blockchain space since way before it was a polite cocktail party topic.

The best explanation for this is that cryptoassets are so new that many capital market investors don’t play in the same asset pools. Therefore, cryptoassets aren’t dancing to the same rhythm of information as traditional capital market assets, at least not yet. Figure 7.19 The correlation coefficient and effects of diversification on risk Source: A Random Walk Down Wall Street, Burton G. Malkiel, 2015 Figure 7.19 clearly shows that if an asset is zero correlated to other assets in a portfolio, then “considerable risk reduction is possible.” In quantitative terms, reducing risk can be seen by a decrease in the volatility of the portfolio. If an asset merely reduces the risk of the overall portfolio by being lowly to negatively correlated with other assets, then it doesn’t have to provide superior absolute returns to improve the risk-reward ratio of the overall portfolio.

LOGARITHMIC Two types of scales are commonly used for representing the change in the price of assets: linear and logarithmic. Linear price scales show unadjusted unit changes in the y-axis. For example, if priced in dollars, $10 in value increase will look the same, whether the asset goes from $10 to $20 or $100 to $110. Logarithmic scales adjust the y-axis—in finance most commonly by factors of 10—which allows percent price increases to be compared. For example, on a logarithmic y-axis the price move from $10 to $20 will show up more clearly than the move from $100 to $110, because the former represents a 100 percent price increase while the latter is only a 10 percent price increase.


pages: 339 words: 109,331

The Clash of the Cultures by John C. Bogle

Alan Greenspan, asset allocation, buy and hold, collateralized debt obligation, commoditize, compensation consultant, corporate governance, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, estate planning, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, financial intermediation, fixed income, Flash crash, Glass-Steagall Act, Hyman Minsky, income inequality, index fund, interest rate swap, invention of the wheel, John Bogle, junk bonds, low interest rates, market bubble, market clearing, military-industrial complex, money market fund, mortgage debt, new economy, Occupy movement, passive investing, Paul Samuelson, Paul Volcker talking about ATMs, Ponzi scheme, post-work, principal–agent problem, profit motive, proprietary trading, prudent man rule, random walk, rent-seeking, risk tolerance, risk-adjusted returns, Robert Shiller, seminal paper, shareholder value, short selling, South Sea Bubble, statistical arbitrage, stock buybacks, survivorship bias, The Wealth of Nations by Adam Smith, transaction costs, two and twenty, Vanguard fund, William of Occam, zero-sum game

See Defined benefit (DB) pension plans; Defined contribution (DC) pension plans; Retirement system “People-who-live-in-glass-houses” syndrome PIMCO (Pacific Investment Management Company) Pioneer Fund Politics Portfolio managers, experience and stability of Portfolio turnover: actively managed equity funds exchange traded funds index funds mutual funds Stewardship Quotient and Positive Alpha Press, financial Pricing strategy PRIMECAP Management Company Principals Product, as term Product proliferation, in mutual fund industry Product strategy Profit strategy Proxy statement access by institutional investors, proposed Proxy vote disclosure by mutual funds Prudent Man Rule Public accountants Putnam, Samuel Putnam Management Company Quantitative techniques Random Walk Down Wall Street, A (Malkiel) Rappaport, Alfred Rating agencies Real market Redemptions, shareholder Regulatory issues REIT index fund Retirement accumulation, inadequate Retirement system: about Ambachtsheer, Keith, on asset allocation and investment selection components conflicts of interest costs, excessive current flaws in flexibility, excessive 401(k) retirement plans ideal investor education, lack of longevity risk, failure to deal with mutual funds in New Pension Plan, The pensions, underfunded recommendations retirement accumulation, inadequate savings, inadequacy of “Seven Deadly Sins,” speculation and stock market collapse and value extracted by financial sector Returns: asset allocation and balanced funds defined benefit pension plans projections of equity mutual funds exchange traded funds investment large-cap funds market mutual fund industry speculative Wellington Fund Reversion to the mean (RTM) Riepe, James S.

In very different degrees and in very different ways, speculation plays a major role in the operation of our nation’s retirement plan system, creating challenges and risks that we must work to resolve in the interests of our citizen/investors. I’ll discuss that subject in depth in Chapter 7. 1 A year later, another article, “The Loser’s Game,” by investment professional Charles D. Ellis, published in the Financial Analysts Journal, also gave me encouragement. Even earlier, in the first edition of his remarkable book A Random Walk Down Wall Street (1973), Princeton professor Burton G. Malkiel also issued a challenge for someone to start “a mutual fund that simply buys the hundreds of stocks making up the market averages.” Alas, I didn’t read his book until the early 1980s. 2 My thinking has long been informed by a fifteenth-century maxim known as Occam’s Razor (after English philosopher Sir William of Occam): When there are multiple solutions to a problem, pick the simplest one. 3 That high cost was justified by a Wells Fargo spokesperson because “we (the manager) can make a lot of money.

However, I hold as a general principle that government should, under nearly all circumstances, keep its hands off the free functioning of the marketplace. I wince when the Federal Reserve states its intention to raise asset prices—including “higher stock prices”—apparently irrespective of the level of underlying intrinsic stock values. Substantive limits on short selling are another nonstarter for me. The overriding principle should be: Let the markets clear, at whatever prices that willing and informed buyers agree to pay to willing and informed (but often better-informed) sellers. Individual investors need to wake up. Adam Smith–like, they need to look after their own best interests.


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The Data Detective: Ten Easy Rules to Make Sense of Statistics by Tim Harford

Abraham Wald, access to a mobile phone, Ada Lovelace, affirmative action, algorithmic bias, Automated Insights, banking crisis, basic income, behavioural economics, Black Lives Matter, Black Swan, Bretton Woods, British Empire, business cycle, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, Charles Babbage, clean water, collapse of Lehman Brothers, contact tracing, coronavirus, correlation does not imply causation, COVID-19, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Attenborough, Diane Coyle, disinformation, Donald Trump, Estimating the Reproducibility of Psychological Science, experimental subject, fake news, financial innovation, Florence Nightingale: pie chart, Gini coefficient, Great Leap Forward, Hans Rosling, high-speed rail, income inequality, Isaac Newton, Jeremy Corbyn, job automation, Kickstarter, life extension, meta-analysis, microcredit, Milgram experiment, moral panic, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, opioid epidemic / opioid crisis, Paul Samuelson, Phillips curve, publication bias, publish or perish, random walk, randomized controlled trial, recommendation engine, replication crisis, Richard Feynman, Richard Thaler, rolodex, Ronald Reagan, selection bias, sentiment analysis, Silicon Valley, sorting algorithm, sparse data, statistical model, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, survivorship bias, systematic bias, TED Talk, universal basic income, W. E. B. Du Bois, When a measure becomes a target

Survivor bias even distorts some studies of investment performance. These studies often start by looking at “funds that exist today” without fully acknowledging or adjusting for the fact that any fund still in existence is a survivor—and that introduces a survivorship bias. Burton Malkiel, economist and author of A Random Walk down Wall Street, once tried to estimate how much survivorship bias flattered the performance of the surviving funds. His estimate—an astonishing 1.5 percent per year. That might not sound like much, but over a lifetime of investing it’s a factor of two: you expect retirement savings of (say) $100,000 and end up with $50,000 instead.

See also elections political influence, 13, 33–34, 192–97 political partisanship, 34–35 political polarization, 267–70, 272–73, 277 population data, 94, 202 positivity bias, 247–48 post-hoc theorizing, 117–18 pothole-detection apps, 165 poverty statistics, 59–60, 79, 91, 98, 99–100 Powell-Smith, Anna, 135 practice and memory, 111–12 Pratchett, Terry, 87 “precognition” study, 111–12, 113–14, 116, 121, 123, 128–29 preconceptions, 33, 245–46 prejudice, 167 premature enumeration, 65–85 and gun death data, 72–74, 72n and infant mortality statistics, 56–67 and self-harm data, 73–76 and understanding statistical claims, 67–72 and wealth inequality, 76–84 preregistration of research projects, 127–28 pre-release access to data, 208–11 Presence (Cuddy), 121 priming, 121 PRIS (Puerto Rican Institute of Statistics), 197–200 prisoner data, 55 privacy issues, 181–82 private pensions, 80 Proctor, Robert, 13 Prohibition, 242 propaganda, 12 ProPublica, 176–79 proxy measures, 59 psychology research and choice experiment, 105–7 and conformity studies, 135–38 and political affiliations, 268–69 and “precognition” study, 111–14 and reproducibility crisis, 112–16, 120–22, 128–29, 130–31 public health. See health and medical data public opinion, 149, 220 public transportation, 47–49 publication bias, 113–16, 118–23, 125–27 publicity, 107 Puerto Rico, 197–98, 200 Puy de Dôme, France, 172 Quetelet, Adolphe, 219 racial data, 176–79, 206 Random Walk down Wall Street, A (Malkiel), 125 randomized clinical trials, 4n, 53, 125–26, 133, 180 randomness, 123–24 Rapid Safety Feedback, 170–71 Rayner, Derek, 205–8 Reaper Man (Pratchett), 87 recessions, 11 recommendation engines, 181 record-keeping practices, 220–21 refugees, 191 Reifler, Jason, 129 Reischauer, Robert, 187 Reiter, Jonathan, 108 reliability of data, 233–37 religious authority, 16 religious beliefs, 247–48 Remington Rand, 244 replication/reproducibility studies and problems, 107, 112–16, 120–22, 129–31 Republican Party, 34, 189n, 269, 270 résumé-sorting algorithms, 166 ridership data, 49–51 Riecken, Henry, 239 risk models, 71 Rivlin, Alice, 186–87, 188, 212 Robinson, Nicholas, 168, 169–70 Roman Catholic Church, 16 Rönnlund, Anna Rosling, 62, 63 Roosevelt, Franklin Delano, 143–44 rose diagrams, 215–16, 233–36, 234 Roser, Max, 89, 96 Rosling, Hans, 63, 185 Ross, Lee, 35 Royal Naval Reserve, 218 Royal Society, 13 Royal Statistical Society, 194, 214, 219, 233 Rozenblit, Leonid, 272 Ruge, Mari, 89 sampling techniques, 135–38, 142–51, 155 Samuelson, Paul, 239 sanitation advocacy, 225–26, 233–37 Santos, Alexander, 198 Say It with Charts (Zelazny), 228 scale of statistical data, 92, 93–95, 103 Scarr, Simon, 231–32 Schachter, Stanley, 239 Scheibehenne, Benjamin, 106, 111, 114, 120–21 Scientific American, 102 scientific curiosity, 268–69 scientific literacy, 34–35 scientific method, 173 Scott, James C., 201, 203 Scott Brown, Denise, 217 screen-use studies, 117–18 Scutari (Üsküdar, Istanbul) barracks hospital, 213–14, 220, 225, 233, 235 search algorithms, 156–57 Second World War, 4, 262 secrecy, 174–75 Seehofer, Horst, 191 Seeing Like a State (Scott), 201, 203 selection bias, 2, 245–46.

* * * — In 2011, Guy Mayraz, then a behavioral economist at the University of Oxford, conducted a test of wishful thinking.9 Mayraz showed his experimental subjects a graph of a price rising and falling over time. These graphs were actually historical snippets from the stock market, but Mayraz told people that the graphs showed recent fluctuations in the price of wheat. He asked each person to make a forecast of where the price would move next—and offered them a reward if their forecasts came true. But Mayraz had also divided his experiment participants into two categories. Half of them were told that they were “farmers,” who would be paid extra if wheat prices were high. The rest were “bakers,” who would earn a bonus if wheat was cheap.


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The Ascent of Money: A Financial History of the World by Niall Ferguson

Admiral Zheng, Alan Greenspan, An Inconvenient Truth, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Bear Stearns, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, classic study, collateralized debt obligation, colonial exploitation, commoditize, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, equity risk premium, financial engineering, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, Future Shock, German hyperinflation, Greenspan put, Herman Kahn, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, Modern Monetary Theory, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, National Debt Clock, negative equity, Nelson Mandela, Nick Bostrom, Nick Leeson, Northern Rock, Parag Khanna, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, risk free rate, Robert Shiller, rolling blackouts, Ronald Reagan, Savings and loan crisis, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, stocks for the long run, structural adjustment programs, subprime mortgage crisis, tail risk, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, transaction costs, two and twenty, undersea cable, value at risk, W. E. B. Du Bois, Washington Consensus, Yom Kippur War

. ; USA 241-2 illiquidity 273 investment in 123 law 274-7 and political power 234 price indexes 261-3 price rises and downturns 8 regional variations in prices 233n. unsafe investment bet 229 protectionism 159. see also free trade Prussian government bonds 86 public housing 246-7 publicly owned firms 353 Public Works Administration 246-7 Pückler-Muskau, Prince 90 Putin, Vladimir 276 put options 12 ‘quants’ 321-7 Quantum Fund 319 Quilmes 274 race divisions 243-6 . see also anti-Semitism; ethnic minorities Rachman, Peter 252 railways 226 Rand Corporation 323 random drift 350 randomness 342 ‘random walk’ 320 Ranieri, Lewis 259 rating agencies 268 raw materials see resources RCA 160 Reagan, Ronald 252 and capital account liberalization 312 and S&Ls 254 welfare reforms 219 real estate see property recessions 103-4 prospects of 8 recourse 270n.

One where the inhabitants were omniscient and perfectly rational; where they instantly absorbed all new information and used it to maximize profits; where they never stopped trading; where markets were continuous, frictionless and completely liquid. Financial markets on this planet would follow a ‘random walk’, meaning that each day’s prices would be quite unrelated to the previous day’s but would reflect all the relevant information available. The returns on the planet’s stock market would be normally distributed along the bell curve (see Chapter 3), with most years clustered closely around the mean, and two thirds of them within one standard deviation of the mean.

If not, well, it was only an option, so forget about it. The only cost was the price of the option, which the seller pockets. The big question was what that price should be. ‘Quants’ - the mathematically skilled analysts with the PhDs - sometimes refer to the Black Scholes model of options pricing as a black box. It is worth taking a look inside this particular box. The question, to repeat, is how to price an option to buy a particular stock on a particular date in the future, taking into account the unpredictable movement of the price of the stock in the intervening period. Work out that option price accurately, rather than just relying on guesswork, and you truly deserve the title ‘rocket scientist’.


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

Alan Greenspan, Alvin Roth, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, benefit corporation, Bernie Madoff, buy and hold, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, cognitive dissonance, collateralized debt obligation, collective bargaining, computer age, corporate governance, Daniel Kahneman / Amos Tversky, democratizing finance, Deng Xiaoping, diversification, diversified portfolio, Donald Trump, Edward Glaeser, eurozone crisis, experimental economics, financial engineering, financial innovation, financial thriller, fixed income, full employment, fundamental attribution error, George Akerlof, Great Leap Forward, Ida Tarbell, income inequality, information asymmetry, invisible hand, John Bogle, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, land reform, loss aversion, Louis Bachelier, Mahatma Gandhi, Mark Zuckerberg, market bubble, market design, means of production, microcredit, moral hazard, mortgage debt, Myron Scholes, Nelson Mandela, Occupy movement, passive investing, Ponzi scheme, prediction markets, profit maximization, quantitative easing, random walk, regulatory arbitrage, Richard Thaler, Right to Buy, road to serfdom, Robert Shiller, Ronald Reagan, selection bias, self-driving car, shareholder value, Sharpe ratio, short selling, Simon Kuznets, Skype, social contagion, Steven Pinker, tail risk, telemarketer, Thales and the olive presses, Thales of Miletus, The Market for Lemons, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, Vanguard fund, young professional, zero-sum game, Zipcar

Moreover, statistical research—as in the work of Holbrook Working in 1934 and Maurice Kendall in 1953—had already found evidence, years before Fama, that short-run changes in prices in speculative markets are hard to forecast.5 But Fama raised this theory to the status of a broad new scienti c paradigm and likened to astrologers the old-fashioned analysts who looked to patterns in stock market data for trading opportunities. Fama believed that market prices were too perfect to be predictable, to show any pattern other than a random walk. Fama used a data set that had recently been compiled by the Center for Research in Security Prices (CRSP) at the University of Chicago, founded in 1960 with a $50,000 grant from Merrill Lynch. The center’s initial purpose was to put a huge set of monthly (later daily) stock prices, and associated information about capital changes and dividends that would allow accurate computation of returns, on magnetic tape so that the data could be analyzed with a univac computer.

., and Robert J. Shiller. 1990. “Comparing Information in Forecasts from Econometric Models.” American Economic Review 80(3):375–89. Falke, Armin. 2004. “Charitable Giving as a Gift Exchange: Evidence from a Field Experiment.” IZA Discussion Paper 1148. University of Bonn. Fama, Eugene F. 1965. “Random Walks in Stock Market Prices.” Financial Analysts Journal 21(5):55–59. Fama, Eugene F., and Kenneth French. 2005. “Financing Decisions: Who Issues Stock?” Journal of Financial Economics 76(3):549–74. Fehr, Ernst. 2009. “Social Preferences and the Brain.” In Paul Glimcher, Colin Camerer, Ernst Fehr, and Russell Poldrack, eds., Neuroeconomics: Decision Making and the Brain, 215–30.

For example, there was, until recently, no derivatives market for residential real estate prices, for single-family home prices. There are markets for commercial real estate prices, but not for the much bigger category of real estate represented by our homes. The subprime crisis, which triggered the severe nancial crisis that began in 2007, was caused by a bursting bubble in U.S. home prices. Perhaps this crisis could have been averted if there had been a market that revealed public opinions about future home prices. My colleagues and I worked with the Chicago Mercantile Exchange (CME) to launch a futures market in single-family home prices.15 Futures market prices of single-family homes for ten U.S. cities—Boston, Chicago, Denver, Las Vegas, Los Angeles, Miami, New York, San Diego, San Francisco, and Washington, D.C.


pages: 519 words: 118,095

Your Money: The Missing Manual by J.D. Roth

Airbnb, Alan Greenspan, asset allocation, bank run, book value, buy and hold, buy low sell high, car-free, Community Supported Agriculture, delayed gratification, diversification, diversified portfolio, do what you love, estate planning, Firefox, fixed income, full employment, hedonic treadmill, Home mortgage interest deduction, index card, index fund, John Bogle, late fees, lifestyle creep, low interest rates, mortgage tax deduction, Own Your Own Home, Paradox of Choice, passive investing, Paul Graham, random walk, retail therapy, Richard Bolles, risk tolerance, Robert Shiller, speech recognition, stocks for the long run, traveling salesman, Vanguard fund, web application, Zipcar

The same is generally true of the returns on these asset classes—they're normally independent of each other. (But sometimes, as in the recent financial crisis, there's a whole lot of correlation going on!) Over time. "Risk is also reduced for investors who build up a retirement nest egg by putting their money in the market regularly over time," writes Burton Malkiel in The Random Walk Guide to Investing. By using techniques like dollar-cost averaging (see All-in-one funds), you ensure that you're not investing all your money when the market is high. There are other types of diversification, too. For example, when you buy foreign stocks, you're diversifying by geography.

According to the 2009 National Retirement Risk Index from the Center for Retirement Research, 51% of Americans are "at risk of being unable to maintain their pre-retirement standard of living in retirement" (http://tinyurl.com/CRR-nrri). Part of the reason is that these folks didn't plan ahead and set aside enough when they were young. "The amount of capital you start with is not nearly as important as getting started early," writes Burton Malkiel in The Random Walk Guide to Investing. "Procrastination is the natural assassin of opportunity. Every year you put off investing makes your ultimate retirement goals more difficult to achieve." An article about retirement in the February 2010 issue of Consumer Reports featured a survey of more than 24,000 of the magazine's readers.

This will help you avoid buying on impulse—which is how shopping trips get out of control. Compare unit pricing. An item's unit price tells you the cost for each unit of measurement. For example, the unit price of a box of cereal tells you how much you're paying for each ounce. If you're lucky, your grocery store already posts unit pricing for most items, which makes comparing them easy. If not, carry a calculator. Note The biggest package isn't always the most cost-effective. Stores know that people want to buy in bulk, so sometimes they actually make the larger package's unit price higher than the smaller package's. Choose a store and learn its prices. Because supermarkets monkey with prices, you can't be sure a deal is really a deal unless you know what the store usually charges.


pages: 823 words: 220,581

Debunking Economics - Revised, Expanded and Integrated Edition: The Naked Emperor Dethroned? by Steve Keen

accounting loophole / creative accounting, Alan Greenspan, banking crisis, banks create money, barriers to entry, behavioural economics, Benoit Mandelbrot, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, book value, business cycle, butterfly effect, capital asset pricing model, cellular automata, central bank independence, citizen journalism, clockwork universe, collective bargaining, complexity theory, correlation coefficient, creative destruction, credit crunch, David Ricardo: comparative advantage, debt deflation, diversification, double entry bookkeeping, en.wikipedia.org, equity risk premium, Eugene Fama: efficient market hypothesis, experimental subject, Financial Instability Hypothesis, fixed income, Fractional reserve banking, full employment, Glass-Steagall Act, Greenspan put, Henri Poincaré, housing crisis, Hyman Minsky, income inequality, information asymmetry, invisible hand, iterative process, John von Neumann, Kickstarter, laissez-faire capitalism, liquidity trap, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, market clearing, market microstructure, means of production, minimum wage unemployment, Money creation, money market fund, open economy, Pareto efficiency, Paul Samuelson, Phillips curve, place-making, Ponzi scheme, Post-Keynesian economics, power law, profit maximization, quantitative easing, RAND corporation, random walk, risk free rate, risk tolerance, risk/return, Robert Shiller, Robert Solow, Ronald Coase, Savings and loan crisis, Schrödinger's Cat, scientific mainstream, seigniorage, six sigma, South Sea Bubble, stochastic process, The Great Moderation, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, total factor productivity, tulip mania, wage slave, zero-sum game

In the case of the stock market, it means at least four things: that the collective expectations of stock market investors are accurate predictions of the future prospects of companies; that share prices fully reflect all information pertinent to the future prospects of traded companies; that changes in share prices are entirely due to changes in information relevant to future prospects, where that information arrives in an unpredictable and random fashion; and that therefore stock prices ‘follow a random walk,’ so that past movements in prices give no information about what future movements will be – just as past rolls of dice can’t be used to predict what the next roll will be.

If this set of theories were correct, then the propositions cited earlier would be true: the collective expectations of investors will be an accurate prediction of the future prospects of companies; share prices will fully reflect all information pertinent to the future prospects of traded companies.14 Changes in share prices will be entirely due to changes in information relevant to future prospects; and prices will ‘follow a random walk,’ so that past movements in prices give no information about what future movements will be. Reservations The outline above covers the theory as it is usually presented to undergraduates (and victims of MBA programs), and as it was believed by its adherents among stockbrokers and speculators (of whom there are now almost none).

The Fractal Markets Hypothesis The Fractal Markets Hypothesis is primarily a statistical interpretation of stock market prices, rather than a model of how the stock market, or investors in it, actually behave. Its main point is that stock market prices do not follow the random walk predicted by the EMH,5 but conform to a much more complex pattern called a fractal. As a result, the statistical tools used by the EMH, which were designed to model random processes, will give systematically misleading predictions about stock market prices. The archetypal set of random numbers is known as the ‘normal’ distribution, and its mathematical properties are very well known.


Mathematical Finance: Theory, Modeling, Implementation by Christian Fries

Black-Scholes formula, Brownian motion, continuous integration, discrete time, financial engineering, fixed income, implied volatility, interest rate derivative, martingale, quantitative trading / quantitative finance, random walk, short selling, Steve Jobs, stochastic process, stochastic volatility, volatility smile, Wiener process, zero-coupon bond

q y Definition 27 (Canonical Setup): The space (C([0, ∞)), B(C([0, ∞)), P∗ ) q (as defined in Theorem 25) is called the canonical setup for a Brownian motion W defined by W(t, ω) := ω(t), ω ∈ C([0, ∞)). y Remark 28: A more detailed discussion of Theorem 25 may be found in [18]. A less formal discussion of properties of the Brownian motion may be found in [12]. 2.6. Itô Calculus Motivation: The Brownian motion W is our first encounter with an important continuous stochastic process. The Brownian motion may be viewed as the limit of a scaled random walk.11 If we interpret the Brownian motion W in this sense as a model for the movement of a particle, then W(T ) denotes the position of the particle at time T and W(T + ∆T ) − W(T ) the position change that occurs from T to T + ∆T ; to be precise W(T ) models the probability distribution of the particle position.

The random variable W(T i ) (position of the particle) may be expressed through the increments ∆W(T i ) := W(T i+1 ) − W(T i ): W(T i ) = i−1 X ∆W(T j ). j=0 Using the increments ∆W(T j ) we may define a whole family of discrete stochastic processes. We give an step by step introduction and use the illustrative interpretation of a particle movement: First we assume that the particle may lose energy over time 11 In a (one dimensional) random walk a particle changes position at discrete time steps by a (constant) distance (say 1) in either direction with equal probability. In other words we have binomial distributed Yi in Theorem 25. 40 This work is licensed under a Creative Commons License. http://creativecommons.org/licenses/by-nc-nd/2.5/deed.en Comments welcome. ©2004, 2005, 2006 Christian Fries Version 1.3.19 [build 20061210]- 13th December 2006 http://www.christian-fries.de/finmath/ 2.6.

Linear Interpolation of Prices In Figure 6.2 we show the linear interpolation of the given option prices. The prices do not allow arbitrage, which is obvious from the convexity of their linear interpolation. Interpolated prices Interpolated volatlities Probability density 1,2E-1 0,50 0,30 0,20 0,60 density volatility price 0,40 0,50 0,40 1,0E-1 7,5E-2 5,0E-2 2,5E-2 0,10 0,0E0 0,50 1,00 1,50 2,00 0,50 1,00 1,50 2,00 0,50 1,00 1,50 2,00 strike strike underlying value Figure 6.2.: Linear interpolation of option prices. However, the linear interpolation of prices has severe disadvantages: The linear interpolation of option prices implies a model under which the underlying may not attain values K , Ki , the corresponding probability density is zero for these values.


pages: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 3Com Palm IPO, Alan Greenspan, Albert Einstein, asset allocation, Bear Stearns, beat the dealer, Bernie Madoff, Black Monday: stock market crash in 1987, Black Swan, Black-Scholes formula, book value, Brownian motion, buy and hold, buy low sell high, caloric restriction, caloric restriction, carried interest, Chuck Templeton: OpenTable:, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial engineering, financial innovation, Garrett Hardin, George Santayana, German hyperinflation, Glass-Steagall Act, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Bogle, John Meriwether, John Nash: game theory, junk bonds, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, Mason jar, merger arbitrage, Michael Milken, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, PalmPilot, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, power law, price anchoring, publish or perish, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, risk-adjusted returns, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stock buybacks, stocks for the long run, survivorship bias, tail risk, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Tragedy of the Commons, uptick rule, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

or 82 percent I have omitted details such as how the actual cash required for the investment might vary from the $9,000 of the example because of the interaction of margin regulations with the investor’s preexisting portfolio, and also because of time-varying marks to the market on the short position. The Wall Street Journal Wall Street Journal, March 3, 2000, page C19, “Palm Soars As 3Com Unit Makes Its Trading Debut.” the EMH explained Malkiel, Burton G., A Random Walk Down Wall Street, Norton & Co., New York, 2007. The New York Times New York Times, March 3, 2000, page A1, “Offspring Upstages Parent In Palm Inc.’s Initial Trading.” academic literature documents It often takes weeks or months for the stock price to fully adjust after announcements of unexpected earnings, stock buybacks, and spin-offs. CHAPTER 27 already been counted Mutual fund management companies and hedge fund general partnership interests have a separate and often considerable market value but they have already been counted as part of the private equity subcategory.

Bell System Technical Journal 35.4 (1956): 917–26. Lack, Simon. The Hedge Fund Mirage: The Illusion of Big Money and Why It’s Too Good to Be True. Hoboken, NJ: Wiley, 2012. MacLean, L. C., Edward O. Thorp, and W. T. Ziemba. The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific, 2011. Malkiel, Burton Gordon. A Random Walk Down Wall Street: The Time-tested Strategy for Successful Investing. New York: W. W. Norton, 2007. Mezrich, Ben. Bringing Down the House: The Inside Story of Six MIT Students Who Took Vegas for Millions. New York: Free Press, 2002. Munchkin, Richard W. Gambling Wizards: Conversations with the World’s Greatest Gamblers.

A warrant, like a lottery ticket, was always worth something before it expired even if the stock price was very low, if there was any chance the stock price could move above the exercise price and put the warrant “into the money.” The more time left, and the higher the stock price, the more the warrant was likely to be worth. The prices of these two securities followed a simple relationship regardless of the complexities of the balance sheet or business affairs of the underlying company. As I thought about this I formed a rough idea of the rules relating the warrant price to the stock price. Since the prices of the two securities tended to move together, the important idea of “hedging” occurred to me, in which I could use this relationship to exploit any mispricing of the warrant and simultaneously reduce the risk of doing so.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, AOL-Time Warner, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bear Stearns, Bernie Madoff, Black Swan, bond market vigilante , book value, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, commodity super cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, equity risk premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, global macro, Greenspan put, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, inverted yield curve, invisible hand, junk bonds, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, low interest rates, market bubble, market fundamentalism, market microstructure, Minsky moment, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, proprietary trading, purchasing power parity, quantitative easing, random walk, Reminiscences of a Stock Operator, reserve currency, risk free rate, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, SoftBank, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, tail risk, The Great Moderation, Thomas Bayes, time value of money, too big to fail, Tragedy of the Commons, transaction costs, two and twenty, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

If we think of valuations as departures from long-term fair value, then many approaches have been shown to allow better forecasting of returns in equities—at least better than a random walk. For judging valuation levels, I would start with price-to-earnings multiples, price-to-book multiples, or whatever kind of fundamental earnings model you want to use. Tobin’s Q ratio, as demonstrated by Andrew Smithers’ work, has correctly identified over- and undervalued episodes in the U.S. equity market, and the use of cyclically adjusted price to earnings ratios has worked in a similar manner. Everybody on the Street generates these kinds of numbers, and you can take an average of them or develop your own metric.

See Risk premia payment Price/earnings (P/E) multiples, exchange rate valuation (relationship) Primary Dealer Credit Facility, placement Prime broker risk Princeton University (endowment) Private equity cash flow production tax shield/operational efficiency arguments Private sector debt, presence Private-to-public sector risk Probability, Bayesian interpretation Professor, The bubble predication capital loss, avoidance capital management cataclysms, analysis crowding factor process diversification efficient markets, disbelief fiat money, cessation global macro fund manager hedge fund space historical events, examination idea generation inflation/deflation debate interview investment process lessons LIBOR futures ownership liquidity conditions, change importance market entry money management, quality opportunities personal background, importance portfolio construction management positioning process real macro success, personality traits/characteristics (usage) returns, generation risk aversion rules risk management process setback stocks, purchase stop losses time horizon Titanic scenario threshold trades attractiveness, measurement process expression, options (usage) personal capital, usage quality unlevered portfolio Property/asset boom Prop shop trading, preference Prop trader, hedge fund manager (contrast) Protectionism danger hedge process Public college football coach salary, public pension manager salary (contrast) Public debt, problems Public pensions average wages to returns endowments impact Q ratio (Tobin) Qualitative screening, importance Quantitative easing (QE) impact usage Quantitative filtering Random walk, investment Real annual return Real assets Commodity Hedger perspective equity-like exposure Real estate, spread trade Real interest rates, increase (1931) Real macro involvement success, personality traits/characteristics (usage) Real money beta-plus domination denotation evolution flaws hedge funds, differentiation impacts, protection importance investors commodity exposure diversification, impact macro principles management, change weaknesses Real money accounts importance long-only investment focus losses (2008) Real money funds Commodity Hedger operation Equity Trader management flexibility frontier, efficiency illiquid asset avoidance importance leverage example usage management managerial reserve optimal portfolio construction failure portfolio management problems size Real money managers Commodity Investor scenario liquidity, importance long-term investor misguidance poor performance, usage (excuse) portfolio construction valuation approach, usage Real money portfolios downside volatility, mitigation leverage, amount management flaws Rear view mirror investment process Redemptions absence problems Reflexivity Rehypothecation Reichsmarks, foreign holders (1922-1923) Relative performance, inadequacy Reminiscences of a Stock Operator (Lefèvre) Renminbi (2005-2009) Repossession property levels Republic of Turkey examination investment rates+equities (1999-2000) Reserve currency, question Resource nationalism Returns forecast generation maximization momentum models targets, replacement Return-to-worst-drawdown, ratios (improvement) Reward-to-variability ratio Riksbank (Sweden) Risk amount, decision aversion rules capital, reduction collars function positive convexity framework, transition function global macro manager approach increase, leverage (usage) measurement techniques, importance parameters Pensioner management pricing reduction system, necessity Risk-adjusted return targets, usage Risk assets, decrease Risk-free arbitrage opportunities Risk management Commodity Hedger process example game importance learning lessons portfolio level process P&L, impact tactic techniques, importance Risk premia annualization earning level, decrease specification Risk/reward trades Risk-versus-return, Pensioner approach Risk-versus-reward characteristics opportunities Roll yield R-squared (correlation) Russia crisis Russia Index (RTSI$) (1995-2002) Russia problems Savings ratio, increase Scholes, Myron Sector risk, limits Securities, legal lists Self-reinforcing cycles (Soros) Sentiment prediction swings Seven Sisters Sharpe ratio increase return/risk Short-dated assets Short selling, ban Siegel’s Paradox example Single point volatility 60-40 equity-bond policy portfolio 60-40 model 60-40 portfolio standardization Smither, Andrew Socialism, Equity Trader concern Society, functioning public funds, impact real money funds, impact Softbank (2006) Soros, George self-reinforcing cycles success Sovereign wealth fund Equity Trader operation operation Soybeans (1970-2009) Special drawing rights (SDR) Spot price, forward price (contrast) Spot shortages/outages, impact Standard deviation (volatility) Standard & Poor’s 500 (S&P500) (2009) decrease Index (1986-1995) Index (2000-2009) Index (2008) shorting U.S. government bonds, performance (contrast) Standard & Poor’s (S&P) shorts, coverage Stanford University (endowment) State pension fund Equity Trader operation operation Stochastic volatility Stock index total returns (1974-2009) Stock market increase, Predator nervousness Stocks hedge funds, contrast holders, understanding pickers, equity index futures usage shorting/ownership, contrast Stops, setting Stress tests, conducting Subprime Index (2007-2009) Sunnies, bidding Super Major Survivorship bias Sweden AP pension funds government bond market Swensen, David equity-centric portfolio Swiss National Bank (SNB) independence Systemic banking crisis Tactical asset allocation function models, usage Tactical expertise Tail hedging, impact Tail risk Take-private LBO Taleb, Nassim Tax cut sunset provisions Taxes, hedge Ten-year U.S. government bonds (2008-2009) Theta, limits Thundering Herd (Merrill Lynch) Time horizons decrease defining determination shortening Titanic funnel, usage Titanic loss number Titanic scenario threshold Topix Index (1969-2000) Top-line inflation Total credit market, GDP percentage Total dependency ratio Trade ideas experience/awareness, impact generation process importance origination Traders ability Bond Trader hiring characteristics success, personality characteristics Trades attractiveness, measurement process hurdle money makers, percentage one-year time horizon selection, Commodity Super Cycle (impact) time horizon, defining Trading decisions, policy makers (impact) floor knowledge noise level ideas, origination Tragedy of the commons Transparency International, Corruption Perceptions Index Treasury Inflation-Protected Securities (TIPS) trade Triangulated conviction Troubled Asset Relief Program (TARP) Turkey economy inflation/equities (1990-2009) investment rates+equities (1999-2000) stock market index (ISE 100) Unconventional Success (Swensen) Underperformance, impact Undervaluation zones, examination United Kingdom (UK), two-year UK swap rates (2008) United States bonds pricing debt (1991-2008) debt (2000-2008) home prices (2000-2009) hyperinflation listed equities, asset investment long bonds, market pricing savings, increase stocks tax policy (1922-1936) trade deficit, narrowing yield curves (2004-2006) University endowments losses impact unlevered portfolio U.S.

Roll Yield, Backwardation and Contango Roll Yield—The amount of return generated in a backwardated futures market that is achieved by rolling a futures contract into the higher-priced spot market. As time passes and the futures contract appreciates, traders will take profits in the near-dated positions and purchase less-expensive futures contracts. Backwardation allows the trader to consistently profit from the rise in a futures’ price as it nears expiration or the spot price. The biggest risk to this strategy is that the market will shift, resulting in a futures price above the spot price, a condition is known as contango. Backwardation—The market condition in which the spot price is above the futures price. This is also known an inverted sloping forward curve.


pages: 396 words: 117,149

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

The technical term for this is Markov chain Monte Carlo, or MCMC for short. The “Monte Carlo” part is because the method involves chance, like a visit to the eponymous casino, and the “Markov chain” part is because it involves taking a sequence of steps, each of which depends only on the previous one. The idea in MCMC is to do a random walk, like the proverbial drunkard, jumping from state to state of the network in such a way that, in the long run, the number of times each state is visited is proportional to its probability. We can then estimate the probability of a burglary, say, as the fraction of times we visited a state where there was a burglary.

(Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 2007), explains how Google Translate works. “The PageRank citation ranking: Bringing order to the Web,”* by Larry Page, Sergey Brin, Rajeev Motwani, and Terry Winograd (Stanford University technical report, 1998), describes the PageRank algorithm and its interpretation as a random walk over the web. Statistical Language Learning,* by Eugene Charniak (MIT Press, 1996), explains how hidden Markov models work. Statistical Methods for Speech Recognition,* by Fred Jelinek (MIT Press, 1997), describes their application to speech recognition. The story of HMM-style inference in communication is told in “The Viterbi algorithm: A personal history,” by David Forney (unpublished; online at arxiv.org/pdf/cs/0504020v2.pdf).

It’s nice to not have to do anything, given that Facebook users upload upward of three hundred million photos per day. Applying any of the learners we’ve seen so far to them, with the possible exception of Naïve Bayes, would take a truckload of computers. And Naïve Bayes is not smart enough to recognize faces. Of course, there’s a price to pay, and the price comes at test time. Jane User has just uploaded a new picture. Is it a face? Nearest-neighbor’s answer is: find the picture most similar to it in Facebook’s entire database of labeled photos—its “nearest neighbor”—and if that picture contains a face, so does this one. Simple enough, but now you have to scan through potentially billions of photos in (ideally) a fraction of a second.


pages: 398 words: 31,161

Gnuplot in Action: Understanding Data With Graphs by Philipp Janert

bioinformatics, business intelligence, Debian, general-purpose programming language, iterative process, mandelbrot fractal, pattern recognition, power law, random walk, Richard Stallman, six sigma, sparse data, survivorship bias

All these details can be customized, but gnuplot typically does a good job at anticipating what the user wants. 1.1.2 Determining the future The same weekend when 2,000 runners are running through the city, a diligent graduate student is working on his research topic. He studies diffusion limited aggregation (DLA), a process wherein a particle performs a random walk until it comes in contact with a growing cluster of particles. At the moment of contact, the particle sticks to the cluster at the location where the contact occurred and becomes part of the cluster. Now, a new random walker is released to perform a random walk, until it sticks to the cluster. And so on. Clusters grown through this process have a remarkably open, tenuous structure (as in figure 1.2). DLA clusters are fractals, but rather little is known about them with certainty.2 The DLA process is very simple, so it seems straightforward to write a computer program to grow such clusters in a computer, and this is what our busy graduate student has done.

Assuming that the file shown in listing 2.1 is called prices, we can simply type plot "prices" Since data files typically contain many different data sets, we’ll usually want to select the columns to be used as x and y values. This is done through the using directive to the plot command: plot "prices" using 1:2 This will plot the price of PQR shares as a function of time: the first argument to the using directive specifies the column in the input file to be plotted along the horizontal (x) axis, while the second argument specifies the column for the vertical (y) axis. If we want to plot the price of XYZ shares in the same plot, we can do so easily (as in figure 2.4): plot "prices" using 1:2, "prices" using 1:3 By default, data points from a file are plotted using unconnected symbols.

If we want to plot the price of XYZ shares in the same plot, we can do so easily (as in figure 2.4): plot "prices" using 1:2, "prices" using 1:3 By default, data points from a file are plotted using unconnected symbols. Often this isn’t what we want, so we need to tell gnuplot what style to use for the data. This is done using the with directive. Many different styles are available. Among the most useful ones are with linespoints, which plots each data point as a symbol and also connects subsequent points, and with lines, which just plots the connecting lines, omitting the individual symbols. plot "prices" using 1:2 with lines, ➥ "prices" using 1:3 with linespoints 180 "prices" u 1:2 "prices" u 1:3 160 140 120 100 80 60 40 1975 Figure 2.4 1980 1985 1990 1995 Plotting from a file: plot "prices" using 1:2, "prices" using 1:3 22 CHAPTER 2 Essential gnuplot This looks good, but it’s not clear from the graph which line is which.


pages: 764 words: 261,694

The Elements of Statistical Learning (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani, Jerome Friedman

algorithmic bias, backpropagation, Bayesian statistics, bioinformatics, computer age, conceptual framework, correlation coefficient, data science, G4S, Geoffrey Hinton, greed is good, higher-order functions, linear programming, p-value, pattern recognition, random walk, selection bias, sparse data, speech recognition, statistical model, stochastic process, The Wisdom of Crowds

In practice one has strong and weak connections, so zero eigenvalues are approximated by small eigenvalues. Spectral clustering is an interesting approach for finding non-convex clusters. When a normalized graph Laplacian is used, there is another way to view this method. Defining P = G−1 W, we consider a random walk on the graph with transition probability matrix P. Then spectral clustering yields groups of nodes such that the random walk seldom transitions from one group to another. There are a number of issues that one must deal with in applying spectral clustering in practice. We must choose the type of similarity graph—eg. fully connected or nearest neighbors, and associated parameters such as the number of nearest of neighbors k or the scale parameter of the kernel c.

The particular MCMC approach that was used is called hybrid Monte Carlo, and may be important for the success of the method. It includes an auxiliary momentum vector and implements Hamiltonian dynamics in which the potential function is the target density. This is done to avoid 11.9 Bayesian Neural Nets and the NIPS 2003 Challenge 411 random walk behavior; the successive candidates move across the sample space in larger steps. They tend to be less correlated and hence converge to the target distribution more rapidly. Neal and Zhang (2006) also tried different forms of pre-processing of the features: 1. univariate screening using t-tests, and 2. automatic relevance determination.

This means that we can find p̂ by the power method: starting with some p = p0 we iterate pk ← Apk−1 ; pk ← N pk T e p . (14.110) k The fixed points p̂ are the desired PageRanks. In the original paper of Page et al. (1998), the authors considered PageRank as a model of user behavior, where a random web surfer clicks on links at random, without regard to content. The surfer does a random walk on the web, choosing among available outgoing links at random. The factor 1 − d is the probability that he does not click on a link, but jumps instead to a random webpage. Some descriptions of PageRank have (1 − d)/N as the first term in definition (14.107), which would better coincide with the random surfer interpretation.


pages: 387 words: 119,409

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock

Abraham Maslow, Abraham Wald, Airbnb, Albert Einstein, AltaVista, Atul Gawande, behavioural economics, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, cognitive load, company town, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice, en.wikipedia.org, experimental subject, Fairchild Semiconductor, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, Kevin Roose, longitudinal study, Menlo Park, mental accounting, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, power law, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, Sheryl Sandberg, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, Susan Wojcicki, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, Wayback Machine, winner-take-all economy, Y2K

For Annabelle, Emily, and Lila may you always love what you do Where’s the work that’ll set my hands, my soul free —“WE TAKE CARE OF OUR OWN,” BRUCE SPRINGSTEEN Preface: A Guidance Counselor’s Nightmare Building the perfect Google resume, in retrospect My first paycheck came in the summer of 1987, when I was fourteen years old. My best friend, Jason Corley, and I had been invited by our high school to enroll in a summer-school debate class the year before ninth grade. By the next year, we were teaching it. We earned $420 each. Over the next twenty-eight years, I amassed a random walk resume that could best be described as a guidance counselor’s nightmare: I worked in a deli, a restaurant, and a library. I tutored high school students in California and taught elementary school students English in Japan. I was first a lifeguard in real life at my college pool, and then I played one on TV, appearing on Baywatch as a 1960s lifeguard in a flashback and as that old acting standby, “Man walking across background.”

It’s why in politics you can never win points by arguing “But the recession would have been so much worse if not for my policies!” But you’ll know, and your team will know. And your company will run better. And people will be happier. Once you’ve scaled this pyramid, which isn’t that different from Maslow’s, you achieve HR nirvana. For employees, it means that you enjoy what feels like a random walk through Google: You have a terrific set of interviews with compelling people, you join and feel welcome, becoming productive in a few weeks because you’ve met helpful people, and you’re constantly surprised as opportunities unfold ahead of you. It’s like one of those choose-your-own-adventure books that some of you may have read as kids, where every page opens more and more options.

Newton once said, “If I have seen far, it is because Jeff Dean will stand on my shoulders.” As special as Jeff is to us, he’s not alone. Salar Kamangar had the insight on how to create auctions for search terms, and worked closely with engineer Eric Veach to build our first ad systems. In publishing, for example, magazines will list a price they charge advertisers for every thousand readers. Instead of naming a price up front, Salar dreamed up running an auction for every word or phrase a user might search for. Google doesn’t arbitrarily decide in what order ads are presented. Rather, our advertisers bid for the position they want in the list of ads, which can cost from less than a penny to more than $10 per word.


pages: 356 words: 51,419

The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns by John C. Bogle

asset allocation, backtesting, buy and hold, creative destruction, currency risk, diversification, diversified portfolio, financial intermediation, fixed income, index fund, invention of the wheel, Isaac Newton, John Bogle, junk bonds, low interest rates, new economy, passive investing, Paul Samuelson, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, stocks for the long run, survivorship bias, transaction costs, Upton Sinclair, Vanguard fund, William of Occam, yield management, zero-sum game

That means avoiding the most hyped but expensive funds, in favor of low-cost index funds. And finally, invest for the long term. [Investors] should simply have index funds to keep their fees low and their taxes down. No doubt about it.” * * * In terms that are a bit less contentious, Princeton University professor Burton G. Malkiel, author of A Random Walk Down Wall Street, expresses these views: “Index funds have regularly produced [annual] rates of return exceeding those of active managers by close to 2 percentage points. Active management as a whole cannot achieve gross returns exceeding the market as a whole, and therefore they must, on average, underperform the indexes by the amount of these expense and transaction costs.

EXHIBIT 13.2 Costs of Selected S&P 500 Index Funds Five Low-Cost 500 Index Funds Annual Expense Ratio Sales Load Vanguard 500 Index Admiral 0.04% 0.0% Fidelity 500 Index Premium 0.045 0.0 Schwab S&P 500 Index 0.09 0.0 Northern Stock Index 0.10 0.0 T. Rowe Price Equity Index 500 0.25 0.0 Five High-Cost Funds Invesco S&P 500 Index 0.59% 1.10% State Farm S&P 500 Index 0.66 1.00 Wells Fargo Index 0.45 1.15 State Street Equity 500 Index 0.51 1.05 JPMorgan Equity Index 0.45 4.80 Even among the low-cost S&P 500 Index funds, we see a wide range of expenses. While the Admiral class of Vanguard’s index fund carries a minuscule 0.04 percent expense ratio, the T. Rowe Price fund charges 0.25 percent. Although lower than the high-cost index funds, that T. Rowe Price fund is hardly “low.” Assuming an annual return of 6 percent compounded over 25 years, an initial investment of $10,000 would grow to $40,458 in the T.

EXHIBIT 2.1 Investment Return versus Market Return. Growth of $1, 1900–2016 That difference of 0.5 percentage points per year arose from what I call speculative return. Speculative return may be a plus or a minus, depending on the willingness of investors to pay either higher or lower prices for each dollar of earnings at the end of a given period than at the beginning. The price/earnings (P/E) ratio measures the number of dollars investors are willing to pay for each dollar of earnings. As investor confidence waxes and wanes, P/E multiples rise and fall.1 When greed holds sway, very high P/Es are likely. When hope prevails, P/Es are moderate.


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MONEY Master the Game: 7 Simple Steps to Financial Freedom by Tony Robbins

"World Economic Forum" Davos, 3D printing, active measures, activist fund / activist shareholder / activist investor, addicted to oil, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, backtesting, Bear Stearns, behavioural economics, bitcoin, Black Monday: stock market crash in 1987, buy and hold, Carl Icahn, clean water, cloud computing, corporate governance, corporate raider, correlation does not imply causation, Credit Default Swap, currency risk, Dean Kamen, declining real wages, diversification, diversified portfolio, Donald Trump, estate planning, fear of failure, fiat currency, financial independence, fixed income, forensic accounting, high net worth, index fund, Internet of things, invention of the wheel, it is difficult to get a man to understand something, when his salary depends on his not understanding it, Jeff Bezos, John Bogle, junk bonds, Kenneth Rogoff, lake wobegon effect, Lao Tzu, London Interbank Offered Rate, low interest rates, Marc Benioff, market bubble, Michael Milken, money market fund, mortgage debt, Neil Armstrong, new economy, obamacare, offshore financial centre, oil shock, optical character recognition, Own Your Own Home, passive investing, profit motive, Ralph Waldo Emerson, random walk, Ray Kurzweil, Richard Thaler, risk free rate, risk tolerance, riskless arbitrage, Robert Shiller, Salesforce, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Skype, Snapchat, sovereign wealth fund, stem cell, Steve Jobs, subscription business, survivorship bias, tail risk, TED Talk, telerobotics, The 4% rule, The future is already here, the rule of 72, thinkpad, tontine, transaction costs, Upton Sinclair, Vanguard fund, World Values Survey, X Prize, Yogi Berra, young professional, zero-sum game

We think that if we work harder, smarter, longer, we’ll achieve our financial dreams, but our paycheck alone—no matter how big—isn’t the answer. I was reminded of this fundamental truth on a recent visit with the noted economist Burton Malkiel, author of one of the classic books on finance, A Random Walk Down Wall Street. I went to see Malkiel in his office at Princeton University because I admired not just his track record but also his no-nonsense style. In his books and interviews, he comes across as a straight shooter—and the day I met him was no exception. I wanted to get his insights on some of the pitfalls facing people at all stages of their investment lives.

And when the market is up, we buy more. As a famous money manager named Barton Biggs observed, “A bull market is like sex. It feels best just before it ends.” WISDOM OF AGES At 82 years young, Burt Malkiel has lived through every conceivable market cycle and new marketing fad. When he wrote A Random Walk Down Wall Street in 1973, he had no idea it would become one of the classic investment books in history. The core thesis of his book is that market timing is a loser’s game. In section 4, we will sit down and you’ll hear from Burt but for now what you need to know is that he was the first guy to come up with the rationale of an index fund, which, again, does not to try to beat the market but simply “mimics,” or matches, the market.

Boone, 47, 456, 505–13, 505, 596 Pitbull, 15 Pitt, Brad, 65 pivot, 265 plan: building, 206, 230–46, 247 choosing your adventure, 234–37 financial blueprint, 233–34 illusion of advantage, 232–33 late to the party, 242–44 playing your own hand, 231–32 proven, 187 rates of return for, 235 savings as foundation of, 56–57 speeding up, see speed it up timeline for, 292 too good to be true, 240–42 Platinum Partners, 62, 350–51, 509 population growth, 555–56 portfolio, 23, 101 All Weather, see All Seasons strategy; All Weather portfolio asset allocation in, see asset allocation balanced, 381–82, 384, 398, 412 diversity in, see diversification rebalancing, 358, 359–62, 363, 393n, 402, 613 60/40, 399 see also investments portfolio theory, 297, 379, 469 possibilities, 245 posture, improving, 197 poverty, 596–98, 599 Powell, Colin, xxvii Preisano, Michael, 115 present bias, 66 priming, 584–85 Prince, Bob, 387 private placement life insurance (PPLI), 443–48 accessing money in, 446–47 investment management, 446 probabilities, 361 probate, avoiding, 448 Protégé Partners, 95, 487 Pure Alpha, 375–76, 397 Qantas, 318 quality of life, 37, 292, 538, 576, 577, 580–82, 583 questions, wording of, 40 Quinn, Jane Bryant, 92 Ramsey, Dave, 254 Random Walk Down Wall Street, A (Malkiel), 49, 96–97 Rattner, Steven, 550 real estate: and financial meltdown, 313–14, 514–15, 516–18 first trust deeds, 286, 312–13 investment in, 283–86, 308, 323 residential real estate loan, 312–14 real estate investment trusts (REITs), 286, 323, 328, 473 rebalancing, 358, 359–62, 363, 393n, 402, 613 redemption fee, 115 Redstone, Sumner, 34 registered investment advisor (RIA), 126 repetition, 42 residential real estate loan, 312–14 retirement: automatic savings for, 64–65 being unprepared for, 90–91 contribution limits, 156 financial advisors for, 126–27 401(k)s, see 401(k)s individual management of, 140 inertia in planning for, 40 investment in mutual funds, 93, 110, 114, 141 IRAs, 93, 110, 148 money needed for, 32, 210–11 and moving to another place, 288 outliving your money in, 32, 421 pensions, 34–35, 86 quality of life in, 37, 538 saving for, 37 sequence of returns in, 412 and Social Security, 31–32, 34 and target-date funds, 158, 159, 162–63 and technology, 410 retirement communities, investment in, 284–86, 308 Retirement Savings Drain, The (Hiltonsmith), 109 retooling, 264, 265–66 return on investment, 116–20, 281–86 actual returns, 119, 414 asset allocation, 282–83 asymmetric risk/reward, 281–82 average returns, 116–18, 119, 415 diversification, 276, 282, 297 dollar-weighted returns, 118–19, 121 doubling your money, 283, 284 maximizing, 379 sequence of returns, 412 time-weighted returns, 118–19, 121 reversion to the mean, 321, 471 Richest Man in Babylon, The (Clason), 69 Riley, Theresa, 597 risk aversion, 400–401 Risk/Growth Bucket, 321–39 and asset allocation, 326, 336–39, 612 being prepared to lose, 321 collectibles, 324 commodities, 324 currencies, 324 diversification, 325–26 equities, 322–23 factors to consider, 331–36 high-yield (junk) bonds, 323 real estate, 323 structured notes, 324–25 riskless return, 174 Risk Parity, 390 risk/reward, 172–82 and asset allocation, 159, 326, 336–39, 383 asymmetric, 25, 173, 174, 180, 281–82, 283, 456, 486, 493–94, 515–16, 535–36 and balance, 382–83, 390 diversification, 297, 300, 379, 383, 456, 472–73 market-linked CDs, 178–79 market timing, 296 Risk/Growth Bucket, 321–39 security as subjective concept, 314–15 Security/Peace of Mind Bucket, 301–20 security selection, 296 structured notes, 176–78 risk tolerance, 331, 332–36, 399–401 Robbins, Bonnie Pearl, 284 Robbins, Tony: final words of advice, 607 interviews with masters, 453–55; see also specific names story of poverty to wealth, 602–6 Robin Hood Foundation, 16, 489 robotics, 562 Rockefeller, John D., 595 Roethke, Theodore, 557 Rogoff, Kenneth, 520 Rohn, Jim, 42, 246, 260–62, 263, 266, 585 role model, 206 Rolling Stones, 34 Roman Empire, annuities in, 167 Roosevelt, Eleanor, 212 Roosevelt, Franklin D., 31, 269, 409 Roosevelt, Theodore, 19, 331 Roth, Marc, 560 Roth, William, 150 Roth 401(k), 150–51, 152–53, 154, 235 Roth IRA, 150, 153–54, 236, 278, 442 Rothschild, Baron, 456, 523 Rowe, Mike, 265 Royal Bank of Canada, 177, 310 rule of 72, 283 Rutgers University, risk-tolerance scale, 332 Rwandan orphans, 592–93 S&P index funds, 92, 93–94, 95–96, 98, 99, 101, 105, 107, 182, 330, 354, 357, 383, 394–95, 396, 474 15, 173 Salesforce Foundation, xxvii Sandy Hook Elementary School, 591–92 Save More Tomorrow (SMarT), 66–68, 151, 236–37, 249 savings, 55–70 automatic, 59, 64–65, 69–70 compounding, 60, 62–65, 238, 280 creating wealth, 64 critical mass of, 90 cutting expenses, 253–56 discipline of, 543 financial plan founded on, 56–57 investment, 58, 90, 247–58, 292 mindful, 256–58 paying yourself first, 62 and pay raises, 67, 68 percentage of, 59, 63, 68–69 rate of, 37 for retirement, 37 as ultimate ATM, 55 Schilling, Curt, 52 Schwab, Charles, 10, 47, 116, 130, 235, 455, 529–39, 529 Schwed, Fred Jr., 124 Securities and Exchange Commission (SEC), 135, 530 security, subjective concept of, 314–15 Security/Peace of Mind Bucket, 301–20 annuities, 308, 438 asset allocation, 302, 310, 328–29, 338, 612 bonds, 303–5, 306, 315–20 cash/cash equivalents, 302–3 CDs, 305, 306 and compounding, 311, 312 home, 306–8 life insurance, 309 pension, 308 structured notes, 309–10 time on your side, 310–15 security selection, 471, 472 self-employment: and automatic savings, 65, 69 and Solo 401(k), 153 self-esteem, 580 self-fulfilling prophecy, 189–90 senior housing, investment in, 284–86, 308 sequence of returns, 412 serendipity, 228–29 service, 538–39, 586, 616 7 Simple Steps, 23, 41, 43–48 asset allocation, 45, 292, 295, 612–13 decide savings percentage, 45–46, 63–66, 609 invest like .001%, 46–47, 614–15 just do it, 47, 615–16 know the rules, 44, 85, 609–10 lifetime income plan, 46, 613–14 make the game winnable, 45–46, 610–12 timing, 348–49 turn dreams into reality, 212–25 shareholders, 459–60, 461, 464 Shiller, Robert, 307, 323, 501, 595 Shoven, John, 31, 139 significance, 76–77, 204 Silicon Valley, 264, 557 Sinclair, Upton, 121 60/40, 399 Slaughter, Frank, 183 slavery, child, 600 small business: and automatic savings, 65, 69 cash-balance plan for, 155 and 401(k)s, 146–48, 152, 153, 181 Social Security, 243, 409, 418 as government liability, 149 and retirement, 31–32, 34 Social Security Act, 31 soft-dollar trading, 114–15 Solo 401(k)s, 153 Soros, George, 385 South Korea, education in, 266–67 Spanx, 271 speculation, 172, 325 speed it up, 611 change your life, 287–92 and Dream Bucket, 344 earn and invest, 259–72 get better returns, 281–86 prepaying your mortgage, 251–52 reduce fees and taxes, 273–80 save and invest, 247–58 spending plan, 253–56 SPIVA, 98 stable value funds, 160 stagger (lead), 232 state, physical, changing, 196–99 stock index, 93–94 stock market: diversity in, 104 and inflation, 386 long-term investment in, 93, 104, 329–30, 351 volatility in, 104, 382, 390 stock picking, 119, 180, 296 stocks: and bonds, 158, 160, 329–30 employer’s, 162–63 foreign, 328, 473, 535 poison pills, 464 story: changing, 194–95, 249 of empowerment, 190 limiting, 188–95 new, 192 power of, 188–95 as self-fulfilling prophecy, 189–90 tipping point in, 193 strategy: breakthrough in, 187–88, 199 save more and invest the difference, 248–58 stress: emotional, 191 financial, 190–91 physical signs of, 191, 197 Stronghold Financial, 130–32, 135, 163, 236, 310, 358, 403 Stronghold Wealth Management, 25, 130 structured notes, 176–78, 309–10, 324–25 student loans, 239–40 sub accounts, 168, 424 subconscious mind, 205–6 success: clues left by, 9, 187 destroyed by emotion, 355 psychology of, 189 and unconventional wisdom, 384–85 without fulfillment, 575 suitability standard, 125–26 Summers, Larry, 454 Sun Tzu, 369, 453 survival instinct, 570, 585 Swensen, David, 91, 468, 533 on active management, 165 on asset allocation, 296, 469, 471–74 author’s interview with, 295–96, 384, 468–75 on bonds, 316, 317, 318 on brokers, 125 on ETFs, 322–23 and index funds, 92, 472 on mutual funds, 102, 110, 470–71, 472 portfolio of, 326–31, 337, 339, 473–74 on rebalancing, 359 on returns, 276–77 on reversion to the mean, 471 on risk/reward, 295–96 Unconventional Success, 469 and Yale endowment fund, 10, 101, 110, 295, 468–69 Yale model, 276, 469, 471–74 SwipeOut, 596–600, 602 Tae Kwon Do, 42–43 Takahashi, Dean, 469 target-date funds (TDFs), 157–64, 181 glide paths of, 158 low-cost, 163 misconceptions about, 159, 162–64 performance of, 161–62, 163 and retirement, 158, 159, 162–63 stable value funds vs., 160 tax-advantage accounts, 235, 472 tax efficiency, 287–89, 402 taxes, 35–37 capital gains, 277 and compounding, 235, 277–78, 279, 445–46 deferring, 235, 236, 278, 279, 408 and depreciation, 285–86 and exclusion ratio, 421n and 401(k)s, 149–50, 152, 235, 472 future, 149–50, 153, 236 and home ownership, 308 income, 277, 290 and moving to another place, 288–90 and mutual funds, 111, 114, 119, 279, 472 and planning, 235–36 and PPLI, 444 and rebalancing, 362 reducing, 273–80 tax loss harvesting, 362–63 T-bills, 316 T-bonds, 316 teachers, 266–67 technology, 87 and agriculture, 264 artificial intelligence (AI), 569, 570 cloud computing, xxvii computers “R” us, 569–71 disruption caused by, 264–65 email, 553 and energy, 556–57 exponential growth of, 564–65 food, 566 genome project, 565 as hidden asset, 549–53 and internet expansion, 560–61 jobs in, 264 and living standards, 554–55 Maker Revolution, 559–61 medical, 410, 551–52, 557–59, 562, 566–68 nanotechnology, 562, 567 robotics, 562 SwipeOut, 596–600, 602 3-D printing, 410, 561–62, 567, 568 technology wave, 562–63 TED Talks, 18, 39, 67 Templeton, Sir John, 61–62, 350, 351, 456, 524, 540 author’s interviews with, 61, 79, 455, 540–45 on gratitude, 79, 347, 544–45, 577 and philanthropy, 62, 541, 542, 602 and savings, 62, 63, 542–43 on trust, 542 1035 exchange, 171 Tepper, David, 263, 517 Teresa, Mother, 541 Tergesen, Anne, 426–27 testosterone, 197, 334 Thaler, Richard, 66–67, 249 Thatcher, Margaret, 69 Think and Grow Rich (Hill), 19 $13 trillion lie, 93, 97 Thompson, Derek, 431 Thoreau, Henry David, 341, 576 3-D printing, 410, 561–62, 567, 568 Three to Thrive, 211–12, 225–26, 292, 585 TIAA-CREF, 169, 402, 447–48, 470 Tier 1 capital, 442 time: and compounding, 311, 312 diversifying against, 355 value of, 287, 570 time-weighted returns, 118–19, 121 timing, 62, 256, 348–66 and dollar-cost averaging, 355–59, 363, 365–66, 613 getting in front of a trend, 270 and long-term investing, 351 market, 97, 296, 300, 354, 380, 471–72 mistake in, 177, 348 and mob mentality, 348–49 and patterns of investing, 359 and probabilities, 361 and rebalancing, 359–62 and tax loss harvesting, 362–63 and volatility, 363 tipping point, 90, 193 TIPS (Treasury inflation-protected securities), 305, 316–17, 328, 329, 374, 473, 474 tithing, 602 T-notes, 316 Today, 349–50, 485 tontines, 167 training, retooling our skill sets, 264, 265 transformation, 185, 195 Trebek, Alex, 301 Trichet, Jean-Claude, 518, 520 Truckmaster truck driving school, 259 Trump, Donald, 210 trust, absence of, 119–20, 125, 542 trust deeds, 313 Tubman, Harriet, 345 Tullis, Eli, 491 Turner, Ted, 595 TWA, 318 Twain, Mark, 11, 481, 566 Tyson, Mike, 6, 53, 60 uncertainty/variety, 75 Unconventional Success (Swensen), 469 unemployment, 264, 265 unicorns, 14n, 99, 180, 331, 350 United Parcel Service (UPS), 60 United States: income ladder in, 263 national debt of, 149, 236 states with no income tax, 290 Unleash the Power Within (UPW), 15, 194, 559 Unlimited Power (Robbins), 18 255–56 Uram, Matt, 551–52 Usher, 15, 18 Ustinov, Peter, 549 US Treasury bonds, 305, 316–18, 328–29, 400, 473, 474 US Treasury money market fund with checking privileges, 303 V2MOMs, xxvi value, added, 193, 261, 262–63, 265, 268, 269, 272, 343, 595, 611 value investing, 486 Vanguard Group: annuities, 169, 402 founding of, 470, 476–78 index funds, 95, 97, 113, 143, 144, 157, 472 mutual funds, 10, 477 return after taxes, 235 target-date fund, 163, 181 variety/uncertainty, 75 Venter, Craig, 566–67 Vichy-Chamrond, Marie de, 238 Virgin Airways, 173 volatility, 104, 301, 321, 356, 358, 363, 382, 390 wage, minimum, 263 Walton, Sam, 76 Ward, William A., 54 Warren, Doug, 138 water, potable, 565–66, 599–600 wealth: creation of, 64 final secret of, 588–606 growing, 176 keeping, 295 kinds of, 576 and living trust, 448–49 secrets of ultrawealthy, 442–49, 614 tithing, 602 without risk, 181 wealth calculator, 234, 236, 611 Wealth Mastery seminars, 332–33 Weissbluth, Elliot, 128–31 whale blubber, 556 Where Are the Customers’ Yachts?


pages: 499 words: 148,160

Market Wizards: Interviews With Top Traders by Jack D. Schwager

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", Alan Greenspan, Albert Einstein, asset allocation, backtesting, beat the dealer, Bretton Woods, business cycle, buy and hold, commodity trading advisor, computerized trading, conceptual framework, delta neutral, Edward Thorp, Elliott wave, fixed income, implied volatility, index card, junk bonds, locking in a profit, margin call, market bubble, market fundamentalism, Market Wizards by Jack D. Schwager, Michael Milken, money market fund, Nixon triggered the end of the Bretton Woods system, pattern recognition, Paul Samuelson, Ralph Nelson Elliott, random walk, Reminiscences of a Stock Operator, short selling, Teledyne, transaction costs, uptick rule, yield curve, zero-sum game

They had lost a small fortune in olive oil futures. So they gave praying a whirl. It worked. Olive oil went up. —Kurt Vonnegut Jr. Slaughterhouse Five If the random walk theorists are correct, then Earthbound traders are suffering from the same delusions as the zoo inhabitants of Kilgore Trout’s novel. (Kilgore Trout is the ubiquitous science fiction writer in Kurt Vonnegut’s novels.) Whereas the prisoners on Zircon-212 thought their decisions were being based on actual price quotes—they were not—real-life traders believe they can beat the market by their acumen or skill. If markets are truly efficient and random in every time span, then these traders are attributing their success or failure to their own skills or shortcomings, when in reality it is all a matter of luck.

There is insufficient screening to determine which reports should actually be written. Another major problem with Wall Street research is that it seldom provides sell recommendations. I would assume, given the consistency of your success as a stock investor for over twenty-five years, that you don’t think very much of the random walk theory. The stock market is neither efficient nor random. It is not efficient because there are too many poorly conceived opinions; it is not random because strong investor emotions can create trends. In the most general sense, trading success requires three basic components: an effective trade selection process, risk control, and discipline to adhere to the first two items.

This was in July 1972 and, at the time, we were under price controls. The futures market was supposedly also under price controls. This was Nixon’s price freeze? Yes. As I recall, the plywood price was theoretically frozen at $110 per 1,000 square feet. Plywood was one of the markets I analyzed for the firm. The price had edged up close to $110, and I put out a bearish newsletter saying even though supplies were tight, since prices couldn’t go beyond $110, there was nothing to lose by going short at $110. How did the government keep prices at the set limits? What prevented supply and demand from dictating a higher price? It was against the law for prices to go higher.


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Alvin Toffler, Atul Gawande, backtesting, Bear Stearns, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, California energy crisis, capital asset pricing model, Carl Icahn, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, Future Shock, game design, global macro, hindsight bias, housing crisis, index fund, Isaac Newton, Jim Simons, John Bogle, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, managed futures, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, Market Wizards by Jack D. Schwager, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, Teledyne, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

Michael Mauboussin and Kristen Bartholdson see clearly the state of affairs: “Normal distributions are the bedrock of finance, including the random walk, capital asset pricing, value-at-risk, and Black-Scholes models. Value-at-risk (VaR) models, for example, attempt to quantify how much loss a portfolio may suffer with a given probability. While there are various forms of VaR models, a basic version relies on standard deviation as a measure of risk. Given a normal distribution, it is relatively straightforward to measure standard deviation, and hence risk. However, if price changes are not normally distributed, standard deviation can be a very misleading proxy for risk.”14 Chapter 8 • Science of Trading The problem with using standard deviation as a risk measurement can be seen with the example where two traders have similar standard deviations, but might show entirely different distribution of returns.

This consensus is revealed by analyzing price. Mark Abraham Quantitative Capital Management, L.P. 10 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets “I often hear people swear they make money with technical analysis. Do they really? The answer, of course, is that they do. People make money using all sorts of strategies, including some involving tea leaves and sunspots. The real question is: Do they make more money than they would investing in a blind index fund that mimics the performance of the market as a whole? Most academic financial experts believe in some form of the random-walk theory and consider technical analysis almost indistinguishable from a pseudoscience whose predictions are either worthless or, at best, so barely discernibly better than chance as to be unexploitable because of transaction costs.”12 Markets aren’t chaotic, just as the seasons follow a series of predictable trends, so does price action.

Often associated with strategies employed by commodity trading advisors from the managed futures industry. 3. Penny stock: Loosely defined as stock with a low nominal share price that typically trades in the over-the-counter market, often an OTC Bulletin Board or Pink Sheets quoted stock. 4. Recorded percent return: ((exit price / entry price) –1) 5. Recorded initial risk: (absolute_value((stop loss price / entry price) –1)) 6. Short selling: The selling of a security that the seller does not own with the goal of buying the security back at a lower price, thus profiting from a decline. 7. Buy-in: When a short seller is forced to repurchase the shorted share in order to deliver them to the rightful owner. 8.


pages: 559 words: 155,372

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez

Airbnb, airport security, always be closing, Amazon Web Services, Big Tech, Burning Man, business logic, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, content marketing, corporate governance, Credit Default Swap, crowdsourcing, data science, deal flow, death of newspapers, disruptive innovation, Dr. Strangelove, drone strike, drop ship, El Camino Real, Elon Musk, Emanuel Derman, Fairchild Semiconductor, fake it until you make it, financial engineering, financial independence, Gary Kildall, global supply chain, Goldman Sachs: Vampire Squid, Hacker News, hive mind, How many piano tuners are there in Chicago?, income inequality, industrial research laboratory, information asymmetry, information security, interest rate swap, intermodal, Jeff Bezos, Kickstarter, Malcom McLean invented shipping containers, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, messenger bag, minimum viable product, MITM: man-in-the-middle, move fast and break things, Neal Stephenson, Network effects, orbital mechanics / astrodynamics, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, public intellectual, Ralph Waldo Emerson, random walk, Reminiscences of a Stock Operator, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, Social Justice Warrior, social web, Socratic dialogue, source of truth, Steve Jobs, tech worker, telemarketer, the long tail, undersea cable, urban renewal, Y Combinator, zero-sum game, éminence grise

In a smaller way, mine was the plight of the first tech-boom bankruptcies, who paid taxes when prices were dear, but sold stock when prices were cheap. I saw relatively little of my ersatz AdGrok proceeds in the form of Facebook stock. All of that three-year struggle of endless sixteen-hour days, whether for AdGrok or for Facebook (and hating most of it, as if you couldn’t tell), was mostly for nothing. So you see, the boys and I have very different financial futures awaiting us. Some of it was due to the detailed vagaries of tech compensation and the random walks of public stock prices. Mostly, though, it was due to my getting chewed up and spit out by the Facebook machine within two years, while the boys gamboled in the bucolic hipster pastures of Twitter for four years and counting—the very pastures I struggled and plotted mightily to avoid, and which I traded for the horror show that would thanklessly reject me despite the moneymaker I built them.

The crowd went wild as the hour expired, and just as quickly the entire riot disbursed as everyone hurried back to phones and risk reports. The trading floor smelled like the inside of a deep fryer for the whole day. Capitalism marched inexorably onward.* Of course, the betting didn’t stop at burgers. Analysts would be pressed into push-up contests, with over/under bets on the total. And so, on a random walk across the floor, busily engaged with the important work of capitalism, one could trip on a trading analyst and a particularly fit sales VP, faces red with exertion, sweating through their pressed shirts and pumping out their 237th push-up in an hour, with shouted bets raging all around. On Friday afternoons, to shatter the preweekend slump, the entire desk would play an interesting game.

The fastest way to cheapen anything—be it a woman, a favor, or a work of art—is to put a price tag on it. And that’s what capitalism is, a busy greengrocer going through his store with a price-sticker machine—ka-CHUNK! ka-CHUNK!—$4.10 for eggs, $5 for coffee at Sightglass, $5,000 per month for a run-down one-bedroom in the Mission. Think I’m exaggerating? Stop and think for a moment what this whole IPO ritual was about. For the first time, Facebook shares would have a public price. For all the pageantry and cheering, this was Mr. Market coming along with his price-sticker machine and—ka-CHUNK!—putting one on Facebook for $38 per share.


Globalists: The End of Empire and the Birth of Neoliberalism by Quinn Slobodian

"World Economic Forum" Davos, Alan Greenspan, Asian financial crisis, Berlin Wall, bilateral investment treaty, borderless world, Bretton Woods, British Empire, business cycle, capital controls, central bank independence, classic study, collective bargaining, David Ricardo: comparative advantage, Deng Xiaoping, desegregation, Dissolution of the Soviet Union, Doha Development Round, eurozone crisis, Fall of the Berlin Wall, floating exchange rates, full employment, Garrett Hardin, Greenspan put, Gunnar Myrdal, Hernando de Soto, invisible hand, liberal capitalism, liberal world order, Mahbub ul Haq, market fundamentalism, Martin Wolf, Mercator projection, Mont Pelerin Society, Norbert Wiener, offshore financial centre, oil shock, open economy, pattern recognition, Paul Samuelson, Pearl River Delta, Philip Mirowski, power law, price mechanism, public intellectual, quantitative easing, random walk, rent control, rent-seeking, road to serfdom, Ronald Reagan, special economic zone, statistical model, Suez crisis 1956, systems thinking, tacit knowledge, The Chicago School, the market place, The Wealth of Nations by Adam Smith, theory of mind, Thomas L Friedman, trade liberalization, urban renewal, Washington Consensus, Wolfgang Streeck, zero-sum game

Buchanan complained that “to imply, as Hayek seems to do, that t­ here neither exists nor should exist a guideline for evaluating existing institutions seems to me to be a counsel of despair in the modern setting.”93 John Gray contended that Hayek asks us to “entrust ourselves to all the vagaries of mankind’s random walk in historical space.”94 Does Hayek’s version of system theory ­really prescribe a kind of quietism in the face of the market? How should apparent deviations be corrected in a system of “super-conscious” rules and limited knowledge? Th ­ ese questions came to a head in the late 1970s as neoliberals witnessed what two of them called “the undermining of the world trade order” in the NIEO and the move of industrialized nations to the “new protectionism” of voluntary export restraints, orderly marketing arrangements, and a w ­ hole host of 95 other mea­sures they read as barriers to trade.

Hayek’s model is an economy of princi­ples, or “rules of just conduct,” as he called them, derived from physiology, the accretion of ­human tradition and—­the site of action—­the thin line of deliberate design. It is thus misleading to characterize Hayek’s writings from the 1970s as condemning us to, as Gray put it, “a random walk.” Hayek says in black and white that “collaboration ­will always rest both on spontaneous order as well as on deliberate organ­ization” and labels his proj­ect one itself of design.117 For many scholars, Hayek’s focus on the evolutionary, spontaneous, and unconscious aspects of order can distract from the fact that hard law encases the cosmos.

Haberler proposed that he could prove that “­free trade is beneficial for all even when ­there is no freedom of migration and the ­peoples remain firmly rooted in their countries.”97 He did so by revisiting David Ricardo’s idea of comparative advantage but recasting it without the discredited ­labor theory of value. In his version, workers did not need to be mobile over national borders as long as prices ­were. If prices accurately reflected the relative supply and demand on markets, then t­ hese would guide entrepreneurs to the most efficient use of their resources. For prices to serve their function, however, they must not encounter re­sis­tance. He gave the specific example of l­abor: “­Here the price mechanism is partially switched off, and real frictional losses can occur in the form of strikes and unemployment.” Luckily, he pointed out, “­labor was the most mobile and diverse of all the ­factors of production.”98 Even if unemployment figures remained constant, the ­actual mass of unemployed usually rotated in and out as ­people moved from position to position.


Trend Commandments: Trading for Exceptional Returns by Michael W. Covel

Alan Greenspan, Albert Einstein, Alvin Toffler, behavioural economics, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, disinformation, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, global macro, Jim Simons, Lao Tzu, Long Term Capital Management, managed futures, market bubble, market microstructure, Market Wizards by Jack D. Schwager, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, Sharpe ratio, systematic trading, the scientific method, three-martini lunch, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

Crabel, Toby. Day Trading with Short Term Price Patterns and Opening Range Breakout. Jupiter: Rahfelt and Associates, 1990. Douglas, Mark. The Disciplined Trader: Developing Winning Attitudes. New York: New York Institute of Finance, 1990. Dubner, Stephen, and Steven D. Levitt. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything. New York: Harper Collins, 2005. Eng, William. Trading Rules: Strategies for Success. Chicago: Dearborn Financial Publishing, Inc., 1990. Bibliography 243 Fabricand, Burton. The Science of Winning: A Random Walk Along the Road to Investment Riches. London: High Stakes Publishing, 2002.

For a good return, you gotta go bettin’ on chance and then you’re back with anarchy, right back in the jungle.1 Price Action Tell me something the “market” does not know. The idea that you can know enough about Apple, oil, GE, and gold to trade them all the same way may seem preposterous, but think about what they all have in common: Price. Market price is objective data. You can look at individual price histories, without knowing which market is which, and still trade all successfully. That is not what they teach at Harvard, Wharton, Kellogg, Stern, Darden, or pick your favorite business school du jour. Don’t guess how far a trend will go. You cannot. Price makes the news, not the other way around.

All of a sudden GOOG jumps, or breaks out, to a price level of 600. That type of upward movement from a range is a trigger—the breakout for an entry. You might say, “I don’t know if GOOG is going to continue up, but it’s been going sideways for six months, and all of a sudden, the price has jumped to 600 making a new six-month highest high. I’m in.” You are not in this game to find bargains. You are in this to follow trends, and if the market is heading up, enter. If the market is heading down, enter short. The three best entry indicators in order are price, price, and price. If trend trader Amos Hostetter of Commodities Corporation lost 25 percent, he’d exit: “Never mind the cheese.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

behavioural economics, Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, data science, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Gregor Mendel, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, TED Talk, text mining, the scientific method, Thomas Davenport

It turns out that the chances are pretty good—just over 50 percent, in fact (see http://keepingupwiththequants.weebly.com). An understanding of probability is extremely useful not only for understanding birthday parties, but also a variety of human endeavors. If you don’t understand probability, you won’t understand that the stock market is a random walk (that is, changes in stock prices involve no discernable pattern or trend) and that some stock pickers are likely to do better than average for a number of years in a row, but then inevitably crash to earth. You won’t understand the regression to the mean phenomenon; if your income is well above average, for example, your child’s income is likely to be less than yours.

The solution to this equation was the Black-Scholes formula, which suggested how the price of a call option might be calculated as a function of a risk-free interest rate, the price variance of the asset on which the option was written, and the parameters of the option (strike price, term, and the market price of the underlying asset). The formula introduces the concept that, the higher the share price today, the higher the volatility of the share price, the higher the risk-free interest rate, the longer the time to maturity, and the lower the exercise price, then the higher the option value. The valuation of other derivative securities proceeds along similar lines.

The price that is paid for the asset when the option is exercised is called the exercise price. The last day on which the option may be exercised is called the maturity date. The simplest kind of option, which is often referred to as a call option, is one that gives the right to buy a single share of common stock. A risk premium is the amount an investor pays for a stock or other asset over the price of a risk-free investment. In general, the higher the price of the stock, the greater the value of the option. When the stock price is much greater than the exercise price, the option is almost sure to be exercised. On the other hand, if the price of the stock is much less than the exercise price, the option is almost sure to expire without being exercised, so its value will be near zero.


Economic Gangsters: Corruption, Violence, and the Poverty of Nations by Raymond Fisman, Edward Miguel

accounting loophole / creative accounting, Andrei Shleifer, Asian financial crisis, barriers to entry, behavioural economics, blood diamond, clean water, colonial rule, congestion charging, crossover SUV, Donald Davies, European colonialism, failed state, feminist movement, George Akerlof, Great Leap Forward, income inequality, income per capita, Intergovernmental Panel on Climate Change (IPCC), invisible hand, mass immigration, megacity, oil rush, prediction markets, random walk, Scramble for Africa, selection bias, Silicon Valley, South China Sea, unemployed young men

But this would never have been possible if medicine had continued in its old ways, with doctors basing treatment on their own individual experiences, their “feel” for a patient, or ancient remedies (such as bleeding people with leeches). Grateful people around the world are living longer and healthier lives as a result.5 A Random Walk to Knowledge in Busia There isn’t any conceptual reason why economists can’t harness the power of randomization, by picking villagers—or even entire villages—to receive an economic treatment, and 192 L EARNI N G TO F I G H T ECO N O M I C G A N G S T E R S compare these changes to control villagers.

If most investors think that the value of future profits will be high relative to the share’s purchase price, we’d see lots of buyers and few sellers at the going price, which pushes the stock price up; if most investors think profits will be low, their selling drives prices down. 25 CH A PTER TW O In reality, there’s a constant flow of information on Amazon’s business situation that forces investors to rethink whether they should be buying or selling the stock, and investors react accordingly. For example, if UPS announces that it’s raising the cost of book delivery, which eats into Amazon’s profits, then selling by investors will drive Amazon’s stock price down. Because of this daily deluge of updates and the differences of opinion among investors in how to interpret the news—How much of the UPS price hike will Amazon be able to pass on to its customers?

Because of this daily deluge of updates and the differences of opinion among investors in how to interpret the news—How much of the UPS price hike will Amazon be able to pass on to its customers? How much of what we read in the morning paper is rumor versus reality?—prices bounce around a lot. A share price reflects investors’ consensus view of future profits—the invisible hand of the market at work—and this collective wisdom of thousands of well-informed buyers and sellers is captured by the daily ups and downs of stock prices. How does this fit into our corruption discussion? Recall the West End Corporation (WEC) discussed in chapter 1 that was set up by journalists to sting politicians and military brass by trying to bribe its way into selling night-vision cameras to the Indian army.


pages: 205 words: 20,452

Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke

backpropagation, call centre, computer vision, discrete time, G4S, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

The second smallest eigenvalue (called the algebraic connectivity of G) provides graph connectivity information and is always smaller or equal to the vertex connectivity of G [30]. Laplacian spectra of graphs have many applications in graph partitions, isoperimetric problems, semidefinite programming, random walks on graphs, and infinite graphs [25,29]. The relationship between graph spectra and graph distance measures can be established using an eigenvalue interpretation [27]. Consider the weighted graph matching problem for two graphs G = (VG , EG ) and H = G H and wE , (VH , EH ), where |VG | = |VH | = n, with positive edge-weights wE which is defined as finding a one-to-one function Φ : VG → VH such that the graph distance function: dist(Φ) = G 2 H wE (u, v) − wE (Φ(u), Φ(v)) (3.1) u∈VG ,v∈VH is minimal.

Number of series Points per series Total number of points 98 stocks, 2.3 years 98 610 60,000 Air and sea Temperatures 68 buoys, 18 years, 2 sensors per buoy 136 1,800–6,600 450,000 Wind speeds 12 stations, 18 years 12 6,570 79,000 EEG 64 electrodes, 1 second 64 256 16,000 series and show that it works well with compressed data. Finally, we present a technique for indexing and retrieval of compressed series; we have tested it on four data sets (Table 1), which are publicly available through the Internet. Stock prices: We have used stocks from the Standard and Poor’s 100 listing of large companies for the period from January 1998 to April 2000. We have downloaded daily prices from America Online, discarded newly listed and de-listed stocks, and used ninety-eight stocks in the experiments. Air and sea temperatures: We have experimented with daily temperature readings by sixty-eight buoys in the Pacific Ocean, from 1980 to 1998, downloaded from the Knowledge Discovery archive at the University of California at Irvine (kdd.ics.uci.edu).

Researchers have also studied the use of small alphabets for compression of time series, and applied string matching to the pattern search [Agrawal et al. (1995), Huang and Yu (1999), Andr-Jnsson and Badal (1997), Lam and Wong (1998), Park et al. (1999), Qu et al. (1998)]. For example, Guralnik et al. (1997) compressed stock prices using a nine-letter alphabet. Singh and McAtackney (1998) represented stock prices, particle dynamics, and stellar light intensity using a three-letter alphabet. Lin and Risch (1998) used a two-letter alphabet to encode major spikes in a series. Das et al. (1998) utilized an alphabet of primitive shapes for efficient compression. These techniques give a high compression rate, but their descriptive power is limited, which makes them inapplicable in many domains.


pages: 654 words: 191,864

Thinking, Fast and Slow by Daniel Kahneman

Albert Einstein, Atul Gawande, availability heuristic, Bayesian statistics, behavioural economics, Black Swan, book value, Cass Sunstein, Checklist Manifesto, choice architecture, classic study, cognitive bias, cognitive load, complexity theory, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, delayed gratification, demand response, endowment effect, experimental economics, experimental subject, Exxon Valdez, feminist movement, framing effect, hedonic treadmill, hindsight bias, index card, information asymmetry, job satisfaction, John Bogle, John von Neumann, Kenneth Arrow, libertarian paternalism, Linda problem, loss aversion, medical residency, mental accounting, meta-analysis, nudge unit, pattern recognition, Paul Samuelson, peak-end rule, precautionary principle, pre–internet, price anchoring, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, Robert Metcalfe, Ronald Reagan, Shai Danziger, sunk-cost fallacy, Supply of New York City Cabdrivers, systematic bias, TED Talk, The Chicago School, The Wisdom of Crowds, Thomas Bayes, transaction costs, union organizing, Walter Mischel, Yom Kippur War

Most of the buyers and sellers know that they have the same information; they exchange the stocks primarily because they have different opinions. The buyers think the price is too low and likely to rise, while the sellers think the price is high and likely to drop. The puzzle is why buyers and sellers alike think that the current price is wrong. What makes them believe they know more about what the price should be than the market does? For most of them, that belief is an illusion. In its broad outlines, the standard theory of how the stock market works is accepted by all the participants in the industry. Everybody in the investment business has read Burton Malkiel’s wonderful book A Random Walk Down Wall Street. Malkiel’s central idea is that a stock’s price incorporates all the available knowledge about the value of the company and the best predictions about the future of the stock.

positive test strategy possibility effect: gambles and; threats and post-traumatic stress poverty precautionary principle predictability, insensitivity to predictions and forecasts; baseline; clinical vs. statistical; disciplining; of experts, see expert intuition; extreme, value of; formulas for, see formulas; increasing accuracy in; low-validity environments and; nonregressive; objections to moderating; optimistic bias in; outside view in; overconfidence in; planning fallacy and; short-term trends and; valid, illusion of; see also probability preference reversals; unjust premonition, use of word premortem pretentiousness language pricing policies priming; anchoring as t="-5%"> Princeton University probability; base rates in, see base rates; decision weights and, see decision weights; definitions of; and disciplining intuition; less-is-more pattern and; Linda problem and; overestimation of; plausibility and; and predicting by representativeness; prior, insensitivity to; professional stereotypes and; of rare events, see rare events; representativeness and, see representativeness; similarity and; subjective; as sum-like variable; see also predictions and forecasts probability neglect Proceedings of the National Academy of Sciences professional stereotypes professorial candidates prospect theory; in Albert and Ben problem; blind spots of; cumulative; decision weights and probabilities in; fourfold pattern in; frames and; graph of losses and gains in; loss aversion in; reference points in “Prospect Theory: An Analysis of Decision Under Risk” (Kahneman and Tversky) prototypes psychiatric patients psychological immune system psychology, teaching psychopathic charm psychophysics psychotherapists pundits; see also expert intuition punishments: altruistic; rewards and; self-administered pupil dilation questionnaire and gift experiments questions; substitution of, see substitution Rabin, Matthew radiologists rafters, skilled rail projects randomness and chance; misconceptions of Random Walk Down Wall Street, A (Malkiel) rare events; overestimation of; regret and rational-agent model rationality Rationality and the Reflective Mind (Stanovich) ">rats Reagan, Ronald reciprocal priming recognition recognition-primed decision (RPD) model Redelmeier, Don reference class forecasting regression to the mean; causal interpretations and; correlation and; difficulty in grasping; two-systems view of “Regression towards Mediocrity in Hereditary Stature” (Galton) regret religion remembering self Remote Association Test (RAT) reorganizations in companies repetition representativeness; base rates and; see also base rates; in Linda problem; predicting by; professional stereotypes and; sins of; in Tom W problem research: artifacts in; hypothesis testing in; optimism in resemblance; in predictions resilience responsibility retrievability of instances reversals; unjust rewards; self-administered Rice, Condoleezza risk assessment; aggregation and; broad framing in; decision weights in, see decision weights; denominator neglect and; by experts; and format of risk expression; fourfold pattern in; for health risks; hindsight bias and; laws and regulations governing; loss aversion in; narrow framing in; optimistic bias and; policies for; possibility effect and; precautionary principle and; probability neglect and; public policies and; small risks and; of technologies; terrorism and; see also gambles risk aversion risk seeking “Robust Beauty of Improper Linear Models in Decision Making, The” (Dawes) Rosett, Richard Rosenzweig, Philip Royal Dutch Shell Royal Institution Rozin, Paul < Philip Rumsfeld, Donald Russell Sage Foundation Russia Saddam Hussein sadness safety; health risks and; health violation penalties and; precautionary principle and samples, sampling: accidents of; and bias of confidence over doubt; law of large numbers; law of small numbers; size of; small, exaggerated faith in Samuelson, Paul San Francisco Exploratorium Savage, Jimmie Save More Tomorrow Schelling, Thomas Schkade, David school size Schwarz, Norbert Schweitzer, Maurice Science Scientific American scientific controversies scientific research: artifacts in; hypothesis testing in; optimism in Scottish Parliament self-control self-criticism Seligman, Martin selves; experiencing; remembering sets Shafir, Eldar similarity judgments Simmel, Mary-Ann Simon, Herbert Simons, Daniel Simpson, O.

They visited the house and studied a comprehensive booklet of information that included an asking price. Half the agents saw an asking price that was substantially higher than the listed price of the house; the other half saw an asking price that was substantially lower. Each agent gave her opinion about a reasonable buying price for the house and the lowest price at which she would agree to sell the house if she owned it. The agents were then asked about the factors that had affected their judgment. Remarkably, the asking price was not one of these factors; the agents took pride in their ability to ignore it. They insisted that the listing price had no effect on their responses, but they were wrong: the anchoring effect was 41%.


pages: 241 words: 81,805

The Rise of Carry: The Dangerous Consequences of Volatility Suppression and the New Financial Order of Decaying Growth and Recurring Crisis by Tim Lee, Jamie Lee, Kevin Coldiron

active measures, Alan Greenspan, Asian financial crisis, asset-backed security, backtesting, bank run, Bear Stearns, Bernie Madoff, Bretton Woods, business cycle, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, collapse of Lehman Brothers, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, currency risk, debt deflation, disinformation, distributed ledger, diversification, financial engineering, financial intermediation, Flash crash, global reserve currency, implied volatility, income inequality, inflation targeting, junk bonds, labor-force participation, Long Term Capital Management, low interest rates, Lyft, margin call, market bubble, Money creation, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, negative equity, Network effects, Ponzi scheme, proprietary trading, public intellectual, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, rising living standards, risk free rate, risk/return, sharing economy, short selling, short squeeze, sovereign wealth fund, stock buybacks, tail risk, TikTok, Uber and Lyft, uber lyft, yield curve

Any option will change in price when its underlying changes in price. Take a very deep in-the-money option very close to expiration. The option will almost surely expire in-the-money, and its price will be very close to the current underlying price minus the option strike price. If the underlying price changes a little tomorrow, the option price will change almost one-toone with the underlying price. Conversely, take a very far out-of-the-money option very close to expiration. The option will almost surely expire out-ofthe-money, and its price will be very close to zero. If the underlying price changes a little tomorrow, the option will still be worth almost nothing; its price changes almost zero-to-one with the underlying price.

In the case of an at-the-money call option, the payout profile is The Foundation of Carry in the Structure of Volatility 147 simple and familiar: at expiration, if the underlying asset price is above the option strike price, the call payout goes up one-for-one with the underlying asset price; if the asset price ends up at or below the strike price, the option is worthless. Imagine a situation where the price of the asset starts off at 100. How would a speculator trade in order to replicate the payoff to a call option on the asset, with a strike price equal to 100, which expires in a month’s time? The very simplest way would be for the speculator to resolve to own the asset whenever its price was above 100 and not to own it whenever its price was below. Since the current price equals the strike price and, if nothing changes over the next month, the call option would pay out zero at expiration, the speculator begins with no position.

Need, first of all, comes from threats to survival, and survival is a deeply felt impulse for all entities that survive—whether biological or corporate. The equivalence, for a levered entity, of rebalancing costs with the expected risk of ruin manifests the link between carry and ruin. More generally, need could arise wherever agents might suffer some irreversible negative consequence— in the language of random walks, if they might touch an absorbing barrier. A person might justifiably fear, for example, losing a limb, or, for another example, gaining a criminal record. And while a company or investor that loses assets could eventually regain them, unlike a limb, the well-known fact that if you have lost 50 percent you need to make 100 percent to get back to where you started means all losses contain some ghost of irreversibility.


pages: 333 words: 76,990

The Long Good Buy: Analysing Cycles in Markets by Peter Oppenheimer

Alan Greenspan, asset allocation, banking crisis, banks create money, barriers to entry, behavioural economics, benefit corporation, Berlin Wall, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, book value, Bretton Woods, business cycle, buy and hold, Cass Sunstein, central bank independence, collective bargaining, computer age, credit crunch, data science, debt deflation, decarbonisation, diversification, dividend-yielding stocks, equity premium, equity risk premium, Fall of the Berlin Wall, financial engineering, financial innovation, fixed income, Flash crash, foreign exchange controls, forward guidance, Francis Fukuyama: the end of history, general purpose technology, gentrification, geopolitical risk, George Akerlof, Glass-Steagall Act, household responsibility system, housing crisis, index fund, invention of the printing press, inverted yield curve, Isaac Newton, James Watt: steam engine, Japanese asset price bubble, joint-stock company, Joseph Schumpeter, Kickstarter, Kondratiev cycle, liberal capitalism, light touch regulation, liquidity trap, Live Aid, low interest rates, market bubble, Mikhail Gorbachev, mortgage debt, negative equity, Network effects, new economy, Nikolai Kondratiev, Nixon shock, Nixon triggered the end of the Bretton Woods system, oil shock, open economy, Phillips curve, price stability, private sector deleveraging, Productivity paradox, quantitative easing, railway mania, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, savings glut, secular stagnation, Shenzhen special economic zone , Simon Kuznets, South Sea Bubble, special economic zone, stocks for the long run, tail risk, Tax Reform Act of 1986, technology bubble, The Great Moderation, too big to fail, total factor productivity, trade route, tulip mania, yield curve

The real effects of the financial crisis. Brookings Papers on Economic Activity. Borio, C. (2012). The financial cycle and macroeconomics: What have we learnt? BIS Working Papers No 395 [online]. Available at https://www.bis.org/publ/work395.htm Burton, M. (1973). A random walk down Wall Street. New York, NY: W. W. Norton & Company. Cagliarini, A., and Price, F. (2017). Exploring the link between the macroeconomic and financial cycles. In J. Hambur and J. Simon (Eds.), Monetary policy and financial stability in a world of low interest rates (RBA annual conference volume). Sydney, Australia: Reserve Bank of Australia.

The varying relationship between bonds and equities, which is affected by both the cycle and longer-term inflation expectations, can be viewed through the correlation between these two asset markets. Theoretically, when bond prices rise (and their yields, or the level of interest rates, fall), equity prices tend to rise (often buoyed by higher valuations). By contrast, rising interest rates or bond yields (and falling bond prices) tend to be negative for equities because the rate at which future cash flows can be discounted would be increasing (therefore reducing the net present value of equity cash flows). So, there is usually a positive correlation between equity and bond prices (or a negative correlation between bond yields and equity prices). For much of the history, the positive correlation between bond and equity prices has generally been the norm.

That said, although interest in economic and financial cycles has a long history, views on whether they can be predicted are widely contested. One set of ideas about the inability to anticipate future price movements in markets stems from the efficient market hypothesis (Fama 1970), which argues that the price of a stock, or the value of a market, reflects all of the information available about that stock or market at any given time; the market is efficient in pricing and so is always correctly priced unless or until something changes. Following on from this idea is the argument that an investor cannot really predict the market, or how a company will perform.


pages: 267 words: 72,552

Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge

accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Apollo 11, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, Bear Stearns, behavioural economics, bitcoin, blockchain, book value, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, cognitive load, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, data science, Didi Chuxing, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, flying shuttle, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, Higgs boson, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, Large Hadron Collider, lone genius, low cost airline, low interest rates, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, radical decentralization, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Robinhood: mobile stock trading app, Sam Altman, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, subprime mortgage crisis, Suez canal 1869, tacit knowledge, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, vertical integration, William Langewiesche, Y Combinator

It took participants hours to answer hundreds of questions about themselves, and the resulting matches were only marginally better than a random walk in the supermarket of love. Online dating sites reacted as conventional markets might have done. New competitors assumed the problem was cognitive overload and decided that rather than more information, people wanted less. Much as traditional money-based markets have aimed to reduce the complexity of preference matching by headlining price (to give users a sense of comparability), more recent online dating platforms, like Tinder, narrowed the necessary interactions down to a single dimension—desirability.

res=9F0CE0DF153FEE3 BBC4E53DFB466838D629EDE. Prices ending in nines: Even if policy makers prohibit prices ending in nine, recent research has shown that the market adjusts quickly to the restriction and shifts from prices ending in ninety-nine to prices ending in ninety, with the same deceiving effect on consumers; see Avichai Snir, Daniel Levy, and Haipeng Chen, “End of 9-Endings, Price Recall, and Price Perceptions,” Economics Letters, forthcoming (posted April 2, 2017), https://ssrn.com/abstract=2944919. “under $1,000, which is code for $999”: Matthew Amster-Burton, “Price Anchoring, or Why a $499 iPad Seems Inexpensive,” Mint-Life, April 6, 2010, https://blog.mint.com/how-to/price-anchoring.

So, too, are browser plug-ins and apps such as InvisibleHand and PriceBlink, which can search in the background as you visit Amazon, Walmart, and other retailer sites and notify you if a lower price is available anywhere on the Web. They, too, focus on price, taking for granted that the less it costs to discover and compare prices, the lower the overall cost of transacting on the market; and everyone wins. PRICE-BASED MARKETS ARE THE ESTABLISHED ORTHODOXY. We are accustomed to them. They do the job. But condensing countless dimensions of information into a single figure hardly seems the right choice for an information age, for an era characterized by astonishing improvements in our ability to communicate and process lots of information. A system based on money and price solved a problem of too much information and not enough processing power, but in the process of distilling information down to price, many details get lost.


pages: 229 words: 75,606

Two and Twenty: How the Masters of Private Equity Always Win by Sachin Khajuria

"World Economic Forum" Davos, affirmative action, bank run, barriers to entry, Big Tech, blockchain, business cycle, buy and hold, carried interest, COVID-19, credit crunch, data science, decarbonisation, disintermediation, diversification, East Village, financial engineering, gig economy, glass ceiling, high net worth, hiring and firing, impact investing, index fund, junk bonds, Kickstarter, low interest rates, mass affluent, moral hazard, passive investing, race to the bottom, random walk, risk/return, rolodex, Rubik’s Cube, Silicon Valley, sovereign wealth fund, two and twenty, Vanguard fund, zero-sum game

In the chapters that follow, I will paint an insider’s picture of private equity, unvarnished, taking one brushstroke at a time. Let’s start with the secret sauce that private equity is selling. CHAPTER TWO We Don’t Sell Plain Vanilla What private equity sells sounds so good, some think it’s alchemy—the financial equivalent of turning water into wine. We always beat the market; we’re better than a random walk. We generate high returns over the long term and minimize the risk of losing money. We focus on the endgame, not the myopia of quarterly or annual results. We add value to your investments in a way that very few others can. We see opportunities others can’t see or can’t handle. Your money will be locked up with us, but in a few years, you will get a multiple of it back—without any effort from you.

It’s actually a great time for the Firm to capture the opportunity, even though the investment is in listed stock and debt rather than acquiring a business outright. This is a target the Firm knows. It’s a scenario that the partners feel can’t go wrong—at the right price—and that the investment professionals can model with reasonable confidence. Mutual funds, index funds, and ETFs are running out the door, sending the prices of these securities into free fall. Passive money is trying to escape. Meanwhile, the active managers in the room want to back up the truck and buy in bulk at a big discount. Why are the prices of these securities so distressed? The world is in chaos. Global demand for advertising on TV is plummeting because the multinationals are worried about the financial crisis causing a hit to their revenues.

The first idea is to negotiate with pharmaceutical industry giants to carve out unloved parts of their groups that contain older medicines, ex-blockbuster drugs, where a little investment in active marketing of the products would justify modest price increases—a few percent per year. The drugs are not monopolies—real competition from low-cost generics exists, but the brand names of these medicines are strong enough to retain a crucial portion of the customer base. Unloved assets sell for weak prices, and so industry giants would accept the price they get from the Firm. The corporate folks running the sales processes at the pharmaceuticals are likely to be more interested in getting a deal done than in optimizing the terms.


pages: 249 words: 77,342

The Behavioral Investor by Daniel Crosby

affirmative action, Asian financial crisis, asset allocation, availability heuristic, backtesting, bank run, behavioural economics, Black Monday: stock market crash in 1987, Black Swan, book value, buy and hold, cognitive dissonance, colonial rule, compound rate of return, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, disinformation, diversification, diversified portfolio, Donald Trump, Dunning–Kruger effect, endowment effect, equity risk premium, fake news, feminist movement, Flash crash, haute cuisine, hedonic treadmill, housing crisis, IKEA effect, impact investing, impulse control, index fund, Isaac Newton, Japanese asset price bubble, job automation, longitudinal study, loss aversion, market bubble, market fundamentalism, mental accounting, meta-analysis, Milgram experiment, moral panic, Murray Gell-Mann, Nate Silver, neurotypical, Nick Bostrom, passive investing, pattern recognition, Pepsi Challenge, Ponzi scheme, prediction markets, random walk, Reminiscences of a Stock Operator, Richard Feynman, Richard Thaler, risk tolerance, Robert Shiller, science of happiness, Shai Danziger, short selling, South Sea Bubble, Stanford prison experiment, Stephen Hawking, Steve Jobs, stocks for the long run, sunk-cost fallacy, systems thinking, TED Talk, Thales of Miletus, The Signal and the Noise by Nate Silver, Tragedy of the Commons, trolley problem, tulip mania, Vanguard fund, When a measure becomes a target

Swift, ‘The three faces of overconfidence in organizations,’ in David De Cremer, Rolf van Dick and J. K. Murnighan (eds.) Social Psychology and Organizations (Routledge, 2012). 62 J. Zweig, in Benjamin Graham, The Intelligent Investor (HarperBusiness, 2006), p. 374. 63 C. H. Browne, The Little Book of Value Investing (Wiley, 2006). 64 B. Malkiel, A Random Walk Down Wall Street (W. W. Norton & Company, 2016). 65 D. D. P. Johnson and J. H. Fowler, ‘The evolution of overconfidence,’ Nature (2011). 66 M. Muthukrishna, S. J. Heine, W. Toyakawa, T. Hamamura, T. Kameda and J. Henrich, ‘Overconfidence is universal? Depends what you mean’ (2015). 67 J.

Bubbles are born and die on fundamentals but are fueled by our need to create stories all along the way. The process typically looks something like this: Price gains occur for fundamental reasons. Increasing prices attract attention. Narratives emerge to explain price gains. The positive narrative begets a cascade of increased price and volume. Narrative is broken, causing a return to fundamentals. Robert Shiller defines a bubble as “a social epidemic where price increases lead to further price increases” and stories are the means by which a spark of fundamental value becomes a raging fire of irrationality. Teeter and Sandberg speak to the power of story to create and sustain bubbles in the aptly named, ‘Cracking the Enigma of Asset Bubbles with Narratives’ and cite three specific reasons why narrative is so powerful.

Empirically, there is now over a century’s worth of evidence data that value investing works. Lakonishok, Vishny and Shleifer examined the effect of price-to-book values on returns in, ‘Contrarian Investment, Extrapolation and Risk.’ They found that low price-to-book stocks (that is, value stocks) outperformed the high price-to-book glamour stocks 73% of the time over one-year periods, 90% of the time over three-year periods and 100% of the time over five-year periods. In ‘Decile Portfolios of the NYSE, 1967–1985,’ Yale Professor Roger Ibbotson ranked stocks by deciles according to price-to-earnings ratios and measured their performance from 1967 to 1985. Ibbotson found that the stocks in the cheapest decile outperformed those in the most expensive decile by over 600% and the “average” decile by over 200% over that time period.


pages: 252 words: 71,176

Strength in Numbers: How Polls Work and Why We Need Them by G. Elliott Morris

affirmative action, call centre, Cambridge Analytica, commoditize, coronavirus, COVID-19, critical race theory, data science, Donald Trump, Francisco Pizarro, green new deal, lockdown, Moneyball by Michael Lewis explains big data, Nate Silver, random walk, Ronald Reagan, selection bias, Silicon Valley, Socratic dialogue, statistical model, Works Progress Administration

Algorithmic news feeds at social media companies, especially Facebook, had created a new method by which people read news: instead of turning to aggregation websites, they just read what their friends shared. The Huffington Post’s ad revenue fell precipitously. In 2011, AOL had bought the outlet for $315 million; by 2019, it was laying off staff and AOL was looking for a buyer. A RANDOM WALK DOWN MAIN STREET The rise of polling aggregation websites such as Pollster.com created a better way for journalists to cover political polling. A good model, like Charles Franklin’s, is necessary to communicate the findings from an array of polls. An election forecast can be thought of as a similar iteration: whereas aggregators answer the question of who is leading in the polls today, forecasters tell you how likely that is to change by Election Day.

While pictures of Iraqi mothers holding starving children scrolled across the screen, Stahl cited a shocking figure from a new academic study: according to surveys out of Iraq, the sanctions that the UN had put in place following Iraq’s invasion of Kuwait had killed roughly 500,000 Iraqi children. “We have heard that a half million children have died,” Stahl said. “I mean, that’s more children than died in Hiroshima. And, you know, is the price worth it?” Albright waffled over an answer. As the camera panned to her, she said, “I think this is a very hard choice, but the price—we think the price is worth it.” Stahl won an Emmy for the segment, a highlight of her career. But while Albright’s answer didn’t cause any immediate shock waves to ripple through the media (there are some accounts that college students later protested her visits because of the segment), it would turn out to be one of the more embarrassing mistakes of her career.

Rather, it suggests a broader theoretical framework of how leaders make their decisions. In a 1997 Public Opinion Quarterly article titled “Opinion Quality in Public Opinion Research,” Vincent Price and Peter Neijens, two professors of communication and scholars of polling, wrote of the “decision matrix” that can help make sense of how many actors, relying on different sources of information, can come to a collective decision. Price and Neijens formulate an exercise including six groups of actors all participating in a collective decision-making process: political leaders, technical experts, interest groups, newspapers, “attentive publics,” and the masses.


pages: 240 words: 73,209

The Education of a Value Investor: My Transformative Quest for Wealth, Wisdom, and Enlightenment by Guy Spier

Albert Einstein, Atul Gawande, Bear Stearns, Benoit Mandelbrot, big-box store, Black Swan, book value, Checklist Manifesto, classic study, Clayton Christensen, Daniel Kahneman / Amos Tversky, Exxon Valdez, Gordon Gekko, housing crisis, information asymmetry, Isaac Newton, Kenneth Arrow, Long Term Capital Management, Mahatma Gandhi, mandelbrot fractal, mirror neurons, Nelson Mandela, NetJets, pattern recognition, pre–internet, random walk, Reminiscences of a Stock Operator, risk free rate, Ronald Reagan, South Sea Bubble, Steve Jobs, Stuart Kauffman, TED Talk, two and twenty, winner-take-all economy, young professional, zero-sum game

During my seven-year stint in Jungian therapy, I found Edward Whitmont’s The Symbolic Quest: Basic Concepts of Analytical Psychology a very useful handbook. My first explorations into the power of emotion came from reading Diana Fosha’s The Transforming Power of Affect: A Model for Accelerated Change, which then led me to works by Allan Schore, Antonio Damasio, Joseph LeDoux, and others, some of which I’ve listed below. A Random Walk through My Library What follows is a brief list of additional books that I’ve found intriguing and enriching for countless reasons. Are they relevant to your education as an investor? Some yes. Some not so much. But I’ve found all of these books richly rewarding. They are filled with wisdom not just on stock-picking but on everything from ants to anarchy, finance to love.

But if I were managing solely my own account, I’d set up a system in which I’d look at the price of my holdings only once a quarter, or possibly even once a year. As things stand, I check the price of my holdings no more than once a week. It’s a wonderful release to see that your portfolio does just fine when you don’t check it. For good measure, I don’t have my computer or Bloomberg monitor set up to show me the price of all my holdings on one screen; if I need to check the price of a stock, I do it individually so that I won’t see the price of all my other stocks at the same time. I don’t want to see these other prices unnecessarily and to subject myself to this barrage of calls to action.

The goal in these situations is to find companies where one aspect of the value chain has gone awry, dragging down the whole business. If I believe this problem is temporary, I can buy the stock at a beaten-down price and then benefit once this issue within the value chain is resolved. In 2007 this thought process led me to invest in Alaska Milk, the dominant producer of condensed milk in the Philippines. The company’s key ingredient was powdered milk that had to be imported from abroad. When the global price of powdered milk shot up, the company’s profit margins were squeezed and its stock plunged. I was convinced that the price of powdered milk would eventually return to normal as supply rose to match increased demand from China.


pages: 270 words: 79,068

The Hard Thing About Hard Things: Building a Business When There Are No Easy Answers by Ben Horowitz

Airbnb, Ben Horowitz, Benchmark Capital, business intelligence, cloud computing, financial independence, Google Glasses, hiring and firing, Isaac Newton, Jeff Bezos, Kiva Systems, Larry Ellison, Marc Andreessen, Mark Zuckerberg, move fast and break things, new economy, nuclear winter, Peter Thiel, Productivity paradox, random walk, Ronald Reagan, Silicon Valley, six sigma, SoftBank, Steve Ballmer, Steve Jobs, stock buybacks, Strategic Defense Initiative

Do you make it to the moon? To Jupiter? Do you just get lost in space? There were lots of companies in the ’90s that had launch parties but no landing parties. “But the indeterminate future is somehow one in which probability and statistics are the dominant modality for making sense of the world. Bell curves and random walks define what the future is going to look like. The standard pedagogical argument is that high schools should get rid of calculus and replace it with statistics, which is really important and actually useful. There has been a powerful shift toward the idea that statistical ways of thinking are going to drive the future.”

Listen to how Robin owned delivering results: In 2004, we raised our last round of VC money led by Draper Fisher Jurvetson . . . and Google, one of our great colleagues. Then a year later, in 2005, the company went public. The ideal price was $27 [the stock’s initial offer price] and it closed on the first day at $122. It was great for many of the Baidu employees and for all of the Baidu investors. It was a very miserable thing for me because when I decided to take the company public, I was only prepared to deliver financial results that match the price of $27 or maybe a little higher, $30, $40. But I was really shocked to see that the price went to $122 on the first day. So that meant I needed to deliver real results that matched an expectation much, much higher than what I had prepared to do.

I decided to bring my direct reports into the loop and ask them what they thought. The answers came back clear: Everyone, with the exception of one person who felt that the opportunity in front of us was still quite large, opted for the sale. Now it was just a matter of price. But what price? After a long discussion with John O’Farrell, I decided that the right price to sell the company would be $14 per share, or about $1.6 billion. I took that number back to the board. They thought the number was extremely high and that it was unlikely we’d be able to generate a bid at that level, but they were supportive nonetheless.


pages: 203 words: 63,257

Neutrino Hunters: The Thrilling Chase for a Ghostly Particle to Unlock the Secrets of the Universe by Ray Jayawardhana

Albert Einstein, Alfred Russel Wallace, anti-communist, Arthur Eddington, cosmic microwave background, dark matter, Eddington experiment, Ernest Rutherford, Higgs boson, invention of the telescope, Isaac Newton, it's over 9,000, Johannes Kepler, Large Hadron Collider, Magellanic Cloud, New Journalism, race to the bottom, random walk, Richard Feynman, Schrödinger's Cat, seminal paper, Skype, South China Sea, Stephen Hawking, time dilation, undersea cable, uranium enrichment

The neutron, on the other hand, would zigzag through the fluid, like a drunkard staggering through a crowd, losing energy as it collided with one nucleus and then another, until it was eventually absorbed. The nucleus that captured the neutron would emit the excess energy as gamma rays. As Reines and Cowan were aware, there is a characteristic time for the neutron’s random walk: 5 microseconds. That means there is a precise delay between the two gamma ray bursts, the first one from the positron’s annihilation and the second from the neutron’s absorption. If their experiment recorded two flashes coming precisely 5 microseconds apart, it would constitute the unmistakable signal of a neutrino.

They had expected the National Science Foundation and the Department of Energy (DOE) to share the nearly $2 billion cost of building it. But both institutions have declined to proceed with the original plan, because the price tag was deemed too high at a time of tight budgets. “That’s disappointing, but not shocking,” says Kayser. “When the NSF pulled out, the DOE couldn’t afford it alone. But the DOE is keen to find some way to make it happen.” In fact, in December 2012 the DOE gave preliminary approval to build a bare-bones version of the experiment, at half the original price tag, with a smaller detector that sits on the surface instead of underground. Meanwhile, researchers in other countries are not sitting idle.

Formulated in the early 1970s, the standard model incorporates two dozen elementary particles of matter and their antimatter twins, three types of interactions among them, and the symmetries that govern those interactions. It is the best description of the subatomic world that we have, and countless experiments over three decades have verified its predictions with exquisite precision. The fabled Large Hadron Collider at CERN, the most powerful and expensive atom smasher ever, was constructed at a jaw-dropping price tag of roughly $9 billion in large part to nail down the final missing piece of the theory. The LHC confirmed the existence of the Higgs boson, a particle hypothesized to be responsible for endowing other elementary particles with mass. The standard model, however, presumed that neutrinos have no mass, come in three flavors, and cannot change form.


pages: 611 words: 188,732

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom) by Adam Fisher

adjacent possible, Airbnb, Albert Einstein, AltaVista, An Inconvenient Truth, Andy Rubin, AOL-Time Warner, Apple II, Apple Newton, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, Bill Atkinson, Bob Noyce, Brownian motion, Buckminster Fuller, Burning Man, Byte Shop, circular economy, cognitive dissonance, Colossal Cave Adventure, Computer Lib, disintermediation, Do you want to sell sugared water for the rest of your life?, don't be evil, Donald Trump, Douglas Engelbart, driverless car, dual-use technology, Dynabook, Elon Musk, Fairchild Semiconductor, fake it until you make it, fake news, frictionless, General Magic , glass ceiling, Hacker Conference 1984, Hacker Ethic, Henry Singleton, Howard Rheingold, HyperCard, hypertext link, index card, informal economy, information retrieval, Ivan Sutherland, Jaron Lanier, Jeff Bezos, Jeff Rulifson, John Markoff, John Perry Barlow, Jony Ive, Kevin Kelly, Kickstarter, knowledge worker, Larry Ellison, life extension, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Maui Hawaii, Menlo Park, Metcalfe’s law, Mondo 2000, Mother of all demos, move fast and break things, Neal Stephenson, Network effects, new economy, nuclear winter, off-the-grid, PageRank, Paul Buchheit, paypal mafia, peer-to-peer, Peter Thiel, pets.com, pez dispenser, popular electronics, quantum entanglement, random walk, reality distortion field, risk tolerance, Robert Metcalfe, rolodex, Salesforce, self-driving car, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, skunkworks, Skype, Snow Crash, social graph, social web, South of Market, San Francisco, Startup school, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Stewart Brand, Susan Wojcicki, synthetic biology, Ted Nelson, telerobotics, The future is already here, The Hackers Conference, the long tail, the new new thing, Tim Cook: Apple, Tony Fadell, tulip mania, V2 rocket, We are as Gods, Whole Earth Catalog, Whole Earth Review, Y Combinator

At Stanford you had to do something fundamentally new, so you couldn’t do something that’s already done, because that’s not cool, right? John Markoff: There were so many search engines at the time. They were all over the place. Building the crawler and downloading the web was not Google’s breakthrough. The breakthrough was PageRank. Terry Winograd: I can remember Larry talking about a random walk on the web. “Random surf,” he called it. So, you’re on the web at some page and it’s got a bunch of links. So you pick one of them at random and you go there. And then you do this again and again with a zillion bots. So, if everybody’s doing this, where would you end up most of the time? The point is if lots of people point to me, you’re going to end up with me more often.

Then if I point to you, you’re going to get a lot of them even if there’s only one link from me to you: You’re going to get a lot because I’m getting a lot. So think of this traffic moving through this network, just statistically. Who would get the most traffic? Scott Hassan: Larry came up with the idea of doing random walk, but Larry didn’t know how to compute it. Sergey looked at it and said, “Oh, that looks like computing the eigenvector of a matrix!” Sergey Brin: Basically we convert the entire web into a big equation, with several hundred million variables, which are the page ranks of all the web pages and billions of terms, which are the links.

Steve Jarrett: We’d already sold a significant amount of stock to these large partner companies, and we had a bunch of licensing revenue coming in, so the company looked superstrong on paper. And so when we went public in 1995, it was one of the first internet IPOs in the sense that we had a particular price and then we opened way, way above the S1 price. Marc Porat: We were priced at $14, opened at $32, and took in tons of money. So the IPO as an event was very successful. But then the Magic Link sales were not good, and we said to ourselves, “Oh man, this is not going to go well.” Andy Hertzfeld: We were hoping to sell a hundred thousand of the first Sony devices, but they only sold like fifteen thousand.


pages: 192 words: 75,440

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, Bear Stearns, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, deal flow, discounted cash flows, family office, financial engineering, fixed income, global macro, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, managed futures, merger arbitrage, offshore financial centre, proprietary trading, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stock buybacks, stocks for the long run, systematic trading, two and twenty, unpaid internship, value at risk, yield curve, yield management

Lowenstein, Roger. When Genius Failed: The Rise and Fall of Long Term Capital Management. New York: Random House Trade Paperbacks, 2000. Lynch, Peter. Beating the Street, New York: Fireside, 1994. Lynch, Peter and Rothschild, John. Learn to Earn. New York: John Wiley & Sons, Inc., 1996. Malkiel, Burton G. A Random Walk Down Wall Street. New York: W.W. Norton & Company, 2003. Nicholas, Joseph. Investing in Hedge Funds: Strategies for the New Marketplace. New York: Bloomberg Press, 2005. Siegel, Jeremy J. Stocks for the Long Run. New York: McGraw-Hill, 2002. Soros, George. The Alchemy of Finance. Hoboken, N.J: John Wiley & Sons, Inc., 1987.

Note: The candidate whose picks are reflected in the table was an analyst at a long/short equity fund (the grayed sections were recommendations that did not make it into the portfolio). c05.indd 60 1/10/08 11:06:17 AM Getting In Later in Your Career 61 Table 5.1 Candidate 1—Self-Generated Investment Ideas AVG. REC. PRICE DIV. CURRENT UNREALIZED YIELD PRICE % GAIN AVG. EXIT PRICE REALIZED % GAIN STOCK INVESTMENT PUT IN TYPE PORTFOLIO Company A Long Yes $46.00 $73.50 59.8% $61.00 33% Company B Long Yes $11.50 $28.00 143.5% $17.50 52% Company C Long Yes $20.85 $24.00 27.1% Company D Long Yes $32.25 $43.35 34.4% Company E Long Yes $8.50 $11.50 35.3% $11.50 35% Company F Long Yes $13.86 $18.20 31.3% Company G Long Yes $11.75 $16.40 39.6% $13.75 17% Company H Long Yes $14.00 $20.32 45.1% $17.00 21% Company I Long Yes $17.50 $16.89 ⫺3.5% $16.50 ⫺6% Company J Long Yes $19.50 $21.50 10.3% $16.50 ⫺15% Company K Long Yes $12.00 $13.23 10.3% Company L Long No $56.00 $65.32 16.6% Company M Long No $11.00 $12.92 33.5% Company N Long No $9.60 $14.00 45.8% Company O Long No $24.00 $34.80 45.0% Company P Long No $8.25 $16.75 103.0% Company Q Long No $21.50 $27.44 27.6% Company R Long No $23.50 $32.59 38.7% Company S Long No $20.50 $28.00 36.6% Company T Short Yes $15.75 $6.10 61.3% $10.50 33.3% Company U Short Yes $12.00 $5.05 57.9% $9.00 25.0% Company V Short Yes $8.50 $5.39 36.6% $6.00 29.4% Company W Short Yes $5.25 $2.30 56.2% $4.00 23.8% Company X Short Yes $11.41 $14.50 Company Y Short Yes $7.00 $5.70 18.6% $5.25 25.0% Company Z Short Yes $45.00 $41.00 8.9% $41.50 7.8% Company AA Short Yes $19.50 $22.45 ⫺15.1% Company BB Short Yes $40.00 $47.00 ⫺17.5% Company CC Short Yes $17.50 100.0% $15.00 14.3% Company DD Short Yes $20.50 64.1% $17.00 17.1% 12% 16% $7.35 ⫺27.1% (Continued) c05.indd 61 1/10/08 11:06:18 AM 62 Getting a Job in Hedge Funds Table 5.1 (Continued) Returns Analysis—Assuming Equal Weighting Hit Rate Analysis Up Down % Hits Unrealized Total Picks 37.2% All Ideas—Realized/Unrealized 25 6 80.6% Unrealized Total Longs 40.5% All Longs—Realized/Unrealized 17 2 89.5% Unrealized Total Shorts 31.3% All Shorts—Realized/Unrealized 8 4 66.7% Total Portfolio Returns—Candidate 1 Picksa Portfolio Position Longs 22.6% Portfolio Position Shorts 16.8% Total Portfolio Positions 19.7% Total Picks Candidate 1—Realized ⫹ Unrealizedb Longs 30.9% Shorts 16.8% Total 25.6% Total Picks—Not Included in Portfolio 43.4% Source: Glocap Search LLC. a Includes only self-generated ideas that were included in the portfolio (included positions at exit prices).

The securities of the target companies are often out of favor or under-followed by the Wall Street research community, but are believed by these funds to be selling at deep discounts to what they believe is their potential worth. Hedge funds that employ this style may simultaneously purchase stock in companies being acquired and sell stock of the acquiring company, hoping to profit from the spread between the current market price and the ultimate purchase price of the company. These funds may also utilize derivatives to leverage returns and to hedge out interest rate and/or market risk. Because they invest in special situations, the performance of these funds is typically not dependent on the direction of the public stock market. Note: This is primarily an equity-based style.


pages: 1,336 words: 415,037

The Snowball: Warren Buffett and the Business of Life by Alice Schroeder

affirmative action, Alan Greenspan, Albert Einstein, anti-communist, AOL-Time Warner, Ayatollah Khomeini, barriers to entry, Bear Stearns, Black Monday: stock market crash in 1987, Bob Noyce, Bonfire of the Vanities, book value, Brownian motion, capital asset pricing model, card file, centralized clearinghouse, Charles Lindbergh, collateralized debt obligation, computerized trading, Cornelius Vanderbilt, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, desegregation, do what you love, Donald Trump, Eugene Fama: efficient market hypothesis, Everybody Ought to Be Rich, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, financial engineering, Ford Model T, Garrett Hardin, Glass-Steagall Act, global village, Golden Gate Park, Greenspan put, Haight Ashbury, haute cuisine, Honoré de Balzac, If something cannot go on forever, it will stop - Herbert Stein's Law, In Cold Blood by Truman Capote, index fund, indoor plumbing, intangible asset, interest rate swap, invisible hand, Isaac Newton, it's over 9,000, Jeff Bezos, John Bogle, John Meriwether, joint-stock company, joint-stock limited liability company, junk bonds, Larry Ellison, Long Term Capital Management, Louis Bachelier, low interest rates, margin call, market bubble, Marshall McLuhan, medical malpractice, merger arbitrage, Michael Milken, Mikhail Gorbachev, military-industrial complex, money market fund, moral hazard, NetJets, new economy, New Journalism, North Sea oil, paper trading, passive investing, Paul Samuelson, pets.com, Plato's cave, plutocrats, Ponzi scheme, proprietary trading, Ralph Nader, random walk, Ronald Reagan, Salesforce, Scientific racism, shareholder value, short selling, side project, Silicon Valley, Steve Ballmer, Steve Jobs, supply-chain management, telemarketer, The Predators' Ball, The Wealth of Nations by Adam Smith, Thomas Malthus, tontine, too big to fail, Tragedy of the Commons, transcontinental railway, two and twenty, Upton Sinclair, War on Poverty, Works Progress Administration, Y2K, yellow journalism, zero-coupon bond

These academics had started by positing the reasonable but not necessarily obvious truth that if a whole lot of people were trying to be better than average, they would become the average. Paul Samuelson, an MIT economist, revived and circulated the 1900 work of Louis Bachelier, who observed that the market is made up of speculators who cohere into a whole that operates according to a “random walk.”38 A professor from the University of Chicago, Eugene Fama, took Bachelier’s work and tested it empirically in the modern-day market, which he described as “efficient.” The scrabbling efforts of legions of investors to beat the market made those very efforts futile, he said. Yet an army of professionals had sprung up who charged everything from modest fees to the soon-to-be-legendary hedge-fund cut of “two-and-twenty”(two percent of assets and twenty percent of returns) for the privilege of managing an investor’s money and trying to predict the future behavior of stocks.

All of them, he said, came from the village of Graham-and-Doddsville, had been flipping straight heads for more than twenty years, and for the most part had not retired and were still doing it. Such a concentration proved statistically that their success could not have come by random luck. Since what Buffett said was obviously true on its face, the audience broke into applause and lobbed questions at him, which he answered gladly and at length. The random-walk theory was based on statistics and Greek-letter formulas. The existence of people like Buffett had been waved away using bafflemath. Now, to the Grahamites’ relief, Buffett had used numbers to disprove the absolutist version of the efficient-market hypothesis. That fall, he wrote up “The Superinvestors of Graham-and-Doddsville” as an article for Hermes, the magazine of the Columbia Business School.

Charles Ellis, Investment Policy: How to Win the Loser’s Game. Illinois: Dow-Jones-Irwin, 1985, which is based on his article “Winning the Loser’s Game” in the July/August 1975 issue of the Financial Analysts Journal. 40. The modern-day equivalents of Tweedy Browne’s Jamaica Water warrants still exist, for example. 41. Burton Malkiel, A Random Walk Down Wall Street. New York: W. W. Norton, 1973. 42. Aside from the Superinvestors article, Buffett did not write about EMH directly until the Berkshire 1987 shareholders letter, but he had led up to it with related subjects such as excessive trading turnover since 1979. 43. Transcript of Graham and Dodd 50th Anniversary Seminar.


pages: 254 words: 76,064

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

After a while, Joi received word that the project had been abandoned because after the interviews everyone had such a different view of what the Media Lab did and how it did it that the researchers couldn’t actually map it. Trying to understand the Lab in some sort of structure is sort of futile. Like a random walk in a vibrant natural ecosystem with a random group of people, some people will see how the geology is working, others will note the way the plants are working together, others will focus on the microbial flora, and still others will focus on the rich culture of the people who live there. Everyone in the Media Lab is, metaphorically speaking, running his or her own algorithms, and they interact with each other and various internal and external systems.

Later, when Jeff’s company is a huge success, goes public, and is on the front page of the New York Times, it might be time for me to sell. Everyone, including my Japanese government fund manager friend, will be saying, “Gee, what an amazing company! How could anything go wrong now?” The price will be surging. We often say, “The information is in the price.” The company may be in better shape than when I first invested, but people may be underestimating the risks and overestimating the opportunity. The stock could be overpriced. In other words, use the information that you have to understand risk, take risk, but buy low, sell high.

In the good, old, slow days planning—of almost any endeavor, but certainly one that required capital investment—was an essential step in avoiding a failure that might bring on financial woe and social stigma. In the network era however, well-led companies have embraced, even encouraged failure. Now, launching anything from a new line of shoes to your own consulting practice has dropped dramatically in price, and businesses commonly regard “failure” as a bargain-priced learning opportunity. While that may sound frightening, it can be an incredibly powerful tool. When you emphasize practice over theory, you don’t need to wait for permission, or explain yourself before you begin. And once you’ve started, if your circumstances change or your development process takes an unexpected turn, you don’t always need to stop and figure out what happened before you go on.


pages: 829 words: 186,976

The Signal and the Noise: Why So Many Predictions Fail-But Some Don't by Nate Silver

airport security, Alan Greenspan, Alvin Toffler, An Inconvenient Truth, availability heuristic, Bayesian statistics, Bear Stearns, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, big-box store, Black Monday: stock market crash in 1987, Black Swan, Boeing 747, book value, Broken windows theory, business cycle, buy and hold, Carmen Reinhart, Charles Babbage, classic study, Claude Shannon: information theory, Climategate, Climatic Research Unit, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, complexity theory, computer age, correlation does not imply causation, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, Daniel Kahneman / Amos Tversky, disinformation, diversification, Donald Trump, Edmond Halley, Edward Lorenz: Chaos theory, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, everywhere but in the productivity statistics, fear of failure, Fellow of the Royal Society, Ford Model T, Freestyle chess, fudge factor, Future Shock, George Akerlof, global pandemic, Goodhart's law, haute cuisine, Henri Poincaré, high batting average, housing crisis, income per capita, index fund, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, invention of the printing press, invisible hand, Isaac Newton, James Watt: steam engine, Japanese asset price bubble, John Bogle, John Nash: game theory, John von Neumann, Kenneth Rogoff, knowledge economy, Laplace demon, locking in a profit, Loma Prieta earthquake, market bubble, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, Monroe Doctrine, mortgage debt, Nate Silver, negative equity, new economy, Norbert Wiener, Oklahoma City bombing, PageRank, pattern recognition, pets.com, Phillips curve, Pierre-Simon Laplace, Plato's cave, power law, prediction markets, Productivity paradox, proprietary trading, public intellectual, random walk, Richard Thaler, Robert Shiller, Robert Solow, Rodney Brooks, Ronald Reagan, Saturday Night Live, savings glut, security theater, short selling, SimCity, Skype, statistical model, Steven Pinker, The Great Moderation, The Market for Lemons, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Timothy McVeigh, too big to fail, transaction costs, transfer pricing, University of East Anglia, Watson beat the top human players on Jeopardy!, Wayback Machine, wikimedia commons

(The answer is in the endnotes.30) Investors were looking at stock-price movements like these and were mistaking noise for a signal. FIGURE 11-4: RANDOM-WALK AND ACTUAL STOCK-MARKET CHARTS Three Forms of Efficient-Market Hypothesis After looking at enough of this type of data, Fama refined his hypothesis to cover three distinct cases,31 each one making a progressively bolder claim about the predictability of markets. First, there is the weak form of efficient-market hypothesis. What this claims is that stock-market prices cannot be predicted from analyzing past statistical patterns alone.

., 5, 149 by foxes, see foxes of future returns of stocks, 330–31, 332–33 of global warming, 373–76, 393, 397–99, 401–6, 402, 507 in Google searches, 290–91 by hedgehogs, see hedgehogs human ingenuity and, 292 of Hurricane Katrina, 108–10, 140–41, 388 as hypothesis-testing, 266–67 by IPCC, 373–76, 389, 393, 397–99, 397, 399, 401, 507 in Julius Caesar, 5 lack of demand for accuracy in, 202, 203 long-term progress vs. short-term regress and, 8, 12 Pareto principle of, 312–13, 314 perception and, 453–54, 453 in poker, 297–99, 311–15 probability and, 243 quantifying uncertainty of, 73 results-oriented thinking and, 326–28 scientific progress and, 243 self-canceling, 219–20, 228 self-fulfilling, 216–19, 353 as solutions to problems, 14–16 as thought experiments, 488 as type of information-processing, 266 of weather, see weather forecasting prediction, failures of: in baseball, 75, 101–5 of CDO defaults, 20–21, 22 context ignored in, 43 of earthquakes, 7, 11, 143, 147–49, 158–61, 168–71, 174, 249, 346, 389 in economics, 11, 14, 40–42, 41, 45, 53, 162, 179–84, 182, 198, 200–201, 249, 388, 477, 479 financial crisis as, 11, 16, 20, 30–36, 39–42 of floods, 177–79 of flu, 209–31 of global cooling, 399–400 housing bubble as, 22–23, 24, 25–26, 28–29, 32–33, 42, 45 overconfidence and, 179–83, 191, 203, 368, 443 overfitting and, 185 on politics, 11, 14–15, 47–50, 49, 53, 55–59, 64, 67–68, 157, 162, 183, 249, 314 as rational, 197–99, 200 recessions, 11 September 11, 11 in stock market, 337–38, 342, 343–46, 359, 364–66 suicide bombings and, 424 by television pundits, 11, 47–50, 49, 55 Tetlock’s study of, 11, 51, 52–53, 56–57, 64, 157, 183, 443, 452 of weather, 21–22, 114–18 prediction interval, 181-183, 193 see also margin of error prediction markets, 201–3, 332–33 press, free, 5–6 Price, Richard, 241–42, 490 price discovery, 497 Price Is Right, 362 Principles of Forecasting (Armstrong), 380 printing press, 1–4, 6, 13, 17, 250, 447 prior probability, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 497 probability, 15, 61–64, 63, 180, 180, 181 calibration and, 134–36, 135, 136, 474 conditional, 240, 300; see also Bayes’s theorem frequentism, 252 and orbit of planets, 243 in poker, 289, 291, 297, 302–4, 302, 306, 307, 322–23 posterior, 244 predictions and, 243 prior, 244, 245, 246, 252, 255, 258–59, 260, 403, 406–7, 433n, 444, 451, 490, 498 rationality and, 242 as waypoint between ignorance and knowledge, 243 weather forecasts and, 195 probability distribution, of GDP growth, 201 probability theory, 113n productivity paradox, 7–8 “Programming a Computer for Playing Chess” (Shannon), 265–66 progress, forecasting and, 1, 4, 5, 7, 112, 243, 406, 410–11, 447 prospect theory, 64 Protestant Reformation, 4 Protestant work ethic, 5 Protestants, worldliness of, 5 psychology, 183 Public Opinion Quarterly, 334 PURPLE, 413 qualitative information, 100 quantitative information, 72–73, 100 Quantum Fund, 356 quantum mechanics, 113–14 Quebec, 52 R0 (basic reproduction number), 214–15, 215, 224, 225, 486 radar, 413 radon, 143, 145 rain, 134–37, 473, 474 RAND database, 511 random walks, 341 Rapoport, David C., 428 Rasskin-Gutman, Diego, 269 ratings agencies, 463 CDOs misrated by, 20–21, 21, 22, 26–30, 36, 42, 43, 45 housing bubble missed by, 22–23, 24, 25–26, 28–29, 42, 45, 327 models of, 13, 22, 26, 27, 29, 42, 45, 68 profits of, 24–25 see also specific agencies rationality, 183–84 biases as, 197–99, 200 of markets, 356–57 as probabilistic, 242 Reagan, Ronald, 50, 68, 160, 433, 466 RealClimate.org, 390, 409 real disposable income per capita, 67 recessions, 42 double dip, 196 failed predictions of, 177, 187, 194 in Great Moderation, 190 inflation-driven, 191 of 1990, 187, 191 since World War II, 185 of 2000-1, 187, 191 of 2007-9, see Great Recession rec.sport.baseball, 78 Red Cross, 158 Red River of the North, 177–79 regression analysis, 100, 401, 402, 498, 508 regulation, 13, 369 Reinhart, Carmen, 39–40, 43 religion, 13 Industrial Revolution and, 6 religious extremism, 428 religious wars of sixteenth and seventeenth centuries, 2, 6 Remote Sensing Systems, 394 Reno, Nev., 156–57, 157, 477 reserve clause, 471 resolution, as measure of forecasts, 474 results-oriented thinking, 326–28 revising predictions, see Bayesian reasoning Ricciardi, J.

On a particular Friday, MGM starts out priced at $100, so you expect it to rise to $110 by the end of the trading day. What should you do? You should buy the stock, of course, expecting to make a quick profit. But when you buy the stock, its price goes up. A large enough trade49 might send the price up to $102 from $100. There’s still some profit there, so you buy the stock again and its price rises to $104. You keep doing this until the stock reaches its fair price of $110 and there are no more profits left. But look what happened: in the act of detecting this pricing anomaly, you have managed to eliminate it.


pages: 713 words: 93,944

Seven Databases in Seven Weeks: A Guide to Modern Databases and the NoSQL Movement by Eric Redmond, Jim Wilson, Jim R. Wilson

AGPL, Amazon Web Services, business logic, create, read, update, delete, data is the new oil, database schema, Debian, domain-specific language, en.wikipedia.org, fault tolerance, full text search, general-purpose programming language, Kickstarter, Large Hadron Collider, linked data, MVC pattern, natural language processing, node package manager, random walk, recommendation engine, Ruby on Rails, seminal paper, Skype, social graph, sparse data, web application

it.object.equals(bacon)​​ ​​}.filter{it.equals(bacon)}.paths >> 1).name.grep{it}​​ ​​==>Elvis Presley​​ ​​==>Double Trouble​​ ​​==>Roddy McDowall​​ ​​==>The Big Picture​​ ​​==>Kevin Bacon​​ We didn’t know who Roddy McDowall was, but that’s the beauty of our graph database. We didn’t have to know to get a good answer. Feel free to sharpen your Groovy-foo if you want the output to be fancier than our simple list, but the data is all there. Random Walk When looking for good sample from a large data set, a useful trick is the “random walk.” You start with a random number generator. ​​rand = new Random()​​ Then you filter out some target ratio of the total. If we want to return only about one-third of Kevin Bacon’s ~60 movies, we could filter out any random number less than 0.33. ​​bacon.outE.filter{rand.nextDouble() <= 0.33}.inV.name​​ The count should be somewhere around twenty random titles from the Bacon canon.

You define static up front, while dynamic will accept values you may not have intended, even nonsensical types like person_name = 5. Documents are schemaless, so Mongo has no way of knowing if you intended on inserting pipulation into your city or meant to querying on lust_census; it will happily insert those fields or return no matching values. Flexibility has its price. Caveat emptor. References As we mentioned previously, Mongo isn’t built to perform joins. Because of its distributed nature, joins are pretty inefficient operations. Still, it’s sometimes useful for documents to reference each other. In these cases, the Mongo development team suggests you use a construct like { $ref : "collection_name", $id : "reference_id" }.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, behavioural economics, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, data science, David Heinemeier Hansson, deep learning, disruptive innovation, driverless car, Dunbar number, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, gamification, Google X / Alphabet X, haute couture, helicopter parent, hype cycle, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Mary Meeker, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, planned obsolescence, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, scientific management, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, subscription business, survivorship bias, The Home Computer Revolution, the long tail, too big to fail, US Airways Flight 1549, vertical integration, web application, zero-sum game

Inflation, ironically, is a measure that inflates the truth about prices. And the truth is that all prices are going down in real terms. Consumer price index trickery Wage growth has outstripped price growth or CPI in every developed nation since World War II.3 When we overlay the growth in wages each year with the growth in prices, the pattern is clear: income growth outstrips price growth, so life is cheaper. People confuse their desired living standards with the actual cost of living. The confusing part is that consumer price indexes don’t actually measure prices because they don’t measure prices in real terms relative to the increase we see in incomes every year.

Books The Art of Game Design: A Book of lenses Jesse Schell Billions & Billions: Thoughts on Life and Death at the Brink of the Millennium Carl Sagan Brand Hijack: Marketing Without Marketing Alex Wipperfurth The Cluetrain Manifesto Rick Levine, Christopher Locke, Doc Searls, David Weinberger The Demon-Haunted World: Science as a Candle in the Dark Carl Sagan and Ann Druyan How to Create a Mind: The Secret of Human Thought Revealed Ray Kurzweil The Intelligent Investor: The Definitive Book on Value Investing. A Book of Practical Counsel Benjamin Graham One Up On Wall Street: How To Use What You Already Know To Make Money In The Market Peter Lynch The Prince Niccolo Machiavelli A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing Burton G. Malkiel Rework Jason Fried, David Heinemeier Hansson The Road Ahead Bill Gates, Nathan Myhrvold, Peter Rinearson Stuff White People Like: A Definitive Guide to the Unique Taste of Millions Christian Lander Documentaries Connections (series 1-3, 1978-1997) Series presented by James Burke.

We used to have to rely on advertising messages, shopping around and calling up buyers to compare prices. Now we’re a few keystrokes away from knowing where we can get the best price on anything without error. Price comparison sites, or even barcode scanning software, puts perfect pricing knowledge in the hands of us all. This changes things a lot. It puts downward pressure on the seller’s prices and margins. We all now have access to global shopping hubs, the best and cheapest in every category, ensuring we pay the price of the most efficient global operator who sells a common good. But it’s not just knowing where the cheapest price is that will create a further downward pressure; it’s access to lower cost forms of production for all types of work.


pages: 260 words: 77,007

Are You Smart Enough to Work at Google?: Trick Questions, Zen-Like Riddles, Insanely Difficult Puzzles, and Other Devious Interviewing Techniques You ... Know to Get a Job Anywhere in the New Economy by William Poundstone

affirmative action, Albert Einstein, big-box store, Buckminster Fuller, car-free, cloud computing, creative destruction, digital rights, en.wikipedia.org, full text search, hiring and firing, How many piano tuners are there in Chicago?, index card, Isaac Newton, Johannes Kepler, John von Neumann, lateral thinking, loss aversion, mental accounting, Monty Hall problem, new economy, off-the-grid, Paul Erdős, RAND corporation, random walk, Richard Feynman, rolodex, Rubik’s Cube, Silicon Valley, Silicon Valley startup, sorting algorithm, Steve Ballmer, Steve Jobs, The Spirit Level, Tony Hsieh, why are manhole covers round?, William Shockley: the traitorous eight

The Gaussian integral also has something that Euler’s equation lacks—relevance to life as we live it. The e−x2 is the Gaussian function. A chart of it is the familiar bell-shaped curve of a normal probability distribution. This is the “curve” that teachers grade on—the one that supposedly governs heights, IQ scores, and the random walk of stock prices (but doesn’t quite). The Gaussian blur filter in Photoshop uses the same function to blur your ex out of the picture. In the equation, the integral computes the area under the bell-shaped curve and finds it equal to the square root of pi, or about 1.77. The equation can be viewed as a symbol of the role of chance in the world.

Anything that diminishes the chance of your item’s selling in effect costs you a significant fraction of that $100. The numbers in this puzzle were chosen so that the neighbor’s price is comparable to the economic damage he’s doing to you. By buying the item, you get the right to keep it off the market, when that suits your purposes, plus the right to sell it at any price the market will bear. Anything you get from selling the second item is pure gravy. The best plan is to hide one item until the first one sells. Then put the second item on sale at a reduced price, according to how late in the day it is. ? You put a glass of water on a record turntable and begin increasing the speed slowly.

The friendly solution is to pull the neighbor aside and say, “Now look, a mint-condition Wookiee in original packaging is worth a hundred dollars. You can check it out on eBay. You’re throwing money away by offering it for forty dollars.” This may persuade the neighbor to raise his price, maybe matching your $100. But this plan is not considered an especially good answer. Suppose the big spender finds two identical items on sale for $100. He’s equally likely to choose either one, and the other may go unsold. Ironically, you might be better off with the neighbor lowering his price. Were the neighbor to give the item away to the first person who showed up, then you wouldn’t have to worry about it anymore. You simply want the neighbor’s item off the market, one way or another.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

23andMe, Affordable Care Act / Obamacare, airport security, Apollo 11, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, book value, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, data science, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, hype cycle, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, machine readable, machine translation, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, Plato's cave, post-materialism, random walk, recommendation engine, Salesforce, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, sparse data, speech recognition, Steve Jobs, Steven Levy, systematic bias, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, vertical integration, Watson beat the top human players on Jeopardy!

This is not a good thing. Con Edison, the public utility that provides the city’s electricity, does regular inspections and maintenance of the manholes every year. In the past, it basically relied on chance, hoping that a manhole scheduled for a visit might be one that was poised to blow. It was little better than a random walk down Wall Street. In 2007 Con Edison turned to statisticians uptown at Columbia University in hopes that they could use its historical data about the grid, such as previous problems and what infrastructure is connected to what, to predict which manholes were likely to have trouble, so the company would know where to concentrate its resources.

All it requires is analyzing all the ticket sales for a given route and examining the prices paid relative to the number of days before the departure. If the average price of a ticket tended to decrease, it would make sense to wait and buy the ticket later. If the average price usually increased, the system would recommend buying the ticket right away at the price shown. In other words, what was needed was a souped-up version of the informal survey Etzioni conducted at 30,000 feet. To be sure, it was yet another massive computer science problem. But again, it was one he could solve. So he set to work. Using a sample of 12,000 price observations that was obtained by “scraping” information from a travel website over a 41-day period, Etzioni created a predictive model that handed its simulated passengers a tidy savings.

It isn’t just big data but “big text” too, since the system had to analyze words to recognize when a product was being discontinued or a newer model was about to launch, information that consumers ought to know and that affects prices. A year later, Decide.com was analyzing four million products using over 25 billion price observations. It identified oddities about retailing that people had never been able to “see” before, like the fact that prices might temporarily increase for older models once new ones are introduced. Most people would purchase the older one figuring it would be cheaper, but depending on when they clicked “buy,” they might pay more. As online stores increasingly use automated pricing systems, Decide.com can spot unnatural, algorithmic price spikes and warn consumers to wait.


pages: 255 words: 78,207

Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell

AltaVista, Amazon Web Services, Apollo 13, cloud computing, Computing Machinery and Intelligence, data science, en.wikipedia.org, Firefox, Guido van Rossum, information security, machine readable, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

(Hint: the code would crash.) So although these scripts might be fine to run as closely watched examples, autonomous production code requires far more excep‐ tion handling than we can fit into this book. Look back to Chap‐ ter 1 for more information about this. Crawling an Entire Site In the previous section, we took a random walk through a website, going from link to link. But what if you need to systematically catalog or search every page on a site? Crawling an entire site, especially a large one, is a memory-intensive process that is best suited to applications where a database to store crawling results is readily avail‐ able.

A company called Bidder’s Edge formed and created a new kind of meta-auction site. Rather than force you to go from auction site to auction site, comparing prices, it would aggregate data from all current auctions for a specific product (say, a hot new Furby doll or a copy of Spice World) and point you to the site that had the lowest price. Bidder’s Edge accomplished this with an army of web scrapers, constantly making requests to the web servers of the various auction sites in order to get price and prod‐ uct information. Of all the auction sites, eBay was the largest, and Bidder’s Edge hit eBay’s servers about 100,000 times a day.

For exam‐ ple: from urllib.request import urlopen from bs4 import BeautifulSoup html = urlopen("http://www.pythonscraping.com/pages/page3.html") bsObj = BeautifulSoup(html) print(bsObj.find("img",{"src":"../img/gifts/img1.jpg" }).parent.previous_sibling.get_text()) This code will print out the price of the object represented by the image at the loca‐ tion ../img/gifts/img1.jpg (in this case, the price is “$15.00”). How does this work? The following diagram represents the tree structure of the por‐ tion of the HTML page we are working with, with numbered steps: • <tr> — <td> — <td> — <td>(3) — “$15.00” (4) — s<td> (2) — <img src=”../img/gifts/img1.jpg">(1) 1.


pages: 360 words: 85,321

The Perfect Bet: How Science and Math Are Taking the Luck Out of Gambling by Adam Kucharski

Ada Lovelace, Albert Einstein, Antoine Gombaud: Chevalier de Méré, beat the dealer, behavioural economics, Benoit Mandelbrot, Bletchley Park, butterfly effect, call centre, Chance favours the prepared mind, Claude Shannon: information theory, collateralized debt obligation, Computing Machinery and Intelligence, correlation does not imply causation, diversification, Edward Lorenz: Chaos theory, Edward Thorp, Everything should be made as simple as possible, Flash crash, Gerolamo Cardano, Henri Poincaré, Hibernia Atlantic: Project Express, if you build it, they will come, invention of the telegraph, Isaac Newton, Johannes Kepler, John Nash: game theory, John von Neumann, locking in a profit, Louis Pasteur, Nash equilibrium, Norbert Wiener, p-value, performance metric, Pierre-Simon Laplace, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, statistical model, The Design of Experiments, Watson beat the top human players on Jeopardy!, zero-sum game

Contingencies, June 2009. 40“It’s easy to learn how to count cards”: Author interview with Richard Munchkin, August 2013. 40To evade security: Thorp, Beat the Dealer. 40Most mathematicians in the early twentieth century: Mazliak, Laurent. “Poincaré’s Odds.” Séminaire Poincaré XVI (2002): 999–1037. 41His research is still used today: Saloff-Coste, Laurent. “Random Walks on Finite Groups.” In Probability on Discrete Structures, ed. Harry Kesten (New York: Springer Science & Business, 2004). 41To perform the shuffle: Blood, Johnny Blood. “A Riffle Shuffle Being Performed during a Game of Poker at a Bar Near Madison, Wisconsin,. November 2005–April 2006.” Source: Flickr.

At the time, stock exchanges in the United Kingdom operated independently in each region. This meant there were occasional differences in prices. For example, it was sometimes possible to buy a stock for one price in London and sell it for a higher price in one of the provinces. Obtain such information quickly enough, and there was a profit to be made. During the 1850s, traders used telegrams to tell each other about discrepancies, cashing in on the difference before the price changed. From 1866 onward, America and Europe were linked by a transatlantic cable, which meant traders were able to spot incorrect prices even faster. The messages that traveled down the wire were to become an important part of finance (even today, traders refer to the GBP/USD exchange rate as “cable”).

The messages that traveled down the wire were to become an important part of finance (even today, traders refer to the GBP/USD exchange rate as “cable”). The invention of the telegraph meant that if prices were out of line in two locations, traders had the means to take advantage of the situation by buying at the cheaper price and selling at the higher one. In economics, the technique is known as “arbitrage.” Even before the invention of the telegraph, so-called arbitrageurs had been on the hunt for mismatched prices. In the seventeenth century, English goldsmiths would melt down silver coins if the price of silver climbed past the value of the coin. Some would even trek further afield, hauling gold from London to Amsterdam to capitalize on differences in the rate of exchange.


pages: 306 words: 82,765

Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Nicholas Taleb

anti-fragile, availability heuristic, behavioural economics, Benoit Mandelbrot, Bernie Madoff, Black Swan, Brownian motion, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cellular automata, Claude Shannon: information theory, cognitive dissonance, complexity theory, data science, David Graeber, disintermediation, Donald Trump, Edward Thorp, equity premium, fake news, financial independence, information asymmetry, invisible hand, knowledge economy, loss aversion, mandelbrot fractal, Mark Spitznagel, mental accounting, microbiome, mirror neurons, moral hazard, Murray Gell-Mann, offshore financial centre, p-value, Paradox of Choice, Paul Samuelson, Ponzi scheme, power law, precautionary principle, price mechanism, principal–agent problem, public intellectual, Ralph Nader, random walk, rent-seeking, Richard Feynman, Richard Thaler, Ronald Coase, Ronald Reagan, Rory Sutherland, Rupert Read, Silicon Valley, Social Justice Warrior, Steven Pinker, stochastic process, survivorship bias, systematic bias, tail risk, TED Talk, The Nature of the Firm, Tragedy of the Commons, transaction costs, urban planning, Yogi Berra

The Kelly Capital Growth Investment Criterion: Theory and Practice, vol. 3. World Scientific. Mandelbrot, Benoit, 1982. The Fractal Geometry of Nature. Freeman and Co. ———, 1997. Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. New York: Springer-Verlag. Mandelbrot, Benoit B., and N. N. Taleb, 2010. “Random Jump, Not Random Walk.” In Richard Herring, ed., The Known, the Unknown, and the Unknowable. Princeton, N.J.: Princeton University Press. Margalit, Avishai, 2002. The Ethics of Memory. Cambridge, Mass.: Harvard University Press. Nagel, T., 1970. The Possibility of Altruism. Princeton, N.J.: Princeton University Press.

Rav Safra, a third-century Babylonian scholar who was also an active trader, was offering some goods for sale. A buyer came as he was praying in silence, tried to purchase the merchandise at an initial price, and given that the rabbi did not reply, raised the price. But Rav Safra had no intention of selling at a higher price than the initial offer, and felt that he had to honor the initial intention. Now the question: Is Rav Safra obligated to sell at the initial price, or should he take the improved one? Such total transparency is not absurd and not uncommon in what seems to be a cutthroat world of transactions, my former world of trading.

To emit a Yogiberrism, in academia there is no difference between academia and the real world; in the real world, there is. Second, it is about the distortions of symmetry and reciprocity in life: If you have the rewards, you must also get some of the risks, not let others pay the price of your mistakes. If you inflict risk on others, and they are harmed, you need to pay some price for it. Just as you should treat others in the way you’d like to be treated, you would like to share the responsibility for events without unfairness and inequity. If you give an opinion, and someone follows it, you are morally obligated to be, yourself, exposed to its consequences.


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The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe by William Poundstone

Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Arthur Eddington, Bayesian statistics, behavioural economics, Benoit Mandelbrot, Berlin Wall, bitcoin, Black Swan, conceptual framework, cosmic microwave background, cosmological constant, cosmological principle, CRISPR, cuban missile crisis, dark matter, DeepMind, digital map, discounted cash flows, Donald Trump, Doomsday Clock, double helix, Dr. Strangelove, Eddington experiment, Elon Musk, Geoffrey Hinton, Gerolamo Cardano, Hans Moravec, heat death of the universe, Higgs boson, if you see hoof prints, think horses—not zebras, index fund, Isaac Newton, Jaron Lanier, Jeff Bezos, John Markoff, John von Neumann, Large Hadron Collider, mandelbrot fractal, Mark Zuckerberg, Mars Rover, Neil Armstrong, Nick Bostrom, OpenAI, paperclip maximiser, Peter Thiel, Pierre-Simon Laplace, Plato's cave, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Ronald Reagan: Tear down this wall, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Skype, Stanislav Petrov, Stephen Hawking, strong AI, tech billionaire, Thomas Bayes, Thomas Malthus, time value of money, Turing test

The probability of a particular path may be minuscule, but the probability that some path is taken could be high. But what does that tell us, exactly? It bolsters the case that the evolution of multicellular life, and the evolution of eyes, are not-improbable outcomes on an Earth-like planet that did, in fact, develop intelligent life. These adaptations were not roadblocks on the many paths (many random walks) to intelligence. But there might be other developments, just as crucial, that are highly improbable. As far as we know, life itself arose only once on Earth, and intelligence evolved only once. (All existing life shares the same genetic code. We don’t find ruins of intelligent dinosaur civilizations.)

For reasons not clear he left his life’s greatest achievement filed away, unpublished and unread. It was another mathematically inclined minister, Richard Price, who found Bayes’s manuscript after his death and recognized its importance. Price counted among his acquaintances a notorious group: the American revolutionaries Thomas Paine, Thomas Jefferson, and Benjamin Franklin, as well as Mary Wollstonecraft, the feminist who married an anarchist and gave birth to the author of Frankenstein. Price sent the Royal Society of London “an essay which I have found among the papers of our deceased friend Mr. Bayes, and which, in my opinion, has great merit.”

Bayes’s exposition is now judged to be flawed, confusing, and unresolved—and littered with analogies that are harder to understand than the points they attempt to clarify. Price’s introduction adds a spin that Bayes himself did not supply. Price frames the Essay as a dog whistle for believers in the ultimate cause, the Christian God: “The purpose I mean is, to shew what reason we have for believing that… the world must be the effect of the wisdom and power of an intelligent cause; and thus to confirm the argument taken from final causes for the existence of the Deity.” Price’s sentiment is what we today call an argument from design. The universe is a beautifully constructed watch, from which we can infer a divine watchmaker.


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, behavioural economics, buy and hold, buy low sell high, cryptocurrency, data science, deep learning, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, OpenAI, p-value, paper trading, performance metric, prediction markets, proprietary trading, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

For the last n periods, the following applies: Otherwise, the following applies: Otherwise, the following applies: Implementation of the relative strength indicator Now, let's implement and plot a relative strength indicator on our dataset: import statistics as stats time_period = 20 # look back period to compute gains & losses gain_history = [] # history of gains over look back period (0 if no gain, magnitude of gain if gain) loss_history = [] # history of losses over look back period (0 if no loss, magnitude of loss if loss) avg_gain_values = [] # track avg gains for visualization purposes avg_loss_values = [] # track avg losses for visualization purposes rsi_values = [] # track computed RSI values last_price = 0 # current_price - last_price > 0 => gain. current_price - last_price < 0 => loss. for close_price in close: if last_price == 0: last_price = close_price gain_history.append(max(0, close_price - last_price)) loss_history.append(max(0, last_price - close_price)) last_price = close_price if len(gain_history) > time_period: # maximum observations is equal to lookback period del (gain_history[0]) del (loss_history[0]) avg_gain = stats.mean(gain_history) # average gain over lookback period avg_loss = stats.mean(loss_history) # average loss over lookback period avg_gain_values.append(avg_gain) avg_loss_values.append(avg_loss) rs = 0 if avg_loss > 0: # to avoid division by 0, which is undefined rs = avg_gain / avg_loss rsi = 100 - (100 / (1 + rs)) rsi_values.append(rsi) In the preceding code, the following applies: We have used 20 days as our time period over which we computed the average gains and losses and then normalized it to be between 0 and 100 based on our formula for values.

We will also keep the real-time profit and loss, cash, position, and holding values: class ForLoopBackTester: def __init__(self): self.small_window=deque() self.large_window=deque() self.list_position=[] self.list_cash=[] self.list_holdings = [] self.list_total=[] self.long_signal=False self.position=0 self.cash=10000 self.total=0 self.holdings=0 As shown in the code, we will write the create_metric_out_of_prices function to update the real-time metrics the trading strategy needs in order to make a decision: def create_metrics_out_of_prices(self,price_update): self.small_window.append(price_update['price']) self.large_window.append(price_update['price']) if len(self.small_window)>50: self.small_window.popleft() if len(self.large_window)>100: self.large_window.popleft() if len(self.small_window) == 50: if average(self.small_window) >\ average(self.large_window): self.long_signal=True else: self.long_signal = False return True return False The buy_sell_or_hold_something function will take care of placing the orders based on the calculation from the prior function: def buy_sell_or_hold_something(self,price_update): if self.long_signal and self.position<=0: print(str(price_update['date']) + " send buy order for 10 shares price=" + str(price_update['price'])) self.position += 10 self.cash -= 10 * price_update['price'] elif self.position>0 and not self.long_signal: print(str(price_update['date'])+ " send sell order for 10 shares price=" + str(price_update['price'])) self.position -= 10 self.cash -= -10 * price_update['price'] self.holdings = self.position * price_update['price'] self.total = (self.holdings + self.cash) print('%s total=%d, holding=%d, cash=%d' % (str(price_update['date']),self.total, self.holdings, self.cash)) self.list_position.append(self.position) self.list_cash.append(self.cash) self.list_holdings.append(self.holdings) self.list_total.append(self.holdings+self.cash) We will feed this class by using the goog_data data frame as shown: naive_backtester=ForLoopBackTester() for line in zip(goog_data.index,goog_data['Adj Close']): date=line[0] price=line[1] price_information={'date' : date, 'price' : float(price)} is_tradable = naive_backtester.create_metrics_out_of_prices(price_information) if is_tradable: naive_backtester.buy_sell_or_hold_something(price_information) When we run the code, we will obtain the following curve.

= 0: current_pos = position current_pos_start = len(positions) continue # going from long position to flat or short position or # going from short position to flat or long position if current_pos * position <= 0: current_pos = position position_holding_time = len(positions) - current_pos_start current_pos_start = len(positions) if position_holding_time > RISK_LIMIT_MAX_POSITION_HOLDING_TIME_DAYS: print('RiskViolation position_holding_time', position_holding_time, ' > RISK_LIMIT_MAX_POSITION_HOLDING_TIME_DAYS', RISK_LIMIT_MAX_POSITION_HOLDING_TIME_DAYS) risk_violated = True We will check that the new long/short position is within the Max Position risk limits, as shown in the following code: if abs(position) > RISK_LIMIT_MAX_POSITION: print('RiskViolation position', position, ' > RISK_LIMIT_MAX_POSITION', RISK_LIMIT_MAX_POSITION) risk_violated = True Next, we also check that the updated traded volume doesn't violate the allocated Maximum Traded Volume risk limit: if traded_volume > RISK_LIMIT_MAX_TRADED_VOLUME: print('RiskViolation traded_volume', traded_volume, ' > RISK_LIMIT_MAX_TRADED_VOLUME', RISK_LIMIT_MAX_TRADED_VOLUME) risk_violated = True Next, we will write some code that updates the PnLs, unchanged from before: open_pnl = 0 if position > 0: if sell_sum_qty > 0: open_pnl = abs(sell_sum_qty) * (sell_sum_price_qty / sell_sum_qty - buy_sum_price_qty / buy_sum_qty) open_pnl += abs(sell_sum_qty - position) * (close_price - buy_sum_price_qty / buy_sum_qty) elif position < 0: if buy_sum_qty > 0: open_pnl = abs(buy_sum_qty) * (sell_sum_price_qty / sell_sum_qty - buy_sum_price_qty / buy_sum_qty) open_pnl += abs(buy_sum_qty - position) * (sell_sum_price_qty / sell_sum_qty - close_price) else: closed_pnl += (sell_sum_price_qty - buy_sum_price_qty) buy_sum_price_qty = 0 buy_sum_qty = 0 sell_sum_price_qty = 0 sell_sum_qty = 0 last_buy_price = 0 last_sell_price = 0 print("OpenPnL: ", open_pnl, " ClosedPnL: ", closed_pnl, " TotalPnL: ", (open_pnl + closed_pnl)) pnls.append(closed_pnl + open_pnl) Now, we need to write the following code, which checks that the new Total PnL, which is the sum of realized and un-realized PnLs, is not in violation of either the Maximum allowed Weekly Stop Loss limit or the Maximum allowed Monthly Stop Loss limit: if len(pnls) > 5: weekly_loss = pnls[-1] - pnls[-6] if weekly_loss < RISK_LIMIT_WEEKLY_STOP_LOSS: print('RiskViolation weekly_loss', weekly_loss, ' < RISK_LIMIT_WEEKLY_STOP_LOSS', RISK_LIMIT_WEEKLY_STOP_LOSS) risk_violated = True if len(pnls) > 20: monthly_loss = pnls[-1] - pnls[-21] if monthly_loss < RISK_LIMIT_MONTHLY_STOP_LOSS: print('RiskViolation monthly_loss', monthly_loss, ' < RISK_LIMIT_MONTHLY_STOP_LOSS', RISK_LIMIT_MONTHLY_STOP_LOSS) risk_violated = True Here, we have added a robust risk management system to our existing trading strategy that can be extended to any other trading strategies we intend on deploying to live trading markets in the future.


Turing's Cathedral by George Dyson

1919 Motor Transport Corps convoy, Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, Benoit Mandelbrot, Bletchley Park, British Empire, Brownian motion, cellular automata, Charles Babbage, cloud computing, computer age, Computing Machinery and Intelligence, Danny Hillis, dark matter, double helix, Dr. Strangelove, fault tolerance, Fellow of the Royal Society, finite state, Ford Model T, Georg Cantor, Henri Poincaré, Herman Kahn, housing crisis, IFF: identification friend or foe, indoor plumbing, Isaac Newton, Jacquard loom, John von Neumann, machine readable, mandelbrot fractal, Menlo Park, Murray Gell-Mann, Neal Stephenson, Norbert Wiener, Norman Macrae, packet switching, pattern recognition, Paul Erdős, Paul Samuelson, phenotype, planetary scale, RAND corporation, random walk, Richard Feynman, SETI@home, social graph, speech recognition, The Theory of the Leisure Class by Thorstein Veblen, Thorstein Veblen, Turing complete, Turing machine, Von Neumann architecture

“If for example he wanted a green-eyed pig with curly hair and six toes and this event had a non zero probability, then the Monte Carlo experimenter, unlike the agriculturalist, could immediately produce the animal.”75 Biological evolution is, in essence, a Monte Carlo search of the fitness landscape, and whatever the next stage in the evolution of evolution turns out to be, computer-assisted Monte Carlo will get there first. Monte Carlo is able to discover practical solutions to otherwise intractable problems because the most efficient search of an unmapped territory takes the form of a random walk. Today’s search engines, long descended from their ENIAC-era ancestors, still bear the imprint of their Monte Carlo origins: random search paths being accounted for, statistically, to accumulate increasingly accurate results. The genius of Monte Carlo—and its search-engine descendants—lies in the ability to extract meaningful solutions, in the face of overwhelming information, by recognizing that meaning resides less in the data at the end points and more in the intervening paths.

The Ferranti Mark 1, with 256 40-bit words (1 kilobyte) of cathode-ray tube memory, and a 16,000-word magnetic drum, was the first commercially available implementation of Turing’s Universal Machine. At Turing’s insistence, a random number generator was included, so that the computer could learn by trial and error or perform a search by means of a random walk. (Department of Computer Science, University of Manchester) Left to right: James Pomerene, Julian Bigelow, John von Neumann, and Herman Goldstine, at the Institute for Advanced Study, date unknown. Von Neumann, who succumbed to cancer in 1957, “died so prematurely, seeing the promised land but hardly entering it,” remembered Stan Ulam in 1976.

Selling it out of a rented storefront in a blighted neighborhood of Newark, he realized enough of a profit to open his own business, taking on as partners his sister Carrie; her husband, Louis Frank; and their close friend Felix Fuld. By 1928, Bamberger’s department store occupied 1 million square feet, with 3,500 employees and over $32 million in annual sales. The Amazon.com of its time, Bamberger’s featured price tags on all merchandise, no-questions-asked money-back guarantees, toll-free telephone numbers, job security, and an on-site public library for employees. The eight-floor flagship store on Market Street in Newark included a 500-watt radio station, WOR, and introduced what is now the Macy’s Thanksgiving Day Parade.


pages: 369 words: 153,018

Power, Sex, Suicide: Mitochondria and the Meaning of Life by Nick Lane

Benoit Mandelbrot, caloric restriction, caloric restriction, clockwork universe, double helix, Drosophila, Geoffrey West, Santa Fe Institute, Louis Pasteur, mandelbrot fractal, out of africa, phenotype, power law, random walk, Recombinant DNA, Richard Feynman, seminal paper, stem cell, unbiased observer

Ridding himself of higher religious connotations, Stephen Jay Gould once compared complexity with the random meanderings of a drunkard: if a wall blocks his passage on one side of the pavement, then the drunkard is more likely to end up in the gutter, simply because there is nowhere else for him to go. In the case of complexity, the metaphorical wall is the base of life: it is not possible to be any simpler than a bacterium (at least as an independent organism), so life’s random walk could only have been towards greater complexity. A related view is that life became more complex because evolutionary success was more likely to be found in the exploitation of new niches—an idea known as the ‘pioneering’ theory. Given that the simplest niches were already occupied by bacteria, the only direction in which life could evolve was towards greater complexity.

The great chain of being may be an illusion, but it is a compelling one, one that held mankind in its sway for 2000 years (since the ancient Greeks). Just as we must account for the apparent evolution of ‘purpose’ in biology (the heart as a pump, etc), so too we must account for the apparent Power Laws 153 trajectory towards greater complexity. Can a random walk, stopping off at vacant niches on the way, really produce something that even looks like a ramp of complexity? To twist Stephen Jay Gould’s analogy, how come so many meandering drunkards didn’t end up in the gutter, but actually succeeded in crossing the road? One possible solution, inherent to eukaryotic cells but not to bacteria, is sex.

The efficiency of energy metabolism may have been the driving force behind the rampant ascent of eukaryotes to diversity and complexity. The same principles underpin energetic efficiency in all eukaryotic cells, giving an impetus to the evolution of larger size in both unicellular and multicellular organisms, whether plants, animals, or fungi. Rather than being a random walk through vacant niches, or a march driven by the imperative of sex, the trajectory of eukaryotic evolution is better explained as an inherent tendency to become larger, with an immediate payback for an immediate advantage—the economy of scale. As animals become larger, their metabolic rate falls, giving them a lower cost of living.


pages: 263 words: 89,368

925 Ideas to Help You Save Money, Get Out of Debt and Retire a Millionaire So You Can Leave Your Mark on the World by Devin D. Thorpe

asset allocation, buy and hold, call centre, diversification, estate planning, fixed income, Home mortgage interest deduction, index fund, junk bonds, knowledge economy, low interest rates, money market fund, mortgage tax deduction, payday loans, random walk, risk tolerance, Skype, Steve Jobs, transaction costs, women in the workforce, zero-sum game

If the trades are equally likely to make a profit or loss, you’d make zero dollars at the end of a long run of trading—but for the commissions. The only mathematically expected change in your portfolio value from frequent day trading is the cost of the commission. You’re going to lose money. Few Winners: As a function, however, of the random walk of the markets, actual returns will vary. Some people will lose a lot. Some will lose everything. Some will lose exactly as expected—the sum of their commissions. Some will make enough just to cover commissions and end up with what they had in the beginning. A few will make money, but less than the markets earn.

An option gives the employee the right—but not the obligation—to buy a share of company stock at a predetermined price (the strike price) for a term (usually five to ten years). At the end of the term, if the option has not been exercised (used to buy stock) it expires worthless. Why Is That a Good Thing? The right, without the obligation, to buy stock is a good thing because you are not required to make any investment, to put any money at risk to participate in the increasing value of the company. For instance, if the strike price (the price at which you can buy shares of the company stock) is $1 and the stock price goes to $11, you have a profit baked in of $10 per share.

Hence, investors often say things like, “I own Ford and IBM,” by which they simply mean that they own shares of stock in those companies. Stock prices go up and down quite randomly in the short run, but do tend upward over long periods of time. The price you pay for the stock and the price at which you sell the stock are ever so slightly different—you pay a bit more to buy and get a bit less when you sell. Market makers take the difference. You’ll also pay a commission to buy and sell stocks. Stock prices can go all the way to zero. There is no limit to how high a stock price can go. Some companies pay dividends to all the shareholders (the people who own the stock) and some don’t.


pages: 250 words: 75,586

When the Air Hits Your Brain: Tales From Neurosurgery by Frank Vertosick

butterfly effect, double helix, Dr. Strangelove, index card, medical residency, planned obsolescence, random walk, sparse data, zero-sum game

If I bailed out now—changed residencies, went to law school, got an M.B.A.—I risked ending up like William the Registrar, flitting from job to job until I retired, without ever accomplishing anything. Worse, I had no guarantee of being happier or more competent in those fields. No more second chances. I decided that my random walk through life must end in neurosurgery. I refused to operate again for weeks after the aneurysm fiasco, a feat possible on the slow V.A. service. Talking with the attendings about my career doubts would have done little good—a marine boot can’t discuss his doubts about the Corps with his drill sergeant.

You got hours to go before she’s stable…and go easy on the epi; her perfusion is poor as it is and I don’t want her fingers to die. Push the fluid harder.” My ego deflated, I went back to the chair. Maggie was right about the epinephrine. B.G.’s fingertips grew more discolored by the hour. Like Mephistopheles, epinephrine will do your bidding—for a price. The increase in blood pressure and heart contractility after an epi infusion comes at the expense of blood flow to the limbs. Too much epi and the hands and feet will become gangrenous. Another hour passed before the hypotension and fibrillation returned. More CPR, more albumin. Some lidocaine and bretylium.

Pressure makes all the difference in the world.” * * * * See William T. Carpenter Jr., and Robert W. Buchanan’s excellent review article, “Schizophrenia,” in The New England Journal of Medicine 330 (1994): 681-90. 5 The Museum of Pain Pleasure is oft a visitant; but pain clings cruelly to us. —JOHN KEATS Pain is the price we pay for mobility. Since the dawn of life creatures have segregated into two camps: motionless foodmakers and migrating food foragers. Creatures in the first camp learned to draw energy from their immediate environments. Plants turn chloroplasts to the sun and use photosynthesis to manufacture glucose, while deep-sea creatures harness heat arising from thermal vents on the ocean floor.


pages: 357 words: 91,331

I Will Teach You To Be Rich by Sethi, Ramit

Albert Einstein, asset allocation, buy and hold, buy low sell high, diversification, diversified portfolio, do what you love, geopolitical risk, index fund, John Bogle, late fees, low interest rates, money market fund, mortgage debt, mortgage tax deduction, Paradox of Choice, prediction markets, random walk, risk tolerance, Robert Shiller, shareholder value, Silicon Valley, survivorship bias, the rule of 72, Vanguard fund

A complex may start ten small new equity funds with different in-house managers and wait to see which ones are successful. Suppose after a few years only three funds produce total returns better than the broad-market averages. The complex begins to market those successful funds aggressively, dropping the other seven and burying their records. —BURTON G. MALKIEL, A RANDOM WALK DOWN WALL STREET * * * Second, when it comes to fund ratings, companies rely on something called survivorship bias to obscure the picture of how well a company is doing. Survivorship bias exists because funds that fail are not included in any future studies of fund performance for the simple reason that they don’t exist anymore.

One of my friends bought a $20,000 Acura Integra, drove it for about seven years, and then sold it for 50 percent of the price. That means she got a fantastic deal on driving a new car for seven years. To check out how your potential cars will fare, visit www.kbb.com and calculate resale prices in five, seven, and ten years. You’ll be surprised how quickly most cars depreciate and how others (Toyotas and Hondas especially) retain their value. INSURANCE. The insurance rates for a new and used car can be pretty different. Even if they’re only slightly different (say, $50/month), that can add up over many years. FUEL EFFICIENCY. With gas prices on a roller-coaster ride, you may want to hedge your bets and consider a very fuel-efficient, or even a hybrid, car.

I said I was prepared to buy the car within two weeks and, because I knew exactly how much profit they would make off the car, I would go with the lowest price offered to me. The same day, as I sat back with a cup of Earl Grey tea and a Taco Bell burrito, faxes started rolling in from the dealers. After I had all the offers, I called the dealers, told them the lowest price I’d received, and gave each of them a chance to beat it. This resulted in a bidding war that led to a downward spiral of near-orgasmic deals. In the end, I chose a dealer in Palo Alto who sold me the car for $2,000 under invoice—a nearly unheard-of price. I didn’t have to waste my time going to multiple dealerships, and I didn’t have to bother with slimy car salesmen.


The Age of Turbulence: Adventures in a New World (Hardback) - Common by Alan Greenspan

addicted to oil, air freight, airline deregulation, Alan Greenspan, Albert Einstein, asset-backed security, bank run, Berlin Wall, Black Monday: stock market crash in 1987, Bretton Woods, business cycle, business process, buy and hold, call centre, capital controls, carbon tax, central bank independence, collateralized debt obligation, collective bargaining, compensation consultant, conceptual framework, Corn Laws, corporate governance, corporate raider, correlation coefficient, cotton gin, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cuban missile crisis, currency peg, currency risk, Deng Xiaoping, Dissolution of the Soviet Union, Doha Development Round, double entry bookkeeping, equity premium, everywhere but in the productivity statistics, Fall of the Berlin Wall, fiat currency, financial innovation, financial intermediation, full employment, Gini coefficient, Glass-Steagall Act, Hernando de Soto, income inequality, income per capita, information security, invisible hand, Joseph Schumpeter, junk bonds, labor-force participation, laissez-faire capitalism, land reform, Long Term Capital Management, low interest rates, Mahatma Gandhi, manufacturing employment, market bubble, means of production, Mikhail Gorbachev, moral hazard, mortgage debt, Myron Scholes, Nelson Mandela, new economy, North Sea oil, oil shock, open economy, open immigration, Pearl River Delta, pets.com, Potemkin village, price mechanism, price stability, Productivity paradox, profit maximization, purchasing power parity, random walk, Reminiscences of a Stock Operator, reserve currency, Right to Buy, risk tolerance, Robert Solow, Ronald Reagan, Savings and loan crisis, shareholder value, short selling, Silicon Valley, special economic zone, stock buybacks, stocks for the long run, Suez crisis 1956, the payments system, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tipper Gore, too big to fail, total factor productivity, trade liberalization, trade route, transaction costs, transcontinental railway, urban renewal, We are all Keynesians now, working-age population, Y2K, zero-sum game

.* And while the economy and corporate profits subsequently advanced, it took nearly two years for the Dow to recover fully. When markets are behaving rationally, as they do almost all the time, they appear to engage in a "random walk": the past gives no better indication than a coin flip of the future direction of the price of a stock. But sometimes that walk is interrupted by a stampede. When gripped by fear, people rush to disengage from commitments, and stocks will plunge. And when people are driven by euphoria, they will drive up prices to nonsensical levels. So the key question remains, as I summarized it in a 1996 reflection I shall never live down, "How do we know when irrational exuberance has unduly escalated asset values, which then become subject to unexpected and prolonged contractions?"

If the market is efficient, then all knowledge affecting the prospective future supply/ demand balance ought already to be reflected in the spot prices of crude oil.* Many analysts saw spot prices of early 2007 embodying a large "terror- *Spot prices in principle embody the market participants' knowledge not only of the forces setting spot prices but also of those setting futures prices. In fact, when the market participants perceive a forthcoming very large rise in price, long-term futures prices will rise and pull up the spot price with them. If the spot price is below longer-term futures by more than the carrying cost of inventories, speculators can buy spot oil, sell the distant futures, store the spot oil, pay interest on the money borrowed to hold the oil, and, at the expiration of the contract, deliver the oil and pocket the profit.

INDEX Pakistan, 322 Panama, 336 Panetta, Leon, 144-45, 146 Parker, Charlie, 27 Parker, Sanford "Sandy," 4 3 , 47, 55-56, 346 Parry, Bob, 103,105 Parry, Charles, 428 Patten, Tom, 47 pay-go rule, 120, 216, 233-34, 235 payroll tax, 96, 413, 414 Pearl Harbor, attack on, 24, 25 Pearl River delta, 12, 304, 383, 477 peasants, 254, 261, 263, 302, 339 Pension Benefit Guaranty Corporation, 419, 421 Pension Protection Act (2006), 421 pensions, pension funds, 166, 168, 217, 425, 429 defined-benefit, 419-22 rate of return and, 4 1 9 - 2 0 Pepper, Claude, 95, 96 Perez Alfonso, Juan Pablo, 258 Peron,Juan, 342, 345 Perot, Ross, 122 Pershing, John, 336 Persian Gulf, 113,440 Peterson Institute of International Economics, 303, 396 petrodollars, 80, 84 Petroleos Mexicanos (PEMEX), 336n plant and equipment, 165, 368-69, 385, 413, 4 7 1 , 499 plug-in electric vehicles, 458, 461 Podesta, John, 202 Pohl, Karl Otto, 286-87 Poland, 125, 126, 132-33, 137, 138 politics, 1 1 0 - 1 1 , 3 8 9 , 4 8 3 economics and, 54-76, 86-94, 118-22, 142-50, 206-12,215-24,233-38 polls: economic, 232, 273, 328, 392 political, 58, 75, 89, 111, 159 population, 263, 268, 409-12, 467, 470, 499 in China, 3 0 4 , 4 1 1 populism, 18, 337, 364, 389, 392, 394-95, 478, 479,483 anticorporate, 430 charismatic leaders and, 339 defined, 3 3 5 - 3 6 Latin America and, 255, 280, 3 3 4 - 3 5 Pork Barrel Reduction Act (2005), 244 Post, Marjorie Merriweather, 78 Postum Cereal Company, 78 poverty, 14, 18, 19, 85, 259, 264, 267-68 globalization and, 364 in India, 316, 320 in Latin America, 260, 336, 337 Powell, Colin, 239 Prell, Mike, 166 President's Foreign Intelligence Advisory Board (PFIAB), 129,131 Price, Ray, 57 price controls, 15-16, 72, 82, 132-33, 138, 398n, 436 Nixon's wage and price controls, 16, 61-62, 63, 297, 3 4 4 , 3 9 5 , 446n, 482 prices, 13, 139, 296n, 303, 378, 381-84, 4 8 0 - 8 1 , 491 consumer, 102, 380, 395-96, 482 of homes, 229, 230 stability of, 155, 175-77, 201, 377, 389n, 390, 3 9 1 , 4 4 4 , 4 4 8 ^ 9 , 4 8 0 - 8 1 , 491 wage indexing and, 125 see also inflation production, 127, 207, 382, 493 globalization and, 364, 365 oil expansion plans and, 442—43 productivity, 13, 132, 155, 161, 200, 207, 219, 225, 278, 3 6 9 , 4 8 8 , 4 9 9 , 500 in China, 304 competition and, 262, 268-69 forecasting and, 467 in Germany, 2 8 5 - 8 6 in India, 320 technology and, 13, 17, 166-67, 168, 171-73, 213, 214, 393, 471-72, 4 7 4 - 7 6 U.S. current account deficit and, 350, 352, 356 see also G D P profit margins, 171, 172, 368, 369-70, 393 profit seeking, 252, 365, 370 progress, 4 8 - 5 1 , 363-65, 505 property rights, 15, 16, 124, 138, 187, 276, 287, 455 economic growth and, 250, 261, 311, 386, 3 8 8 89, 502 Marxist view of, 140, 252, 299-300 see also specific places protectionism, 12, 141, 261, 265, 288, 312, 313, 362,366,376,498 economic future and, 467, 468 national treasures and, 273-74 see also tariffs Public Company Accounting Oversight Board (PCAOB), 430 purchasing power parity (PPP), 259n—60n, 296, 352n Putin, Vladimir, 323-28, 3 3 1 - 3 3 , 500 Qwest, 200 radio, 22, 24, 25 railroads, 71, 72, 139, 162, 196, 200, 355n, 444n timetables of, 22-23 transcontinental, 275 Rand, Ayn, 4 0 - 4 1 , 5 1 - 5 3 , 97, 138, 265, 323 "random walk," 466 Rao, P V . Narasimha, 318 Ras Tanura, 445 Reagan, Ronald, 73, 8 1 , 8 6 - 9 9 , 102, 111, 115, 122, 138, 1 5 0 , 2 3 5 , 3 7 2 AG nominated by, 99, 249 arms buildup under, 126, 128, 129-30 conservatism of, 87, 211 526 More ebooks visit: http://www.ccebook.cn ccebook-orginal english ebooks This file was collected by ccebook.cn form the internet, the author keeps the copyright.


All About Asset Allocation, Second Edition by Richard Ferri

activist fund / activist shareholder / activist investor, Alan Greenspan, asset allocation, asset-backed security, barriers to entry, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, book value, buy and hold, capital controls, commoditize, commodity trading advisor, correlation coefficient, currency risk, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, equity premium, equity risk premium, estate planning, financial independence, fixed income, full employment, high net worth, Home mortgage interest deduction, implied volatility, index fund, intangible asset, inverted yield curve, John Bogle, junk bonds, Long Term Capital Management, low interest rates, managed futures, Mason jar, money market fund, mortgage tax deduction, passive income, pattern recognition, random walk, Richard Thaler, risk free rate, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, selection bias, Sharpe ratio, stock buybacks, stocks for the long run, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve

The most important factor is the spot price of the commodity. Typically, a near-term futures contract will be priced very close to the spot price. Other factors that influence price include the outlook for supply and demand, the cost of storage until delivery, and the level of interest rates between now and the delivery date. At times the futures price of a commodity contract may be higher than the spot price, and at other times it may be lower. This depends on the consensus price forecast. Figure 10-4 illustrates the historical difference in price between CRB spot prices and one-month CRB futures prices. It is interesting to note that during the 1990s, near-term delivery futures prices lagged spot commodity prices by a considerable amount.

It is interesting to note that during the 1990s, near-term delivery futures prices lagged spot commodity prices by a considerable amount. During the decade, futures buyers were anticipating lower raw material costs as productivity gains and foreign competition drove prices down. A low point for commodities futures prices relative their spot prices occurred in the late 1990s. Futures prices rallied during the past decade. This caused the spread between spot prices and futures prices to disappear. For a while demand for futures-related products become so great that the return from futures was higher than the return from spot prices. This created a one-time excess gain for futures market investors that has since gone away.

An investment advisor shares his views on the asset allocation of low-cost index funds. Published in 1998 by Namborn. Protecting Your Wealth in Good Times and Bad, by Richard A. Ferri, CFA. A sensible life-long saving and investing handbook for all investors. Published in 2003 by McGraw-Hill. 319 320 APPENDIX B A Random Walk Down Wall Street, by Burton G. Malkiel. A comprehensive look at today’s market and what is driving it. 9th edition, published in 2007 by W. W. Norton & Company. Stocks for the Long Run, by Jeremy Siegel. A classic book about investing, with market data going back 200 years. 4th edition, published in 2007 by McGraw-Hill.


pages: 313 words: 34,042

Tools for Computational Finance by Rüdiger Seydel

bioinformatics, Black-Scholes formula, Brownian motion, commoditize, continuous integration, discrete time, financial engineering, implied volatility, incomplete markets, interest rate swap, linear programming, London Interbank Offered Rate, mandelbrot fractal, martingale, random walk, risk free rate, stochastic process, stochastic volatility, transaction costs, value at risk, volatility smile, Wiener process, zero-coupon bond

The discretely sampled At is constant between sampling times, and it jumps at tk with the step Atk−1 = Atk + 1 (Atk − Stk ). k−1 For each k this jump can be written A− (S) = A+ (S) + 1 (A+ (S) − S), where S = Stk . k−1 (6.14a) A− and A+ denote the values of A immediately before and immediately after sampling at tk . The no-arbitrage principle implies continuity of V at the sampling instances tk in the sense of continuity of V (St , At , t) for any realization of a random walk. In our setting, this continuity is written V (S, A+ , tk ) = V (S, A− , tk ) . (6.14b) But for a fixed (S, A) this equation defines a jump of V at tk . The numerical application of the jump condition (6.14) is as follows: The A-axis is discretized into discrete values Aj , j = 1, . . . , J. For each time period between two consecutive sampling instances, say for tk+1 → tk , the option’s value is independent of A because in our discretized setting At is piecewise constant; accordingly ∂V ∂A = 0.

Today there is a virtually unlimited variety of contracts on objects and their future state, from credit risks to weather prediction. The future price of the underlying asset is usually unknown, it may move up or down in an unexpected way. For example, scarcity of a product will result in higher prices. Or the prices of stocks may decline sharply. But the agreement must fix a price today, for an exchange of asset and payment that will happen in weeks or months. At maturity, the spot price usually differs from the agreed price of the contract. The difference between spot price and contract price may be significant. Hence contracts into the future are risky. Investors and portfolio managers hope their shares and markets perform well, and are concerned of risks that might weigh on their assets.

There are two basic types of option: The call option gives the holder the right to buy the underlying for an agreed price K by the date T . The put option gives the holder the right to sell the underlying for the price K by the date T . The previously agreed price K of the contract is called strike or exercise price1 . It is important to note that the holder is not obligated to exercise —that is, to buy or sell the underlying according to the terms of the contract. The holder may wish to close his position by selling the option. In summary, at time t the holder of the option can choose to 1 The price K as well as other prices are meant as the price of one unit of an asset, say, in $. 2 Chapter 1 Modeling Tools for Financial Options • sell the option at its current market price on some options exchange (at t < T ), • retain the option and do nothing, • exercise the option (t ≤ T ), or • let the option expire worthless (t ≥ T ).


pages: 463 words: 105,197

Radical Markets: Uprooting Capitalism and Democracy for a Just Society by Eric Posner, E. Weyl

3D printing, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, anti-communist, augmented reality, basic income, Berlin Wall, Bernie Sanders, Big Tech, Branko Milanovic, business process, buy and hold, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collective bargaining, commoditize, congestion pricing, Corn Laws, corporate governance, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, Donald Trump, Elon Musk, endowment effect, Erik Brynjolfsson, Ethereum, feminist movement, financial deregulation, Francis Fukuyama: the end of history, full employment, gamification, Garrett Hardin, George Akerlof, global macro, global supply chain, guest worker program, hydraulic fracturing, Hyperloop, illegal immigration, immigration reform, income inequality, income per capita, index fund, informal economy, information asymmetry, invisible hand, Jane Jacobs, Jaron Lanier, Jean Tirole, Jeremy Corbyn, Joseph Schumpeter, Kenneth Arrow, labor-force participation, laissez-faire capitalism, Landlord’s Game, liberal capitalism, low skilled workers, Lyft, market bubble, market design, market friction, market fundamentalism, mass immigration, negative equity, Network effects, obamacare, offshore financial centre, open borders, Pareto efficiency, passive investing, patent troll, Paul Samuelson, performance metric, plutocrats, pre–internet, radical decentralization, random walk, randomized controlled trial, Ray Kurzweil, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Ronald Coase, Rory Sutherland, search costs, Second Machine Age, second-price auction, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, special economic zone, spectrum auction, speech recognition, statistical model, stem cell, telepresence, Thales and the olive presses, Thales of Miletus, The Death and Life of Great American Cities, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, trade route, Tragedy of the Commons, transaction costs, trickle-down economics, Tyler Cowen, Uber and Lyft, uber lyft, universal basic income, urban planning, Vanguard fund, vertical integration, women in the workforce, Zipcar

. § 18 (amend. 1950). 12. David Gerber, Law and Competition in Twentieth-Century Europe: Protecting Prometheus (Clarendon Press, 2001). 13. See Peter L. Bernstein, Capital Ideas: The Improbable Origins of Modern Wall Street (Wiley, 1992), for a history. 14. A classic statement of this theory is Burton G. Malkiel, A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (W.W. Norton & Company, 10th ed., 2012). 15. Robert J. Shiller, Irrational Exuberance (Princeton University Press, 3d ed., 2015). 16. As of 2010, institutional investors held common stock worth $11.5 trillion. In the same year, index funds held about $1.4 trillion.

If Ana raises her sale price above her reservation (that is, actual) value, she benefits from the higher sale price 30% of the time—when those higher-value buyers turn up. Her benefit from raising the price would thus be .3ΔP, where ΔP is the increment in the sale price. On the other hand, as long as she remains in possession of the house she must pay the tax of 30%, which, applied to this incremental value, forces her to pay an additional .3ΔP. Thus, the benefit from increasing the price above the reservation price is exactly offset by the cost. This stops owners from holding out for a high sale price by setting a price higher than their reservation value.

For any tax rate below the turnover rate, the possessor will always set a price above the amount she is willing to accept.43 When the tax rate is zero, the possessor is free to set any price she wishes at no cost and thus would set the monopoly price. When the tax rate equals the turnover rate, she has to reveal her true value. For intermediate tax rates, she will still be discouraged by the tax from setting a very high price, but she will not have a full incentive to report her exact value. Instead, she will set a price intermediate between her true value and the monopoly price that she expects a buyer to be willing to pay.


pages: 362 words: 97,288

Ghost Road: Beyond the Driverless Car by Anthony M. Townsend

A Pattern Language, active measures, AI winter, algorithmic trading, Alvin Toffler, Amazon Robotics, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing, Blitzscaling, Boston Dynamics, business process, Captain Sullenberger Hudson, car-free, carbon footprint, carbon tax, circular economy, company town, computer vision, conceptual framework, congestion charging, congestion pricing, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deep learning, deepfake, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, Didi Chuxing, drive until you qualify, driverless car, drop ship, Edward Glaeser, Elaine Herzberg, Elon Musk, en.wikipedia.org, extreme commuting, financial engineering, financial innovation, Flash crash, food desert, Ford Model T, fulfillment center, Future Shock, General Motors Futurama, gig economy, Google bus, Greyball, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, Jevons paradox, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, Kiva Systems, Lewis Mumford, loss aversion, Lyft, Masayoshi Son, megacity, microapartment, minimum viable product, mortgage debt, New Urbanism, Nick Bostrom, North Sea oil, Ocado, openstreetmap, pattern recognition, Peter Calthorpe, random walk, Ray Kurzweil, Ray Oldenburg, rent-seeking, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, SoftBank, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, TED Talk, Tesla Model S, The Coming Technological Singularity, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Great Good Place, too big to fail, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, urban sprawl, US Airways Flight 1549, Vernor Vinge, vertical integration, Vision Fund, warehouse automation, warehouse robotics

In New York, I got nailed with a 500 percent surge charge from Uber that left me speechless. Yet I found it remarkable that a company’s code imposed its own form of demand-based transportation pricing in a city that had itself tried but failed to do so for decades. In Argentina’s second city, Córdoba, I peered over the shoulders of traffic engineers routing buses around a street protest via the city’s fully programmable traffic-signal system, Latin America’s first. And in San Francisco, I downloaded Serendipitor, an app that let me reprogram myself by charting a random walk through the city’s grid. All of these innovations were variations on the same theme—a common understanding that we already have plenty of transportation and just need to use it more effectively.

The rambling study Vickrey produced for City Hall spans more than 150 pages, and makes for dry reading. But it reveals the conceptual roots of congestion pricing as it is practiced today. Vickrey understood the problem foremost as an exercise in marginal-cost pricing, where the price of goods and services is set at the additional cost involved in providing them, rather than at what the market will bear. Fixed costs like track and trains are ignored. In business, marginal-cost pricing often serves as the basis for deep discounting during periods of slow sales—allowing a producer to set the lowest price possible while maintaining output and avoiding operating losses. For the New York City public-transit system, Vickrey reasoned, marginal cost was a perfect measure of what riders would be willing to pay to avoid the delays and discomfort of congestion.

Where the two curves meet, the market reaches equilibrium (E1), and the prevailing price (C1) and level of supply (F1) are set. Figure 5-1. The effect of falling costs on demand for shipping. Now, consider what happens when a new technology like AVs is introduced. Let’s say one day Robohaulers, Inc., enters the market with a million-conveyor fleet and with this last-mile solution can move the goods at 25 percent below what other parcel-haulers charge. The result is predictable. Robohaulers will undercut the competition, who must match the new price, and the price of shipping falls (to C2). But now that prices are lower, consumers respond by buying more, and the supply curve slides to the right (the dashed curve S2).


pages: 372 words: 101,678

Lessons from the Titans: What Companies in the New Economy Can Learn from the Great Industrial Giants to Drive Sustainable Success by Scott Davis, Carter Copeland, Rob Wertheimer

3D printing, activist fund / activist shareholder / activist investor, additive manufacturing, Airbnb, airport security, asset light, barriers to entry, Big Tech, Boeing 747, business cycle, business process, clean water, commoditize, coronavirus, corporate governance, COVID-19, data science, disruptive innovation, Elisha Otis, Elon Musk, factory automation, fail fast, financial engineering, Ford Model T, global pandemic, hydraulic fracturing, Internet of things, iterative process, junk bonds, Kaizen: continuous improvement, Kanban, low cost airline, Marc Andreessen, Mary Meeker, megacity, Michael Milken, Network effects, new economy, Ponzi scheme, profit maximization, random walk, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, shareholder value, Silicon Valley, six sigma, skunkworks, software is eating the world, strikebreaker, tech billionaire, TED Talk, Toyota Production System, Uber for X, value engineering, warehouse automation, WeWork, winner-take-all economy

The reality is more complex and humbling. Companies usually fail because of the incompetence and arrogance of a complacent management team, not because they struggled to predict the future. Predicting the future may itself just be an exercise in futility. The coronavirus pandemic is a clear example of the random walk we take each day. And this is not a new phenomenon. When we were growing up in the 1980s, futurists predicted the widespread adoption of electric cars by the late 1990s. In fact, GM launched a concept electric car with the EV1 all the way back in 1996. Decades later, we are still in the early stages of adoption, with headlines that highlight the rapid pace of potential disruption.

Kneeland had invested heavily in IT right out of the Great Recession, and it paid off in improved operational efficiency, from paperwork, to logistics, to pricing. Pricing strategy was a substantial investment. For years, the industry had suffered from anecdotal, weak pricing dynamics. Jacobs and his successor had worked on better pricing, but it was still not systematic. It can be a little startling to see how “informal” large parts of the economy still are. Construction is one the biggest tech laggards: contractors still do business on paper forms, over the phone, and in texts. Ten years ago the rental industry was even less formal. Sunbelt, now the second largest rental company, used paper notebooks to communicate pricing to its rental branches, while United Rentals used mobile devices instead of paper, but neither had real pricing engines.

A $4 million mining truck looks like a small investment when profits are high. On the other hand, it also looks like a high-cost anchor on profits when copper prices are low. In the early 2000s China was at the inflection point of an amazing run of growth, and its need for iron, coal, and copper to fuel its infrastructure and factory surge was straining the global base of mine production. Copper prices that had been flat for 20 years reached new highs, then doubled, and rose 50 percent again after that. Gold prices reached prior highs as well. The price of metallurgical coal, used to make steel, rose more than five times. These are precisely the sorts of unpredictable compounding factors that can make demand for equipment explode.


pages: 290 words: 82,871

The Hidden Half: How the World Conceals Its Secrets by Michael Blastland

air freight, Alfred Russel Wallace, banking crisis, Bayesian statistics, behavioural economics, Berlin Wall, Brexit referendum, central bank independence, cognitive bias, complexity theory, Deng Xiaoping, Diane Coyle, Donald Trump, epigenetics, experimental subject, full employment, George Santayana, hindsight bias, income inequality, Jeremy Corbyn, manufacturing employment, mass incarceration, meta-analysis, minimum wage unemployment, nudge unit, oil shock, p-value, personalized medicine, phenotype, Ralph Waldo Emerson, random walk, randomized controlled trial, replication crisis, Richard Thaler, selection bias, the map is not the territory, the scientific method, The Wisdom of Crowds, twin studies

What’s more, as Wendy Johnson continues: ‘People. . . bring psychological content to their appraisal of situations, in the form of learned associations between particular aspects of situations and prior experiences. . . even individuals with identical trait levels can be expected to appraise the same situation in different ways.’ Our behaviour, beliefs and choices are clearly not a random walk. Nor are they as regular as perhaps we think they should be. Let’s push the principle a little further still, this time by testing the consistency of another human characteristic. Here’s an example in which an assumption of regularity seems easy: the skill of a surgeon. We might say that if you’re good, you’re good.

Two years later, the expansion was described by the Financial Times as ‘one of the worst deals struck on British shores, with total losses set to reach as high as $1bn’. Wesfarmers reportedly agreed to sell up for the token price of £1.2 ‘Homebase was a classic case of hubris,’ said a shareholder activist, Stephen Mayne, the FT reported, ‘Wesfarmers thought that if it works in Australia it will work elsewhere.’ But, among other differences, the Brits seemed to prefer soft furnishings and garden ornaments to the ‘macho culture of big barbies and power tools’. And they did not take quickly to the strategy of abandoning special promotions in favour of ‘everyday low prices’. At least, that was the favoured explanation in media post-mortems. Then why didn’t Wesfarmers spot the vital differences?

There are things that we do know, where the path ahead is clearly marked. It is no part of this book’s purpose to claim that all paths are equally open to all and only an impulse away. Equally, there are other paths that are far less knowable, no matter how much people flatter themselves that they’ve found the way – a discovery they tell you can be yours, for a price, a vote, or one more research grant. I hope it’s possible to argue that we have the balance wrong – that we think we know more than we do – without being taken to deny the role of systematic causal influences altogether, or to suggest that we should stop searching for them. This is an image of life stumbling through the dance as it goes along, with all its detailed, different, unforeseeable turning points.


The Golden Ratio: The Story of Phi, the World's Most Astonishing Number by Mario Livio

Albert Einstein, Albert Michelson, Alfred Russel Wallace, Benoit Mandelbrot, Brownian motion, Buckminster Fuller, classic study, cosmological constant, Elliott wave, Eratosthenes, Gödel, Escher, Bach, Isaac Newton, Johann Wolfgang von Goethe, Johannes Kepler, mandelbrot fractal, music of the spheres, Nash equilibrium, power law, Ralph Nelson Elliott, Ralph Waldo Emerson, random walk, Richard Feynman, Ronald Reagan, Thales of Miletus, the scientific method

However, for us actually to see the stars shining, bundles of radiation, known as photons, have to make their way from the stellar depths to the surface. Photons do not simply fly through the star at the speed of light. Rather, they bounce around, being scattered and absorbed and reemitted by all the electrons and atoms of gas in their way, in a seemingly random fashion. Yet the net result is that after a random walk, which in the case of the Sun takes some 10 million years, the radiation escapes the star. The power emitted by the Sun's surface determined (and continues to determine) the temperature on Earth's surface and allowed life to emerge. Viswanath's work and the research on random Fibonaccis that followed provide additional tools for the mathematical machinery that explains disordered systems.

The city, on the river Arno, was also proud of its excellent ironwork and shipyards. Pisa is best known today for its famous leaning tower, and the construction of this bell tower began during Fibonacci's youth. Clearly, all of this commercial frenzy required massive records of inventories and prices. Leonardo surely had the opportunity to watch various scribes as they were listing prices in Roman numerals and adding them up using an abacus. Arithmetic operations with Roman numerals are not fun. For example, to obtain the sum of 3,786 and 3,843, you would need to add MMMDCCLXXXVI to MMMDCC-CXLIII; if you think that is cumbersome, try multiplying those numbers.

Figure 127 Some recent books that attempt to apply Elliott's general ideas to actual trading strategies go even further. They use the Golden Ratio to calculate the extreme points of maximum and minimum that can be expected (although not necessarily reached) in market prices at the end of upward or downward trends (Figure 127). Even more sophisticated algorithms include a logarithmic spiral plotted on top of the daily market fluctuations, in an attempt to represent a relationship between price and time. All of these forecasting efforts assume that the Fibonacci sequence and the Golden Ratio somehow provide the keys to the operation of mass psychology. However, this “wave” approach does suffer from some shortcomings.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

"World Economic Forum" Davos, active measures, affirmative action, Albert Einstein, Amazon Mechanical Turk, AOL-Time Warner, Bear Stearns, behavioural economics, Black Swan, business cycle, butterfly effect, carbon credits, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, coherent worldview, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Future Shock, Geoffrey West, Santa Fe Institute, George Santayana, happiness index / gross national happiness, Herman Kahn, high batting average, hindsight bias, illegal immigration, industrial cluster, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Laplace demon, Long Term Capital Management, loss aversion, medical malpractice, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, Pierre-Simon Laplace, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, social contagion, social intelligence, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, tacit knowledge, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, Tragedy of the Commons, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

De Vany, Arthur. 2004. Hollywood Economics: How Extreme Uncertainty Shapes the Film Industry. London: Routledge. De Vany, Arthur, and W. David Walls. 1996. “Bose-Einstein Dynamics and Adaptive Contracting in the Motion Picture Industry.” The Economic Journal 106 (439):1493–1514. Denrell, Jerker. 2004. “Random Walks and Sustained Competitive Advantage.” Management Science 50 (7):922–34. Dholakia, Utpal M., and Silvia Vianello. 2009. “The Fans Know Best.” MIT Sloan Management Review, August 17. Diermeier, Daniel. 1996. “Rational Choice and the Role of Theory in Political Science.” In The Rational Choice Controversy: Economic Models of Politics Reconsidered, ed.

The relationship between our view of the past and our view of the future is illustrated in the figure on the facing page, which shows the stock price of a fictitious company over time. Looking back in time from the present, one sees the history of the stock (the solid line), which naturally traces out a unique path. Looking forward, however, all we can say about the stock price is its probability of falling within a particular range. My Yahoo! colleagues David Pennock and Dan Reeves have actually built an application that generates pictures like this one by mining data on the prices of stock options. Because the value of an option depends on the price of the underlying stock, the prices at which various options are being traded now can be interpreted as predictions about the price of the stock on the date when the option is scheduled to mature.

Because the value of an option depends on the price of the underlying stock, the prices at which various options are being traded now can be interpreted as predictions about the price of the stock on the date when the option is scheduled to mature. More precisely, one can use the option prices to infer various “probability envelopes” like those shown in the figure. For example, the inner envelope shows the range of prices within which the stock is likely to fall with a 20 percent probability, while the outer envelope shows the 60 percent probability range. We also know, however, that at some later time, the stock price will have been revealed—as indicated by the dotted “future” trajectory. At that time, we know the hazy cloud of probabilities defined by the envelope will have been replaced by a single, certain price at each time, just like prices that we can currently see in the past.


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, behavioural economics, Black Swan, butterfly effect, buy and hold, cloud computing, cognitive load, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, How many piano tuners are there in Chicago?, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, operational security, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, precautionary principle, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, tacit knowledge, tail risk, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

For one of the earliest demonstrations of excess volatility in asset market prices, see Robert Shiller, “Do Stock Prices Move Too Much to Be Justified by Subsequent Changes in Dividends?,” National Bureau of Economic Research Working Paper no. 456, 1980; Terrance Odean, “Do Investors Trade Too Much?,” American Economic Review 89, no. 5 (1999): 1279–98. 12. John Maynard Keynes, The General Theory of Employment, Interest, and Money (CreateSpace Independent Publishing Platform, 2011), p. 63. 13. Burton Malkiel, A Random Walk Down Wall Street, rev. and updated ed. (New York: W. W. Norton, 2012), p. 240. 14.

The commentary that superforecasters post on GJP forums is rife with “on the one hand/on the other” dialectical banter. And superforecasters have more than two hands. “On the one hand, Saudi Arabia runs few risks in letting oil prices remain low because it has large financial reserves,” wrote a superforecaster trying to decide if the Saudis would agree to OPEC production cuts in November 2014. “On the other hand, Saudi Arabia needs higher prices to support higher social spending to buy obedience to the monarchy. Yet on the third hand, the Saudis may believe they can’t control the drivers of the price dive, like the drilling frenzy in North America and falling global demand. So they may see production cuts as futile.

If I think a stock is a good value at a certain price, I may offer to buy yours. If you agree with my judgment, you won’t sell. If you think I’m wrong, you will. Of course, in reality, trades happen for other reasons—you and I may have different financial needs steering us in different directions—but in general markets create incentives for people to relentlessly second-guess each other. The aggregation of all those judgments—and the information they are based on—is expressed in the price. If many people agree with me that a stock is worth more than it’s selling for, they will try to buy it. Increasing demand pushes the price up. In that way, all the individual judgments of the buyers, and all the information guiding those judgments, becomes “priced in.”


pages: 337 words: 103,522

The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, stable marriage problem, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

His idea was to take the riffs of a jazz musician and, given a note, to analyse the probability of the next note. Let’s imagine a riff consisting of an ascending and descending scale. If you play a particular note, the chances are 50–50 that the next note will be one up or one down the scale. Based on this fact, the algorithm would do a random walk up and down the scale. The more riffs you gave it, the more data it would have to analyse and the more a particular style of playing would emerge. Pachet figured out that it wasn’t enough to look one note back, and it might take a few notes to know where to go next. But you don’t want the algorithm to reproduce the training data, so it’s no good going too far back.

When Eisen checked the next day to see if the prices had dropped to more sensible levels, he found instead that they’d gone up. Profnath now wanted $2,194,443.04 while bordeebook was asking a phenomenal $2,788,233.00. Eisen decided to put his scientific hat on and analyse the data. Over the next few days he tracked the changes in an effort to work out if there was some pattern to the strange prices. Eventually he spotted the mathematical rule behind the escalating prices. Divide the profnath price by the bordeebook price from the day before and you always got 0.99830. Divide the bordeebook price by the profnath book on the same day and you always got 1.27059.

Each seller had programmed their website to use an algorithm that was setting the prices for books they were selling. Each day the profnath algorithm would check the price of the book at bordeebook and would then multiply it by 0.99830. This algorithm made perfect sense because the seller was programming the site to slightly undercut the competition at bordeebook. It is the algorithm at bordeebook that is slightly more curious. It was programmed to detect any price change in its rival and to multiply this new price by a factor of 1.27059. The combined effect was that each day the price would be multiplied by 0.99830 × 1.27059, or 1.26843. This ensured that the price would grow exponentially.


pages: 372 words: 101,174

How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer age, Computing Machinery and Intelligence, Dean Kamen, discovery of DNA, double helix, driverless car, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Hans Moravec, Isaac Newton, iterative process, Jacquard loom, Jeff Hawkins, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Nick Bostrom, Norbert Wiener, optical character recognition, PalmPilot, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, selective serotonin reuptake inhibitor (SSRI), self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize

A classic example is the laws of thermodynamics (LOT). If you look at the mathematics underlying the LOT, it models each particle as following a random walk, so by definition we cannot predict where any particular particle will be at any future time. Yet the overall properties of the gas are quite predictable to a high degree of precision, according to the laws of thermodynamics. So it is with the law of accelerating returns: Each technology project and contributor is unpredictable, yet the overall trajectory, as quantified by basic measures of price/performance and capacity, nonetheless follows a remarkably predictable path. If computer technology were being pursued by only a handful of researchers, it would indeed be unpredictable.

The semiconductor industry’s “International Technology Roadmap for Semiconductors” projects seven-nanometer features by the early 2020s.2 At that point key features will be the width of thirty-five carbon atoms, and it will be difficult to continue shrinking them any farther. However, Intel and other chip makers are already taking the first steps toward the sixth paradigm, computing in three dimensions, to continue exponential improvement in price/performance. Intel projects that three-dimensional chips will be mainstream by the teen years; three-dimensional transistors and 3-D memory chips have already been introduced. This sixth paradigm will keep the LOAR going with regard to computer price/performance to a time later in this century when a thousand dollars’ worth of computation will be trillions of times more powerful than the human brain.3 (It appears that Allen and I are at least in agreement on what level of computation is required to functionally simulate the human brain.)4 Allen then goes on to give the standard argument that software is not progressing in the same exponential manner as hardware.

For an entertaining example of the complexity of human-generated language, just read one of the spectacular multipage-length sentences in a Gabriel García Márquez story or novel—his six-page story “The Last Voyage of the Ghost” is a single sentence and works quite well in both Spanish and the English translation.1 The primary idea in my three previous books on technology (The Age of Intelligent Machines, written in the 1980s and published in 1989; The Age of Spiritual Machines, written in the mid- to late 1990s and published in 1999; and The Singularity Is Near, written in the early 2000s and published in 2005) is that an evolutionary process inherently accelerates (as a result of its increasing levels of abstraction) and that its products grow exponentially in complexity and capability. I call this phenomenon the law of accelerating returns (LOAR), and it pertains to both biological and technological evolution. The most dramatic example of the LOAR is the remarkably predictable exponential growth in the capacity and price/performance of information technologies. The evolutionary process of technology led invariably to the computer, which has in turn enabled a vast expansion of our knowledge base, permitting extensive links from one area of knowledge to another. The Web is itself a powerful and apt example of the ability of a hierarchical system to encompass a vast array of knowledge while preserving its inherent structure.


pages: 335 words: 86,900

Empire of Ants: The Hidden Worlds and Extraordinary Lives of Earth's Tiny Conquerors by Susanne Foitzik, Olaf Fritsche

deep learning, epigenetics, megacity, microbiome, phenotype, random walk, trade route

., model species for studies on “exploding ants” (Hymenoptera, Formicidae), with biological notes and first illustrations of males of the Colobopsis cylindrica group. ZooKeys, 751, 1–40. computer scientists mimic the pheromone trails of ants to find the best online pathways Bonabeau, E. et al. (2000). Inspiration for optimization from social insect behaviour. Nature, 406, 39–42. Musco, C. et al. (2017). Ant-inspired density estimation via random walks. Proceedings of the National Academy of Sciences USA, 114, 10534–41. Werfel, J. et al. (2014). Designing collective behavior in a termite-inspired robot construction team. Science, 343, 754–58. in heavy rain, tetraponera binghami and cataulacus muticus drink the water that floods into their nests and spit or pee it out outside the nest entrance Kolay, S., & Annagiri, S. (2015).

Its colonies can boast as many as 15,000 queens, each producing new offspring—such that these societies can essentially live on forever. Going by its size, Deborah was able to calculate that the largest garden she examined was just over 800 years old. It’s certainly a successful arrangement that the ants and the plants have come to. Or is it? After all, if you do a deal with the devil, you have to pay the price! It’s not just humans who clear the rainforest and replace it with monoculture. These monotonous devil’s gardens in the heart of the jungle are the work of the Myrmelachista schumanni ant, which build their nests in swellings in plants, known as domatia (smaller image). Despite being created by ants, devil’s gardens suffer the same curse as man-made monocultures: They make a tempting buffet for hungry insects.

These snappy little helpers are still relied upon to keep trees clear of pests, so much so that farmers are happy to forgive them for biting them during the harvest—especially since the ants bring them a little extra income. In some regions in Asia, ant queen larvae are considered a delicacy, costing as much as twice the price of a good cut of beef. The worker brood is not quite so valuable and is sold as high-quality bird feed, known as kroto. And of course, ants are also much-coveted ingredients in traditional Indian and Chinese medicine, where they are used as an aphrodisiac—as well as to treat rheumatism. In Australia, with a little luck, you can even buy a caipirinha mixed with weaver ants instead of lemons.


pages: 410 words: 101,260

Originals: How Non-Conformists Move the World by Adam Grant

"World Economic Forum" Davos, Abraham Maslow, Albert Einstein, Apple's 1984 Super Bowl advert, availability heuristic, barriers to entry, behavioural economics, Bluma Zeigarnik, business process, business process outsourcing, Cass Sunstein, classic study, clean water, cognitive dissonance, creative destruction, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dean Kamen, double helix, Elon Musk, emotional labour, fear of failure, Firefox, George Santayana, Ignaz Semmelweis: hand washing, information security, Jeff Bezos, Jeff Hawkins, job satisfaction, job-hopping, Joseph Schumpeter, Kevin Roose, Kickstarter, Lean Startup, Louis Pasteur, Mahatma Gandhi, Mark Zuckerberg, meta-analysis, minimum viable product, Neil Armstrong, Nelson Mandela, Network effects, off-the-grid, PalmPilot, pattern recognition, Paul Graham, Peter Thiel, Ralph Waldo Emerson, random walk, risk tolerance, Rosa Parks, Saturday Night Live, Sheryl Sandberg, Silicon Valley, Skype, Steve Jobs, Steve Wozniak, Steven Pinker, TED Talk, The Wisdom of Crowds, women in the workforce

Their methods question conventional wisdom about the relative importance of intuition and analysis in assessing ideas, and about how we should weigh passion in evaluating the people behind those ideas. You’ll see why it’s so difficult for managers and test audiences to accurately evaluate new ideas, and how we can get better at deciding when to roll the dice. A Random Walk on the Creative Tightrope The inventor of the Segway is a technological whiz named Dean Kamen, whose closet is stocked with one outfit: a denim shirt, jeans, and work boots. When I asked venture capitalists to describe Kamen, the most common response was “Batman.” At sixteen, he took it upon himself to redesign a museum’s lighting system—and only then sought the chairman’s permission to implement it.

But until that moment, they had taken the status quo for granted, never questioning the default price. “The thought had never crossed my mind,” cofounder Dave Gilboa says. “I had always considered them a medical purchase. I naturally assumed that if a doctor was selling it to me, there was some justification for the price.” Having recently waited in line at the Apple Store to buy an iPhone, he found himself comparing the two products. Glasses had been a staple of human life for nearly a thousand years, and they’d hardly changed since his grandfather wore them. For the first time, Dave wondered why glasses had such a hefty price tag. Why did such a fundamentally simple product cost more than a complex smartphone?

By cutting out the middleman of the retailer, they had determined that they could sell pairs of glasses that normally cost $500 for $45. After a marketing expert warned them that their costs would increase—and that price was viewed as a sign of quality—they created a survey with mock product pages, randomly assigning customers to different price points. They found that the likelihood of purchase increased up to prices around $100, then plateaued and dropped in higher ranges. They tested different website designs with friends to see what would generate not only the most clicks, but also the strongest trust. Since other companies could sell glasses online, the founders realized that branding would be critical to their success.


pages: 407 words: 104,622

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

Members of Axcom’s team viewed investing through a math prism and understood financial markets to be complicated and evolving, with behavior that is difficult to predict, at least over long stretches—just like a stochastic process. It’s easy to see why they saw similarities between stochastic processes and investing. For one thing, Simons, Ax, and Straus didn’t believe the market was truly a “random walk,” or entirely unpredictable, as some academics and others argued. Though it clearly had elements of randomness, much like the weather, mathematicians like Simons and Ax would argue that a probability distribution could capture futures prices as well as any other stochastic process. That’s why Ax thought employing such a mathematical representation could be helpful to their trading models. Perhaps by hiring Carmona, they could develop a model that would produce a range of likely outcomes for their investments, helping to improve their performance.

Simons recruited his sister-in-law and others to input the prices into the database Hullender created to track prices and test various trading strategies based on both mathematical insights and the intuitions of Simons, Baum, and others. Many of the tactics they tried focused on various momentum strategies, but they also looked for potential correlations between commodities. If a currency went down three days in a row, what were the odds of it going down a fourth day? Do gold prices lead silver prices? Might wheat prices predict gold and other commodity prices? Simons even explored whether natural phenomena affected prices. Hullender and the team often came up empty, unable to prove reliable correlations, but Simons pushed them to keep searching.

Axcom had been employing various approaches to using their pricing data to trade, including relying on breakout signals. They also used simple linear regressions, a basic forecasting tool relied upon by many investors that analyzes the relationships between two sets of data or variables under the assumption those relationships will remain linear. Plot crude-oil prices on the x-axis and the price of gasoline on the y-axis, place a straight regression line through the points on the graph, extend that line, and you usually can do a pretty good job predicting prices at the pump for a given level of oil price. Market prices are sometimes all over the place, though.


pages: 827 words: 239,762

The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite by Duff McDonald

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Albert Einstein, Apollo 13, barriers to entry, Bayesian statistics, Bear Stearns, Bernie Madoff, Bob Noyce, Bonfire of the Vanities, business cycle, business process, butterfly effect, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, Clayton Christensen, cloud computing, collateralized debt obligation, collective bargaining, commoditize, compensation consultant, corporate governance, corporate raider, corporate social responsibility, creative destruction, deskilling, discounted cash flows, disintermediation, disruptive innovation, Donald Trump, eat what you kill, Fairchild Semiconductor, family office, financial engineering, financial innovation, Frederick Winslow Taylor, full employment, George Gilder, glass ceiling, Glass-Steagall Act, global pandemic, Gordon Gekko, hiring and firing, Ida Tarbell, impact investing, income inequality, invisible hand, Jeff Bezos, job-hopping, John von Neumann, Joseph Schumpeter, junk bonds, Kenneth Arrow, Kickstarter, Kōnosuke Matsushita, London Whale, Long Term Capital Management, market fundamentalism, Menlo Park, Michael Milken, new economy, obamacare, oil shock, pattern recognition, performance metric, Pershing Square Capital Management, Peter Thiel, planned obsolescence, plutocrats, profit maximization, profit motive, pushing on a string, Ralph Nader, Ralph Waldo Emerson, RAND corporation, random walk, rent-seeking, Ronald Coase, Ronald Reagan, Sam Altman, Sand Hill Road, Saturday Night Live, scientific management, shareholder value, Sheryl Sandberg, Silicon Valley, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Steve Jurvetson, survivorship bias, TED Talk, The Nature of the Firm, the scientific method, Thorstein Veblen, Tragedy of the Commons, union organizing, urban renewal, vertical integration, Vilfredo Pareto, War on Poverty, William Shockley: the traitorous eight, women in the workforce, Y Combinator

An all-state football and baseball player in high school, Porter earned an undergraduate degree from Princeton in mechanical and aerospace engineering, while also making the NCAA’s all-American team in golf. In search of adding a “practical” component to his education, he took the advice of one of his professors at Princeton, Burton Malkiel (’55), and decided to attend HBS. The author of A Random Walk Down Wall Street, Malkiel is one of the leading proponents of the efficient-market hypothesis, which argues that stock prices reflect all available information, that the market is all-knowing. Had Porter, who graduated a Baker Scholar in 1971, gone down the same road as Malkiel, into finance, he might have ended up an intellectual peer of the other influential Michael of his generation, Michael Jensen.

., 127, 335 Putnam, Robert, 56, 391 “Putting Integrity into Finance” (Jensen), 378 Qaddafi, Muammar al-, 405–7, 420, 567 Questrom School of Business, Boston University, 235 Raab, Sidney, 289 Radcliffe College, 238, 239: Harvard-Radcliffe Program in Business Administration, 151, 239, 240 Raiffa, Howard, 215–18, 336–37, 355 Rajaratnam, Raj, 211 RAND Corporation, 258, 272, 275 Random Walk Down Wall Street, A (Malkiel), 411 Rangan, V. Kasturi, 475 rational choice theory, 275 RCA, 335; HBS’s Executive Education and, 151 Reagan, Ronald, 160, 163, 371, 387, 419, 422, 430, 474 Redefining Corporate Soul (Cox), 492 Redefining Health Care (Porter), 421 “Re-engineering Work: Don’t Automate, Obliterate” (Hammer), 301 Reich, Robert, 425, 426–27 Reinhardt, Forest, 560 Relevance Lost (Kaplan and Johnson), 297, 443 Relevance Regained (Johnson), 446 “Remuneration” (Jensen and Murphy), 375 Replogle, John, 561 Rethinking the MBA (Datar et al), 564 Riesman, David, 184, 185–86, 350 Rio Tinto, 209, 543–44 Rise and Fall of Strategic Planning, The (Mintzberg), 262 Rivkin, Jan, 419, 425, 542 RJR Nabisco, 209, 371 Robinson, James D., III, 106, 128, 209, 255 Roby, Joe, 468 Rock, Arthur, 120, 319–21, 328, 329 Rockefeller, John D., 12, 28, 38, 44, 57, 77, 79, 373 Rockefeller, John D., Jr., 71, 142, 145, 220 Rockefeller, Laurence, 322 Rockefeller, Nelson, 289 Rockefeller Foundation, 90, 112, 211; as HBS benefactors, 81, 82–83 Rockefeller General Education Board (GEB), 44, 97 Roeder, George, 289 Roethlisberger, Fritz, 85, 89, 118, 222, 238, 308, 355 Romeo and Juliet (Shakespeare), 397 Rometty, Ginni, 404 Romney, George, 507 Romney, Mitt, 332, 419, 501, 506–10 Romney, Tagg, 419, 506 Roosevelt, Franklin Delano (FDR), 101–3, 123, 131–32, 136, 139, 200; New Deal, 122, 161, 192 Roosevelt, Theodore, 16, 22, 37, 244, 441 Roots, Rituals, and Rhetorics of Change, The (Augier and March), 220 Rose, Charlie, 17, 570 Rose, Clayton, 236 Rosenberg, Nathan, 535 Ross, Stephen M., 533 Rost, Joseph, 197 Rothenberg, Jim, 535 Rothkopf, David, 388 Rothrock, Ray, 322 Rotman School of Management, University of Toronto, 235, 283, 419 Rottenberg, Jennifer, 536 Ruane, Bill, 169 Rubin, Bob, 469 Rubio, Marco, 508 Rude Awakening (Keller), 247 Rudenstine, Neil, 423 Ruggles, Clyde, 131 Sabine, Wallace, 34 Sabrina (film), 183–84 Sachs, Samuel, 474 Sachs, Walter and Paul, 474 Saez, Emmanuel, 540, 544 Sahlman, William, 301, 328, 331, 332, 333, 480, 494 Salmon, Walt, 332, 333, 356 Salter, Malcolm, 521 Sandberg, Sheryl, 74, 241, 534 Sanders, Thomas, 116 Sargent, Ron, 333 Sarofim, Fayez, 328 Sass, Steven A., 286 Scale and Scope (Chandler), 14, 230, 246, 247, 248 Scalia, Antonin, 334 Schacht, Henry, 128 Schlaifer, Robert, 215, 216, 218, 355 Schlesinger, Leonard, 235 Schmoller, Gustav von, 21, 27, 48 School of Business Administration, University of Western Ontario, 228 Schumpeter, Joseph, 243, 244, 348 Schwarzman, Stephen, 76, 394, 466, 470, 531 Sculley, John, 320 Sears, F.

But having neutered unions, American managers decided to sever that tie, too. American productivity shot through the roof at the end of the twentieth century, but wages remained stagnant. “The advances were funneled directly into stock prices,” writes Frank. “The people who got richer as workers became more productive were stockholders.”19 And that has led to an increase in inequality to levels not seen in a century. In The Price of Inequality, economist Joseph Stiglitz points out that when unions were strongest in America—the three decades between 1949 and 1980—productivity and real hourly compensation moved together in manufacturing.20 But then the link was broken, and wages stopped growing, except for those at the very top.


pages: 232 words: 67,934

The Immortalization Commission: Science and the Strange Quest to Cheat Death by John Gray

Alfred Russel Wallace, anthropic principle, anti-communist, death from overwork, dematerialisation, disinformation, George Santayana, laissez-faire capitalism, Law of Accelerating Returns, life extension, Nikolai Kondratiev, public intellectual, random walk, Ray Kurzweil, scientific worldview, the scientific method

No doubt much of what we believe must be roughly accurate, or else we would not have survived. But the beliefs we have evolved might latch on to the world only enough to help us stumble our way through it, and then only for the time being. Human belief-systems could be useful illusions, appearing and disappearing as they prove to be more or less advantageous in the random walk of natural selection. Might not evolution be one of these illusions? Scientific naturalism is the theory that human beliefs are evolutionary adaptations whose survival has nothing to do with their truth. But in that case scientific naturalism is self-defeating, since on its own premises scientific theories cannot be known to be true.

By 1920 there were half a dozen shops left open in the centre of the city – a government store selling crockery, a few selling flowers; the rest had been abandoned, leaving boarded-up or broken windows and dusty bits of old stock. Electric light had disappeared, along with oil lamps; candles were made from animal fat. Milk, eggs and apples were being sold by peasants at street corners and railway stations. Shoelaces, blankets, spoons, forks, razor blades and medicines could not be bought at any price. People were dressed in scraps and remnants – hats were made from the felt that covered billiard tables, dresses from curtains and rugs turned into overcoats. Random death was everywhere. The bodies of people killed for their boots or jackets lay in the gutters. Horses lay dead in the road, picked at by dogs and crows.

To begin with she denied it – there had been an error in translation, and there was nothing that needed explaining. Then she did what she had done with Gorky – she confessed she had been planted on Wells by the Soviet secret police. She had no alternative, she said. For her working for the secret police was the price of life. Wells would not accept that Moura had no alternative. Were there no actions one must never do, whatever the consequences, actions it would be better to die than to commit? Unmoved by Wells’ challenge, she replied, laughing, with a question of her own. Had he not studied biology? Did he not know that survival was the first law of life?


pages: 344 words: 104,522

Woke, Inc: Inside Corporate America's Social Justice Scam by Vivek Ramaswamy

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, activist fund / activist shareholder / activist investor, affirmative action, Airbnb, Amazon Web Services, An Inconvenient Truth, anti-bias training, Bernie Sanders, Big Tech, BIPOC, Black Lives Matter, carbon footprint, clean tech, cloud computing, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, critical race theory, crony capitalism, cryptocurrency, defund the police, deplatforming, desegregation, disinformation, don't be evil, Donald Trump, en.wikipedia.org, Eugene Fama: efficient market hypothesis, fudge factor, full employment, George Floyd, glass ceiling, global pandemic, green new deal, hiring and firing, Hyperloop, impact investing, independent contractor, index fund, Jeff Bezos, lockdown, Marc Benioff, Mark Zuckerberg, microaggression, military-industrial complex, Network effects, Parler "social media", plant based meat, Ponzi scheme, profit maximization, random walk, ride hailing / ride sharing, risk-adjusted returns, Robert Bork, Robinhood: mobile stock trading app, Ronald Reagan, Salesforce, self-driving car, shareholder value, short selling, short squeeze, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, single source of truth, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, source of truth, sovereign wealth fund, Susan Wojcicki, the scientific method, Tim Cook: Apple, too big to fail, trade route, transcontinental railway, traveling salesman, trickle-down economics, Vanguard fund, Virgin Galactic, WeWork, zero-sum game

It’s reminiscent of the amount of money that piled into home loans as a result of mandates in the years leading up to the 2008 financial crisis. Good fundraising strategies don’t always make for good investment strategies. Asset prices may rise in the short run because there are more dollars chasing them due to the expectation they’ll keep rising. But that’s the logic of a Ponzi scheme. It’s what Burton Malkiel refers to as the “greater fool theory” in his book A Random Walk Down Wall Street. It’s a self-fulfilling prophecy: as long as there’s another buyer, the gravy train keeps running, yet it only lasts for so long. Who wins this game? Just like in 2008, the answer is large financial institutions.

In 2016, the pharma industry created an unofficial social contract to limit drug price increases to 10 percent per year. Allergan CEO Brent Saunders kicked it off by pledging to increase prices at most once per year and to do it by single digits. Other companies quickly fell in line. This created a new norm of all companies raising their prices by, of course, about 9.9 percent. A price increase of 10 percent per year compounding each year adds up quickly—but slowly enough to escape the public’s notice. The pharma industry’s insight was that it could continue to raise prices, but it needed to do it in stages rather than all at once.

The pharma industry’s insight was that it could continue to raise prices, but it needed to do it in stages rather than all at once. Then the companies could condition the public to accept price increases as normal while also somehow making themselves seem socially responsible by “only” raising them 10 percent. If government regulators stepped in, drug prices would be restricted far more. As one article put it, “As rising drug prices continue to be a major cause for discord in the US, the self-regulation of price hikes could help the industry avoid regulatory reform. With rising drug prices more constrained in 2017 and 2018, the pharma industry may be on its way to dodging federal regulation.”20 Stakeholder capitalism has created a world in which ordinary people not only expect but also assume that companies pursue not just their own interests but societal interests too.


pages: 310 words: 91,151

Leaving Microsoft to Change the World: An Entrepreneur's Odyssey to Educate the World's Children by John Wood

airport security, Alan Greenspan, Apollo 13, British Empire, call centre, clean water, corporate social responsibility, Deng Xiaoping, Donald Trump, fear of failure, glass ceiling, high net worth, income per capita, Jeff Bezos, Johann Wolfgang von Goethe, Marc Andreessen, microcredit, Own Your Own Home, random walk, rolodex, Salesforce, shareholder value, Silicon Valley, Skype, Steve Ballmer

Do you know the quote by Michael Porter from Harvard Business School?” Bill and Jenny nodded no. “He points out that in the entire history of the travel industry, nobody has ever washed a rental car. If they don’t feel ownership, they won’t do any long-term maintenance. That’s the way I feel about our projects.” The random walk through our business plan continued for a few more hours. Bill was voluble, enthusiastic, complimentary, and full of his own anecdotes about international development work. Obviously, the best approach was to give up control of the meeting. Bill would drive. After several more questions and digressions, Bill said that he was impressed and that he’d look forward to working with us.

I had adopted the commando lifestyle of a corporate warrior. Vacation was for people who were soft. Real players worked weekends, racked up hundreds of thousands of air miles, and built mini-empires within the expanding global colossus called Microsoft. Complainers simply did not care about the company’s future. I was, however, increasingly aware of the price I was paying. Relationships—starved of my time and attention—fell flat as a result. Family members grumbled when I canceled yet another Christmas reunion. I was a regular last-minute dropout for friends’ weddings. Whenever friends proposed an adventure trip, I would usually have an immovable meeting standing in my way.

Bill Gates, the world’s most successful living capitalist, was about to visit the most populous Communist stronghold. On the plane, drinking a lukewarm cup of Chinese tea and munching on steamed pork buns, I did a back-of-the-envelope calculation to determine the wealth disparity between Bill and the average Chinese. Using the Microsoft stock price as a proxy, Bill’s net worth on that day was about $60 billion, give or take a mere billion. In contrast, the average annual GNP per Chinese citizen was $725. Bill’s wealth was thus the equivalent to the annual earnings of 83 million Chinese citizens. By March of 1999, nobody really believed the old orthodoxy that the country was purely Communist.


pages: 375 words: 105,067

Pound Foolish: Exposing the Dark Side of the Personal Finance Industry by Helaine Olen

Alan Greenspan, American ideology, asset allocation, Bear Stearns, behavioural economics, Bernie Madoff, buy and hold, Cass Sunstein, Credit Default Swap, David Brooks, delayed gratification, diversification, diversified portfolio, Donald Trump, Elliott wave, en.wikipedia.org, estate planning, financial engineering, financial innovation, Flash crash, game design, greed is good, high net worth, impulse control, income inequality, index fund, John Bogle, Kevin Roose, London Whale, longitudinal study, low interest rates, Mark Zuckerberg, Mary Meeker, money market fund, mortgage debt, multilevel marketing, oil shock, payday loans, pension reform, Ponzi scheme, post-work, prosperity theology / prosperity gospel / gospel of success, quantitative easing, Ralph Nader, RAND corporation, random walk, Richard Thaler, Ronald Reagan, Saturday Night Live, Stanford marshmallow experiment, stocks for the long run, The 4% rule, too big to fail, transaction costs, Unsafe at Any Speed, upwardly mobile, Vanguard fund, wage slave, women in the workforce, working poor, éminence grise

Instead, she began to do in-person presentations, and not infrequently called in to San Francisco local talk radio shows. Finally, she turned to books. It had taken time for the book publishing industry to warm up to the personal finance and investment culture. That’s not to say they weren’t publishing books on the topic. Even prior to Sylvia Porter’s Money Book, there had been A Random Walk Down Wall Street by former Smith Barney analyst Burton Malkiel in 1973, followed by Andrew Tobias’s The Only Investment Guide You’ll Ever Need, both of which became huge bestsellers and are still in print today. Yet other titles would become bestsellers because they appealed to investors at a very specific moment in time, like former stockbroker and failed Evelyn-Wood-speed-reading franchise owner Howard Ruff’s surprise 1978 hit How to Prosper During the Coming Bad Years, which told readers that they needed to stock up on food and buy gold.

Because at the time I was trying to buy a house, too, and every open house I went to—no matter what the condition of the home, whether it was priced fairly or priced as if were still 1989—was a mob scene. There is something about real estate, something that makes us want it and want it bad, no matter what common sense suggests about its value. As Jane Bryant Quinn would write in Making the Most of Your Money, “A home of our own is still the rock on which our hopes are built. Price appreciation aside (and most houses will appreciate, eventually), homeownership is a state of mind. It’s your piece of earth. It’s where a family’s toes grow roots.”

Housing, health care, and education cost the average family 75 percent of their discretionary income in the 2000s. The comparable figure in 1973: 50 percent. And even as the cost of buying a house plunged in many areas of the country in the latter half of the 2000s (causing, needless to say, its own set of problems) the price of other necessary expenditures kept rising. The cost of medical services continued to increase at numbers far exceeding the rate of inflation, with the price of health insurance doubling in the period between 2001 and 2011, even as that insurance required steeper co-pays and deductibles from families. The cost of raising children increased a stunning 40 percent over the course of the first decade of the twenty-first century.


RDF Database Systems: Triples Storage and SPARQL Query Processing by Olivier Cure, Guillaume Blin

Amazon Web Services, bioinformatics, business intelligence, cloud computing, database schema, fault tolerance, folksonomy, full text search, functional programming, information retrieval, Internet Archive, Internet of things, linked data, machine readable, NP-complete, peer-to-peer, performance metric, power law, random walk, recommendation engine, RFID, semantic web, Silicon Valley, social intelligence, software as a service, SPARQL, sparse data, web application

The adopted graph perspective enables a navigation approach to query answering. That is, instead of relying on join algorithms, the system exploits some graph methods such as checking if a path exists between a set of nodes. This approach is also motivated by the ability to support novel query operations, such as to compute similarity between nodes, random walks, and community detection. The Enterprise edition is the most complete system version of the GraphDB family, formerly OWLIM. It aims at managing and synchronizing multiple GraphDB instances, namely the GraphDB standard edition, in a resilient and scalable cluster configuration. By doing so, it permits the design of parallel query processing.The architecture is based on a master–slave approach but permits several masters that can control several workers nodes.

Three main conditions exist: structured, semi-structured, and unstructured. Structured data implies a strict representation where data is organized in entities, and then similar entities are grouped together and are described with the same set of attributes, such as an identifier, price, brand, or color. This information is stored in an associated schema that provides a type to each attribute—for example, a price is a numerical value.The data organization of a relational database management system (RDBMS) is reminiscent of this approach. The notion of semi-structured data (i.e., self-described) also adopts an entitycentered organization but introduces some flexibility.

That line would contain all page numbers where the term appears. Most RDBMSs propose, through SQL operators, the creation of different types of indexes that are used for speeding up queries, for example, to efficiently access some values of a given attribute or to improve joins involving a set of attributes. The definition of indexes comes at a certain price corresponding to their maintenance cost.That is, whenever an update is performed on an indexed attribute (a.k.a. search key), both the relation and the index have to be properly modified.Within the book metaphor, 13 14 RDF Database Systems a change of the content involving an indexed term would imply an index maintenance, precisely to add or remove the page number of the content modification.


The Art of Computer Programming by Donald Ervin Knuth

Abraham Wald, Brownian motion, Charles Babbage, complexity theory, correlation coefficient, Donald Knuth, Eratosthenes, G4S, Georg Cantor, information retrieval, Isaac Newton, iterative process, John von Neumann, Louis Pasteur, mandelbrot fractal, Menlo Park, NP-complete, P = NP, Paul Erdős, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, sorting algorithm, Turing machine, Y2K

Notes: This exercise is based on the discovery by Vattulainen, Ala-Nissila, and Kankaala [Physical Review Letters 73 A994), 2513-2516] that a lagged Fibonacci generator fails a more complicated two-dimensional random walk test. Notice that the sequence Y2n, Y2n+2, ... will fail the test too, because it satisfies the same recurrence. The bias toward Is also carries over into the subsequence consisting of the even- valued elements generated by Xn = (Xn-55 ± Xn-2A) mod 2e; we tend to have more occurrences of (... 10J than (... 00J in binary notation. There's nothing magic about the number 79 in this test; experiments show that a significant bias towards a majority of Is is present also in random walks of length 101 or 1001 or 10001. But a formal proof seems to be difficult.

Since Algorithm B won't make a sequence any less random, and since it enhances the randomness with very little extra cost, it can be recommended for use in combination with any other random number generator. Shuffling methods have an inherent defect, however: They change only the order of the generated numbers, not the numbers themselves. For most purposes the order is the critical thing, but if a random number generator fails the "birthday spacings" test discussed in Section 3.3.2 or the random-walk test of exercise 3.3.2-31 it will not fare much better after it has been shuffled. Shuffling 3.2.2 OTHER METHODS 35 also has the comparative disadvantage that it does not allow us to start at a given place in the period, or to skip quickly from Xn to Xn+k for large k. Many people have therefore suggested combining two sequences {Xn) and (Yn) in a much simpler way, which avoids both of the defects of shuffling: We can use a combination like Zn = (Xn-Yn)modm A5) when 0 < Xn < m and 0 < Yn < m' < m.

[MUi] The recurrence Yn = (Y"n_24 + Yn-55) mod 2, which describes the least significant bits of the lagged Fibonacci generator 3.2.2-G) as well as the second-least significant bits of 3.2.2-G'), is known to have period length 255 —1; hence every possible nonzero pattern of bits (Y™, Y™+i,..., Yn+54) occurs equally often. Nevertheless, prove that if we generate 79 consecutive random bits Yn, ..., Y"n+78 starting at a random point in the period, the probability is more than 51% that there are more Is than 0s. If we use such bits to define a "random walk" that moves to the right when the bit is 1 and to the left when the bit is 0, we'll finish to the right of our starting point significantly more than half of the time. [Hint: Find the generating function X^ILo Pr(^nH hYn+78 = k) zk.] 32. [M20] True or false: If X and Y are independent, identically distributed random variables with mean 0, and if they are more likely to be positive than negative, then X + Y is more likely to be positive than negative. 33.


pages: 298 words: 43,745

Understanding Sponsored Search: Core Elements of Keyword Advertising by Jim Jansen

AltaVista, AOL-Time Warner, barriers to entry, behavioural economics, Black Swan, bounce rate, business intelligence, butterfly effect, call centre, Claude Shannon: information theory, complexity theory, content marketing, correlation does not imply causation, data science, en.wikipedia.org, first-price auction, folksonomy, Future Shock, information asymmetry, information retrieval, intangible asset, inventory management, life extension, linear programming, longitudinal study, machine translation, megacity, Nash equilibrium, Network effects, PageRank, place-making, power law, price mechanism, psychological pricing, random walk, Schrödinger's Cat, sealed-bid auction, search costs, search engine result page, second-price auction, second-price sealed-bid, sentiment analysis, social bookmarking, social web, software as a service, stochastic process, tacit knowledge, telemarketer, the market place, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Vickrey auction, Vilfredo Pareto, yield management

BAM: Branding, Advertising, and Marketing 133 For sponsored search, price is one key product attribute for which a consumer close to the purchase phase will search online. There are many attention-getting keyphrases related to price, including discount, sale, and free. Potpourri: Pricing a product or service can get really tricky. Two examples to illustrate this are prestige pricing and fractional pricing. Prestige price is the practice of charging higher prices for goods or services to give the impression to the consumer that there is added value for the cost. Prestige pricing capitalizes on people’s notions that correlate price with quality. So, a high-priced product is viewed superior in quality to a similar product priced for significantly less.

The more experienced readers can certainly skip Chapter 1, which lays out the context of the rest of the book, although it is a short read and will not take much time. So, I encourage you to take the few minutes to read it. How is the book organized? I have partitioned the subject of sponsored search in rather precise chapters. I am not in favor of the books on Web subjects that come across as “random walks on the Internet” or “look at the Web pages that I browsed.” The separate chapters are somewhat artificially walled, but I have simultaneously attempted to integrate the chapters into a coherent whole. Therefore, although the chapters are stand-alone, the book is a consistent work. To make each chapter stand alone, there are a few instances where I must repeat a concept across multiple chapters.

For sponsored search, advertisers must isolate their products’ features, attributes, or offerings to relate the product to the target audience. Price.╇ Price is the amount of currency for which a business will sell its product. There are several pricing schemes, ranging from a set price paid in full at the time of purchase to an installment with interest over time. The price is one of the key elements tied to a product, and it is symbolic of several product characteristics in the mind of the customer. These characteristics can include status, quality, and value. As such, businesses should continually examine the price set for their products and services to ensure the price is appropriate for the target market segment.


pages: 406 words: 109,794

Range: Why Generalists Triumph in a Specialized World by David Epstein

Airbnb, Albert Einstein, Apollo 11, Apple's 1984 Super Bowl advert, Atul Gawande, Checklist Manifesto, Claude Shannon: information theory, Clayton Christensen, clockwork universe, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, deep learning, deliberate practice, Exxon Valdez, fail fast, Flynn Effect, Freestyle chess, functional fixedness, game design, Gene Kranz, Isaac Newton, Johannes Kepler, knowledge economy, language acquisition, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, messenger bag, meta-analysis, Mikhail Gorbachev, multi-armed bandit, Nelson Mandela, Netflix Prize, pattern recognition, Paul Graham, precision agriculture, prediction markets, premature optimization, pre–internet, random walk, randomized controlled trial, retrograde motion, Richard Feynman, Richard Feynman: Challenger O-ring, Silicon Valley, Silicon Valley billionaire, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, sunk-cost fallacy, systems thinking, Walter Mischel, Watson beat the top human players on Jeopardy!, Y Combinator, young professional

., “The Effect of Malaria Control on Plasmodium falciparum in Africa Between 2000 and 2015,” Nature 526 (2015): 207–11. the label NBGBOKFO: G. Watts, “Obituary: Oliver Smithies,” Lancet 389 (2017): 1004. Scotch tape to rip thin layers of graphite: Geim details the discovery in his aptly titled Nobel Lecture, “Random Walk to Graphene” (December 8, 2010). Among the cleverly titled lecture sections: “Zombie Management,” “Better to Be Wrong Than Boring,” and “Legend of Scotch Tape.” stronger than steel: C. Lee et al., “Measurement of the Elastic Properties and Intrinsic Strength of Monolayer Graphene,” Science 321 (2008): 385–8.

(In economic bubbles, consumers buy stocks or property with the idea that the price will increase; that buying causes the price to increase, which leads to more buying. When ice caps melt, they reflect less sunlight back to space, which warms the planet, causing more ice to melt.) Or perhaps you would put the act of sweating and actions of the Federal Reserve together as negative-feedback loops. (Sweating cools the body so that more sweating is no longer required. The Fed lowers interest rates to spur the economy; if the economy grows too quickly, the Fed raises rates to slow down the activity it launched.) The way gas prices lead to an increase in grocery prices and the steps needed for a message to traverse neurons in your brain are both examples of causal chains, where one event leads to another, which leads to another, in linear order.

Ehrlich could choose five metals that he expected to become more expensive as resources were depleted and chaos ensued over the next decade. The material stakes were $1,000 worth of Ehrlich’s five metals. If, ten years hence, prices had gone down, Ehrlich would have to pay the price difference to Simon. If prices went up, Simon would be on the hook for the difference. Ehrlich’s liability was capped at $1,000, whereas Simon’s risk had no roof. The bet was made official in 1980. In October 1990, Simon found a check for $576.07 in his mailbox. Ehrlich got smoked. The price of every one of the metals declined. Technological change not only supported a growing population, but the food supply per person increased year after year, on every continent.


pages: 395 words: 116,675

The Evolution of Everything: How New Ideas Emerge by Matt Ridley

"World Economic Forum" Davos, adjacent possible, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Alfred Russel Wallace, AltaVista, altcoin, An Inconvenient Truth, anthropic principle, anti-communist, bank run, banking crisis, barriers to entry, bitcoin, blockchain, Boeing 747, Boris Johnson, British Empire, Broken windows theory, carbon tax, Columbian Exchange, computer age, Corn Laws, cosmological constant, cotton gin, creative destruction, Credit Default Swap, crony capitalism, crowdsourcing, cryptocurrency, David Ricardo: comparative advantage, demographic transition, Deng Xiaoping, discovery of DNA, Donald Davies, double helix, Downton Abbey, driverless car, Eben Moglen, Edward Glaeser, Edward Lorenz: Chaos theory, Edward Snowden, endogenous growth, epigenetics, Ethereum, ethereum blockchain, facts on the ground, fail fast, falling living standards, Ferguson, Missouri, financial deregulation, financial innovation, flying shuttle, Frederick Winslow Taylor, Geoffrey West, Santa Fe Institute, George Gilder, George Santayana, Glass-Steagall Act, Great Leap Forward, Greenspan put, Gregor Mendel, Gunnar Myrdal, Henri Poincaré, Higgs boson, hydraulic fracturing, imperial preference, income per capita, indoor plumbing, information security, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Isaac Newton, Jane Jacobs, Japanese asset price bubble, Jeff Bezos, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Khan Academy, knowledge economy, land reform, Lao Tzu, long peace, low interest rates, Lyft, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, meta-analysis, military-industrial complex, mobile money, Money creation, money: store of value / unit of account / medium of exchange, Mont Pelerin Society, moral hazard, Necker cube, obamacare, out of africa, packet switching, peer-to-peer, phenotype, Pierre-Simon Laplace, precautionary principle, price mechanism, profit motive, RAND corporation, random walk, Ray Kurzweil, rent-seeking, reserve currency, Richard Feynman, rising living standards, road to serfdom, Robert Solow, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, scientific management, Second Machine Age, sharing economy, smart contracts, South Sea Bubble, Steve Jobs, Steven Pinker, Stuart Kauffman, tacit knowledge, TED Talk, The Wealth of Nations by Adam Smith, Thorstein Veblen, transaction costs, twin studies, uber lyft, women in the workforce

Rodriguez found he got 80 per cent of the way through a library of a thousand different metabolic pathways at his first attempt, never having to change more than one step at a time and never producing a metabolic pathway that did not work. ‘When João showed me the answer, my first reaction was disbelief,’ wrote Wagner. ‘Worried that this might be a fluke, I asked João for many more random walks, a thousand more, each preserving metabolic meaning, each leading as far as possible, each leaving in a different direction.’ Same result. Wagner and Rodriguez had stumbled upon a massive redundancy built into the biochemistry of bacteria – and people. Using the metaphor of a ‘Library of Mendel’, in which imaginary building are stored the unimaginably vast number of all possible genetic sequences, Wagner identified a surprising pattern.

Political liberation and economic liberation went hand in hand. Small government was a radical, progressive proposition. Between 1660 and 1846, in a vain attempt to control food prices by prescription, the British government had enacted an astonishing 127 Corn Laws, imposing not just tariffs, but rules about storage, sale, import, export and quality of grain and bread. In 1815, to protect landowners as grain prices fell from Napoleonic wartime highs, it had banned the import of all grain if the price fell below eighty shillings a quarter (twenty-eight pounds). This led to an impassioned pamphlet from the young theorist of free trade David Ricardo, but in vain (his friend and supporter of the Corn Law, Robert Malthus, was more persuasive).

‘This is not the free market at work, but a gross, unintended economic distortion caused by the colossal government intervention.’ The strange obsession that politicians have with determining the price of one currency in terms of another, rather than letting such a price emerge, has always baffled me. Britain, in particular, has a long history of crises caused by the mispricing of exchange rates. In 1925, Winston Churchill, as Chancellor of the Exchequer, took Britain back onto the gold standard at the wrong price, precipitating a recession. In 1967, James Callaghan resisted too long before devaluing the pound. In 1992, Norman Lamont tried to cling to a fixed rate of exchange with the deutschemark.


pages: 393 words: 115,217

Loonshots: How to Nurture the Crazy Ideas That Win Wars, Cure Diseases, and Transform Industries by Safi Bahcall

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, AOL-Time Warner, Apollo 11, Apollo 13, Apple II, Apple's 1984 Super Bowl advert, Astronomia nova, behavioural economics, Boeing 747, British Empire, Cass Sunstein, Charles Lindbergh, Clayton Christensen, cognitive bias, creative destruction, disruptive innovation, diversified portfolio, double helix, Douglas Engelbart, Douglas Engelbart, Dunbar number, Edmond Halley, Gary Taubes, Higgs boson, hypertext link, industrial research laboratory, invisible hand, Isaac Newton, Ivan Sutherland, Johannes Kepler, Jony Ive, knowledge economy, lone genius, Louis Pasteur, Mark Zuckerberg, Menlo Park, Mother of all demos, Murray Gell-Mann, PageRank, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, power law, prediction markets, pre–internet, Ralph Waldo Emerson, RAND corporation, random walk, reality distortion field, Richard Feynman, Richard Thaler, Sheryl Sandberg, side project, Silicon Valley, six sigma, stem cell, Steve Jobs, Steve Wozniak, synthetic biology, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tim Cook: Apple, tulip mania, Wall-E, wikimedia commons, yield management

In the beginning, there were efficient markets. Markets capture all information into their prices; deviations from efficient prices are random (often called “random walks”). Bad actors can spoil the show (insider trading; manipulation), but with good behavior and proper enforcement, markets will revert to pure, perfectly efficient form. Much of modern finance theory, including estimates of risk and the pricing of stock options, is based on this belief. Real markets, however, don’t seem to work this way. Price movements that should happen once a year instead happen daily. Stock exchanges in New York, London, Paris, and Tokyo all show the same pattern.

Futureworld and From Tubby to PIC: 3D in Utah: Catmull, 16–17; Price, 10–15; Rubin, 106–13. Schure: Price, 16–29; Rubin, 103–33. Lucas: Price, 30–35; Rubin, 137–41. The PIC: Catmull, 30; Linzmayer, 225–28. Alan Kay: Catmull, 39; Price, 64–66; Rubin, 298–99. Jobs buys Pixar: Catmull, 41–44; Linzmayer, 218–19; Price, 61, 72–74; Rubin, 411–13. Quotations: “wasted two years”: Rubin, 130. “office of the future”: Price, 20; A. Smith, 13–14. “madman in Long Island”: Rubin, 121. “the house of Utah”: A. Smith, 17. “turns out to be a minuscule market”: Miller. “compete with Apple”: Catmull, 41. “after the divorce”: Price, 67. “How could GM”: Price, 73. “at the Movies”: Wilson.

That behavior cannot be defined or explained by studying a water molecule on its own. (2) Traffic flow: Cars on a highway may flow smoothly with no interruption or they may jam in response to small disruptions. Those emergent behaviors don’t depend on the details of the cars or the drivers. (3) Markets: Prices adjust to demand and resources tend to be allocated efficiently, regardless of what buyers are buying or sellers are selling. Unlike fundamental laws, emergent behaviors can suddenly change. When monopolies or cartels appear in a market, for example, prices may no longer adjust to demand and resources may no longer be allocated efficiently. False Fail* When a valid hypothesis yields a negative result in an experiment because of a flaw in the design of the experiment.


pages: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

., Vogel, D. and Dussutour, A. 2016. ‘Habituation in non-neural organisms: evidence from slime moulds.’ In Proc. R. Soc. B, vol. 283, no. 1829, p. 20160446. The Royal Society. 13 I look at this in more detail in this paper: Ma, Q., Johansson, A., Tero, A., Nakagaki, T. and Sumpter, D. J. T. 2013. ‘Current-reinforced random walks for constructing transport networks.’ Journal of the Royal Society Interface 10, no. 80: 20120864. 14 Baker, M. D. and Stock, J. B. 2007. ‘Signal transduction: networks and integrated circuits in bacterial cognition.’ Current Biology 17, no. 23: R1021–4. 15 Turing, A. M. 1950. ‘Computing machinery and intelligence.’

It allows its members to place small bets on the outcome of political events, such as ‘which party will win Ohio in the 2016 presidential election?’ or ‘how many @realDonaldTrump tweets will mention “CNN” from noon 7/6 to noon 7/13?’ Shares in events are traded directly between its users, with the price of an outcome reflecting the probability of that event. If the price for the market that Trump will tweet about CNN more than five times in that week is 40 cents, and I believe that the probability of him making six or more tweets is more than 41 per cent, then I can buy into the option. I pay 40 cents and if Trump does make more than five CNN tweets my investment becomes one dollar.

There is mathematics involved in the process of modelling football, but a gambler who thinks they can beat the market without incorporating the wisdom already held within the betting crowd, is going to lose eventually.13 This same ‘you can’t beat the bookies without playing their game’ logic applies to Nate’s work. He admits that his sports models don’t beat the bookmakers’ odds. Punters incorporate both the FiveThirtyEight predictions and other relevant information into the market price. They always have the edge over one individual, however clever they happen to be. Mona learnt a lot from her experience at FiveThirtyEight, but not in the way she had imagined when she started. She went into the job hoping to develop her skills in data journalism but came out understanding that the accuracy offered by FiveThirtyEight was an illusion.


pages: 416 words: 112,268

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

As E. coli floats about in its liquid home—your lower intestine—it alternates between rotating its flagella clockwise, causing it to “tumble” in place, and counterclockwise, causing the flagella to twine together into a kind of propeller so the bacterium swims in a straight line. Thus, E. coli does a sort of random walk—swim, tumble, swim, tumble—that allows it to find and consume glucose rather than staying put and dying of starvation. If this were the whole story, we wouldn’t say that E. coli is particularly intelligent, because its actions would not depend in any way on its environment. It wouldn’t be making any decisions, just executing a fixed behavior that evolution has built into its genes.

if Harriet believes there is no coffee available nearby or that it is exorbitantly expensive. Therefore, when Harriet says, “Fetch me a cup of coffee!” Robbie infers not just that Harriet wants coffee but also that Harriet believes there is coffee available nearby at a price she is willing to pay. Thus, if Robbie finds coffee at a price that seems reasonable (that is, a price that it would be reasonable for Harriet to expect to pay) he can go ahead and buy it. On the other hand, if Robbie finds that the nearest coffee is two hundred miles away or costs twenty-two dollars, it might be reasonable for him to report this fact rather than pursue his quest blindly.

Once we get to wide rollers and spray guns—the equivalent of a paintbrush about a meter wide—the price goes down considerably, but demand may begin to saturate so the number of housepainters drops somewhat. When one person manages a team of one hundred housepainting robots—the productivity equivalent of a paintbrush one hundred meters wide—then whole houses can be painted in an hour and very few housepainters will be working. Thus, the direct effects of technology work both ways: at first, by increasing productivity, technology can increase employment by reducing the price of an activity and thereby increasing demand; subsequently, further increases in technology mean that fewer and fewer humans are required.


Hedgehogging by Barton Biggs

activist fund / activist shareholder / activist investor, Alan Greenspan, asset allocation, backtesting, barriers to entry, Bear Stearns, Big Tech, book value, Bretton Woods, British Empire, business cycle, buy and hold, diversification, diversified portfolio, eat what you kill, Elliott wave, family office, financial engineering, financial independence, fixed income, full employment, global macro, hiring and firing, index fund, Isaac Newton, job satisfaction, junk bonds, low interest rates, margin call, market bubble, Mary Meeker, Mikhail Gorbachev, new economy, oil shale / tar sands, PalmPilot, paradox of thrift, Paul Samuelson, Ponzi scheme, proprietary trading, random walk, Reminiscences of a Stock Operator, risk free rate, Ronald Reagan, secular stagnation, Sharpe ratio, short selling, Silicon Valley, transaction costs, upwardly mobile, value at risk, Vanguard fund, We are all Keynesians now, zero-sum game, éminence grise

Meanwhile angry college professors would be publishing articles in the Wall Street Journal about efficient markets, coin flipping, zero-sum games, and how the contest really was a random walk. Of course the contestants would be replying that if it can’t be done, how come there are 32 of us who have done it? In the weeks before the round of 16, the winners would be much in demand by attractive members of the opposite sex, and some of them would be pricing ski houses in Aspen and condominiums in Florida. As I remember, the point of this somewhat farfetched analogy was that investment superstars are similar in a way to the finalists in the national coin-flipping contest.

China was crucial, because it alone accounted for 31% of the increase in global oil consumption between 1992 and 2002 and more than 50% in 2003. Change at the margin, we argued, was what drives prices. We constructed an elaborate oil price regression model that showed the equilibrium or fair value price of oil at $32.48 to be ridiculously precise. Most energy experts maintained it was even lower, pricing out somewhere in the high 20s. At the same time, bullish sentiment on oil was very elevated, and the open interest in crude oil futures was huge.We believed that much of the open interest represented speculative longs that were in the trade because of trend-following models.

For example the Imperial Palace grounds alone had a value in excess of all ccc_biggs_ch09_119-132.qxd 126 11/29/05 7:02 AM Page 126 HEDGEHOGGING FIGURE 9.2 Price-to-Book Japan Busted Bubbles Are Symmetrical MSCI Japan Price-to-Book Ratio: January 1975–May 2003 5.5 5.0 4.5 4.0 3.5 3.0 2.5 2.0 1.5 1.0 1975 1980 1985 1990 1995 2000 Source: Traxis Partners Quantitative Research, MSCI the real estate in the state of California. When the Japanese bubble burst, the wealth of the nation was diminished by roughly 50%. In the United States, by contrast, the loss of paper wealth in equities was offset by the continuing rise in home prices.That’s a huge difference. Enormous, sustained price advances in fixed assets always spawn speculation with borrowed money, and the Japanese banks fell all over themselves in their eagerness to make loans to property developers.As a result the bursting of the Japanese real estate bubble and the inevitable bad-loan hangover has crippled the Japanese banking system, which in turn has caused deflation and made the Japanese recession much longer and more painful.


pages: 416 words: 106,582

This Will Make You Smarter: 150 New Scientific Concepts to Improve Your Thinking by John Brockman

23andMe, adjacent possible, Albert Einstein, Alfred Russel Wallace, Anthropocene, banking crisis, Barry Marshall: ulcers, behavioural economics, Benoit Mandelbrot, Berlin Wall, biofilm, Black Swan, Bletchley Park, butterfly effect, Cass Sunstein, cloud computing, cognitive load, congestion charging, correlation does not imply causation, Daniel Kahneman / Amos Tversky, dark matter, data acquisition, David Brooks, delayed gratification, Emanuel Derman, epigenetics, Evgeny Morozov, Exxon Valdez, Flash crash, Flynn Effect, Garrett Hardin, Higgs boson, hive mind, impulse control, information retrieval, information security, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Jaron Lanier, Johannes Kepler, John von Neumann, Kevin Kelly, Large Hadron Collider, lifelogging, machine translation, mandelbrot fractal, market design, Mars Rover, Marshall McLuhan, microbiome, Murray Gell-Mann, Nicholas Carr, Nick Bostrom, ocean acidification, open economy, Pierre-Simon Laplace, place-making, placebo effect, power law, pre–internet, QWERTY keyboard, random walk, randomized controlled trial, rent control, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, Satyajit Das, Schrödinger's Cat, scientific management, security theater, selection bias, Silicon Valley, Stanford marshmallow experiment, stem cell, Steve Jobs, Steven Pinker, Stewart Brand, Stuart Kauffman, sugar pill, synthetic biology, the scientific method, Thorstein Veblen, Turing complete, Turing machine, twin studies, Vilfredo Pareto, Walter Mischel, Whole Earth Catalog, WikiLeaks, zero-sum game

What, if not fidelity, explains stability? Well, bits of culture—memes, if you want to dilute the notion and call them that—remain self-similar not because they are replicated again and again but because variations that occur at almost every turn in their repeated transmission, rather than resulting in “random walks” drifting away in all directions from an initial model, tend to gravitate around cultural attractors. Ending Little Red Riding Hood when the wolf eats the child would make for a simpler story to remember, but a Happy Ending is too powerful a cultural attractor. If someone had heard only the story ending with the wolf’s meal, my guess is that either she would not have retold it at all (and that is selection) or she would have modified it by reconstructing a happy ending (and that is attraction).

Between 2:42 P.M. and 2:50 P.M. on May 6, 2010, the Dow-Jones Industrial Average experienced a rapid decline and subsequent rebound of nearly six hundred points, an event of unprecedented magnitude and brevity. This disruption occurred as part of a tumultuous event on that day now known as the Flash Crash, which affected numerous market indices and individual stocks, even causing some stocks to be priced at unbelievable levels (e.g., Accenture was at one point priced at $.01). With tick-by-tick data available for every trade, we can watch the crash unfold in slow motion, a film of a financial calamity. But the cause of the crash itself remains a mystery. The U.S. Securities & Exchange Commission report on the Flash Crash was able to identify the trigger event (a $4 billion sale by a mutual fund) but could provide no detailed understanding of why this event caused the crash.

Rounded numbers are cultural attractors: They are easier to remember and provide better symbols for magnitudes. So we celebrate twentieth wedding anniversaries, hundredth issues of journals, the millionth copy sold of a record, and so on. This, in turn, creates a special cultural attractor for prices, just below rounded numbers—$9.99 or $9,990 are likely price tags—so as to avoid the evocation of a higher magnitude. In the diffusion of techniques and artifacts, efficiency is a powerful cultural attractor. Paleolithic hunters learning from their elders how to manufacture and use bows and arrows were aiming not so much at copying the elders as at becoming themselves as good as possible at shooting arrows.


pages: 913 words: 265,787

How the Mind Works by Steven Pinker

affirmative action, agricultural Revolution, Alfred Russel Wallace, Apple Newton, backpropagation, Buckminster Fuller, cognitive dissonance, Columbine, combinatorial explosion, complexity theory, computer age, computer vision, Computing Machinery and Intelligence, Daniel Kahneman / Amos Tversky, delayed gratification, disinformation, double helix, Dr. Strangelove, experimental subject, feminist movement, four colour theorem, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, greed is good, Gregor Mendel, hedonic treadmill, Henri Poincaré, Herman Kahn, income per capita, information retrieval, invention of agriculture, invention of the wheel, Johannes Kepler, John von Neumann, lake wobegon effect, language acquisition, lateral thinking, Linda problem, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mikhail Gorbachev, Murray Gell-Mann, mutually assured destruction, Necker cube, out of africa, Parents Music Resource Center, pattern recognition, phenotype, Plato's cave, plutocrats, random walk, Richard Feynman, Ronald Reagan, Rubik’s Cube, Saturday Night Live, scientific worldview, Search for Extraterrestrial Intelligence, sexual politics, social intelligence, Steven Pinker, Stuart Kauffman, tacit knowledge, theory of mind, Thorstein Veblen, Tipper Gore, Turing machine, urban decay, Yogi Berra

People clamor for bans on pesticide residues and food additives, though they pose trivial risks of cancer compared to the thousands of natural carcinogens that plants have evolved to deter the bugs that eat them. • People feel that if a roulette wheel has stopped at black six times in a row, it’s due to stop at red, though of course the wheel has no memory and every spin is independent. A large industry of self-anointed seers hallucinate trends in the random walk of the stock market. Hoop fans believe that basketball players get a “hot hand,” making baskets in clusters, though their strings of swishes and bricks are indistinguishable from coin flips. • This problem was given to sixty students and staff members at Harvard Medical School: “If a test to detect a disease whose prevalence is 1/1000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease, assuming you know nothing about the person’s symptoms or signs?”

The same is true of the hot-hand illusion and other fallacies among sports fans. If basketball shots were easily predictable, we would no longer call basketball a sport. An efficient stock market is another invention designed to defeat human pattern detection. It is set up to let traders quickly capitalize on, hence nullify, deviations from a random walk. Other so-called fallacies may also be triggered by evolutionary novelties that trick our probability calculators, rather than arising from crippling design defects. “Probability” has many meanings. One is relative frequency in the long run. “The probability that the penny will land heads is .5” would mean that in a hundred coin flips, fifty will be heads.

She thinks he will because she thinks he thinks she thinks he will. And so on. There always is a range of prices that the buyer and seller would both accept. Even if a particular price within that range is not the best price for one party, it is preferable to canceling the deal outright. Each side is vulnerable to being forced to settle for the worst acceptable price because the other side realizes that he or she would have no choice if the alternative was to reach no agreement at all. But when both parties can guess the range, any price within the range is a point from which at least one party would have been willing to back off, and the other party knows it.


pages: 746 words: 221,583

The Children of the Sky by Vernor Vinge

air gap, combinatorial explosion, epigenetics, indoor plumbing, megacity, MITM: man-in-the-middle, power law, random walk, risk tolerance, technological singularity, the scientific method, Vernor Vinge

The tiny onboard archive comprised the technological tricks of myriads Slow Zone races. Humankind on Earth had taken four thousand years to go from the smelting of iron to interstellar travel. That had been more or less a random walk. In the wars and catastrophes that followed, humans were like most races. They had blown themselves back to the medieval many times, and sometimes to the Neolithic, and, on a few worlds, even to extinction. But—at least where humankind survived at all—the way back to technology had been no random walk. Once the archeologists dug up the libraries, renaissance was a matter of a few centuries. With Oobii, she could cut that recovery time down to less than a century.

Sometimes he claimed he stayed with her because in a year she did as many weird things as he would see in ten anywhere else. Pilgrim really was a pilgrim, so that was an extreme claim indeed. His memories went back centuries, hazing off into unreliable history and myth. Few packs had traveled their world so much, or seen so much. The price of the adventuring was that Pilgrim was more a surviving point of view than an enduring mind. It was Johanna’s great good fortune that that point of view was currently embodied in someone whose attitudes were so basically decent. Of all the Tines in the world, Pilgrim and Scriber had been the first she’d known.

We humans were only the third most numerous. But we were popular. There were tourist towns that imitated olden human times—and they attracted at least two of the other races as much as us humans.” “So folks would promenade, right? We could almost imagine we’re out looking for action in some high-priced dive?” “You had such romances in Straumli Realm?” “Well, yes. I was a precocious tot, you know. But you actually lived it, right?” “Um, yes. A few times,” as a shy college girl, before she graduated and shipped out to the Vrinimi Organization. At Vrinimi, the socializing had been exclusively nonhuman—at least till Pham came along.


pages: 349 words: 134,041

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Alan Greenspan, Albert Einstein, Asian financial crisis, asset-backed security, Bear Stearns, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business logic, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, currency risk, disinformation, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial engineering, financial innovation, fixed income, Glass-Steagall Act, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Bogle, John Meriwether, junk bonds, locking in a profit, Long Term Capital Management, low interest rates, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, Nixon triggered the end of the Bretton Woods system, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, proprietary trading, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk free rate, risk-adjusted returns, risk/return, Salesforce, Satyajit Das, shareholder value, short selling, short squeeze, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

He doesn’t like the thought of buying all the stuff you know nothing about. 2 Efficient markets – Eugene Fama and his colleagues hypothesized that prices follow a random walk. Prices do not follow specific discernible patterns, at least from past prices. All known information is already built into the price. Dealers and investors exist to exploit market inefficiency. If markets are truly efficient, then where is the boodle coming from? 3 Mean/variance – the risk of financial markets is reduced to two statistics: mean (average) return and variability of the returns, standard deviation or variance as measure of volatility. For investors, the wilder the swings in price, the higher the risk. Risk is now a known known.

Being short implies that the airline needs to buy oil to keep flying. If the oil price goes up, you lose because your costs rise. If the oil price falls then you win because your costs fall. The airline has the following choices in managing its risk to oil prices: 1 Do nothing – this works best if the oil price falls. Lower oil prices reduce the airline’s costs. It is a bad choice if oil prices rise. 2 Buy oil forward – this locks in the cost of buying oil. This is best if oil prices go up. It is a lousy decision if oil prices go down as the airline is locked into high prices. 3 Buy a call option – this produces the best result if the oil price is volatile. If the price goes up you exercise the call option and buy oil at the agreed price.

The actual market price of the wheat on that day is compared to the price agreed under the forward contract. If the market price is lower than the agreed forward price then the farmer gets the difference. When the farmer actually sells the wheat he gets a lower price. But the payment under the forward contract (the gain) boosts the farmer’s receipts to the locked-in agreed price. If the market price is higher than the agreed forward price then the farmer pays the difference. He makes a loss. The farmer’s loss is offset by the gain he makes when selling the wheat normally because the market price has gone up.


pages: 454 words: 134,482

Money Free and Unfree by George A. Selgin

Alan Greenspan, asset-backed security, bank run, banking crisis, barriers to entry, Bear Stearns, break the buck, Bretton Woods, business cycle, capital controls, central bank independence, centralized clearinghouse, Charles Lindbergh, credit crunch, Credit Default Swap, crony capitalism, disintermediation, Dutch auction, fear of failure, fiat currency, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, foreign exchange controls, Fractional reserve banking, German hyperinflation, Glass-Steagall Act, Hyman Minsky, incomplete markets, inflation targeting, information asymmetry, invisible hand, Isaac Newton, Joseph Schumpeter, large denomination, liquidity trap, Long Term Capital Management, low interest rates, market microstructure, Money creation, money market fund, moral hazard, Network effects, Northern Rock, oil shock, Paul Samuelson, Phillips curve, plutocrats, price stability, profit maximization, purchasing power parity, quantitative easing, random walk, rent-seeking, reserve currency, Robert Gordon, Robert Solow, Savings and loan crisis, savings glut, seigniorage, special drawing rights, The Great Moderation, the payments system, too big to fail, transaction costs, Tyler Cowen, unorthodox policies, vertical integration, Y2K

Robert Barsky (1987) reported in the same vein that, while quarterly U.S. inflation could be described as a white-noise process from 1870 to 1913, it was positively serially correlated from 1919 to 1938 and from 1947 to 1959 (when the Fed was constrained by some form of gold standard), and has since become a random walk. These findings suggest that, as the Fed gained greater control over long-run price level movements, those movements became increasingly difficult to forecast. Our own estimates from an autoregressive–moving-average (ARMA) (1, 1) model yield conclusions similar to Klein’s. Although the standard deviation of inflation was greater before the Fed’s establishment than it has been since World War II, the postwar inflation process includes a large (that is, above 0.9) autoregressive component, whereas that component was small and negative before 1915 (see Table 8.1).6 Relatively small postwar inflation-rate innovations have consequently been associated with relatively large steady-state changes in the price level (see Figure 8.2).

Significantly, both of the major post–Federal Reserve Act episodes of inflation coincided with relaxations of gold standard–based constraints on the Fed’s money-creating abilities, consisting of a temporary gold export embargo from September 1917 through June 1919 and of the permanent closing of the Fed’s gold window in 1971.4 Although the costs of price level instability are hard to assess, the reduced stability of prices under the Fed’s tenure has certainly not been costless. As the Board of Governors itself has observed, Stable prices in the long run are a precondition for maximum sustainable output growth and employment as well as moderate long-term interest rates. When prices are stable and believed to remain so, the prices of goods, services, materials, and labor are undistorted by inflation and serve as clearer signals and guides to the efficient allocation of resources. . . . Moreover, stable prices foster saving and capital formation, because when the risk of erosion of asset values resulting from inflation—and the need to guard against such losses—are minimized, households are encouraged to save more and businesses are encouraged to invest more.

That those initiatives did depend, and depend heavily, on central bank cooperation, and that neither succeeded in replicating the older arrangement’s achievements, suggests that those achievements were realized despite, rather than because of, central bankers’ involvement. The long-term stability, under the gold standard, of world prices, and of the U.S. price level in particular, reflected the connection under that standard of price level changes to changes in gold’s average cost of production. For any given state of gold supply, a growing demand for money would place downward pressure on the money prices of all goods apart from gold itself (the dollar value of which was, of course, fixed), including the prices of labor and other inputs in gold mining. The decline thus enhanced the profitability of gold mining and gold prospecting, ultimately promoting greater output of gold, which would end if not reverse the tendency of prices to fall.


pages: 515 words: 132,295

Makers and Takers: The Rise of Finance and the Fall of American Business by Rana Foroohar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, additive manufacturing, Airbnb, Alan Greenspan, algorithmic trading, Alvin Roth, Asian financial crisis, asset allocation, bank run, Basel III, Bear Stearns, behavioural economics, Big Tech, bonus culture, Bretton Woods, British Empire, business cycle, buy and hold, call centre, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, Carmen Reinhart, carried interest, centralized clearinghouse, clean water, collateralized debt obligation, commoditize, computerized trading, corporate governance, corporate raider, corporate social responsibility, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, crowdsourcing, data science, David Graeber, deskilling, Detroit bankruptcy, diversification, Double Irish / Dutch Sandwich, electricity market, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial deregulation, financial engineering, financial intermediation, Ford Model T, Frederick Winslow Taylor, George Akerlof, gig economy, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, Greenspan put, guns versus butter model, High speed trading, Home mortgage interest deduction, housing crisis, Howard Rheingold, Hyman Minsky, income inequality, index fund, information asymmetry, interest rate derivative, interest rate swap, Internet of things, invisible hand, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", John Bogle, John Markoff, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, Kickstarter, knowledge economy, labor-force participation, London Whale, Long Term Capital Management, low interest rates, manufacturing employment, market design, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, new economy, non-tariff barriers, offshore financial centre, oil shock, passive investing, Paul Samuelson, pensions crisis, Ponzi scheme, principal–agent problem, proprietary trading, quantitative easing, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, Rana Plaza, RAND corporation, random walk, rent control, Robert Shiller, Ronald Reagan, Satyajit Das, Savings and loan crisis, scientific management, Second Machine Age, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Snapchat, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, Steve Jobs, stock buybacks, subprime mortgage crisis, technology bubble, TED Talk, The Chicago School, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tim Cook: Apple, Tobin tax, too big to fail, Tragedy of the Commons, trickle-down economics, Tyler Cowen: Great Stagnation, Vanguard fund, vertical integration, zero-sum game

Bean Counters: The Battle for the Soul of American Business. New York: Portfolio/Penguin, 2011. ———. Icons and Idiots: Straight Talk on Leadership. New York: Portfolio/Penguin, 2013. Madrick, Jeff. Age of Greed: The Triumph of Finance and the Decline of America, 1970 to the Present. New York: Alfred A. Knopf, 2011. Malkiel, Burton G. A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing. 10th ed. New York: W. W. Norton & Company, 2012. Malleson, Tom. After Occupy: Economic Democracy for the 21st Century. Oxford: Oxford University Press, 2009. Mandis, Steven G. What Happened to Goldman Sachs? An Insider’s Story of Organizational Drift and Its Unintended Consequences.

In fact, the company known best for its construction, chemical, and real estate activities traded the world’s first oil swap thirty years ago.15 But the biggest nonfinancial oil derivatives players of all are the oil companies themselves, some of which now have trading arms that are as much a part of their core business model as energy development is. This is sadly ironic, given that the ups and downs of oil and other commodity prices are a huge reason behind the cyclical woes of energy firms. When oil prices are high, everyone rushes to extract as much as possible, inflating prices for equipment like rigs (the cost of a daily fee for an ultra-deepwater rig rose from $400,000 to $700,000 during the last big price run-up in 2011–2012). Later, when prices collapse, talent and money quickly leave the industry, forcing many players to slash costs and capacity. It’s a roller-coaster ride that’s only exacerbated by companies’ own trading.

Institutional investors poured into the market for natural resources; between 2004 and 2007, the number of commodities futures contracts outstanding in the world nearly doubled. Because commodities futures prices are the benchmark for the prices of actual physical commodities that people use on a daily basis, when speculators drive futures prices higher, it affects the real economy immediately. (Indeed, it was during that time that food and fuel price inflation began to rise globally.) In 2003, big investors were putting $13 billion into commodity index trading strategies. By March 2008, they were pouring in $260 billion.27 During that time, the prices of 25 commodities, from cotton to cocoa, cattle to heating oil, aluminum to copper, rose by a whopping 183 percent.


pages: 459 words: 138,689

Slowdown: The End of the Great Acceleration―and Why It’s Good for the Planet, the Economy, and Our Lives by Danny Dorling, Kirsten McClure

"World Economic Forum" Davos, Affordable Care Act / Obamacare, Anthropocene, Berlin Wall, Bernie Sanders, Boeing 747, Boris Johnson, British Empire, business cycle, capital controls, carbon tax, clean water, creative destruction, credit crunch, Donald Trump, drone strike, Elon Musk, en.wikipedia.org, Extinction Rebellion, fake news, Flynn Effect, Ford Model T, full employment, future of work, gender pay gap, global supply chain, Google Glasses, Great Leap Forward, Greta Thunberg, Henri Poincaré, illegal immigration, immigration reform, income inequality, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, It's morning again in America, James Dyson, Jeremy Corbyn, jimmy wales, John Harrison: Longitude, Kickstarter, low earth orbit, Mark Zuckerberg, market clearing, Martin Wolf, mass immigration, means of production, megacity, meta-analysis, military-industrial complex, mortgage debt, negative emissions, nuclear winter, ocean acidification, Overton Window, pattern recognition, Ponzi scheme, price stability, profit maximization, purchasing power parity, QWERTY keyboard, random walk, rent control, rising living standards, Robert Gordon, Robert Shiller, Ronald Reagan, School Strike for Climate, Scramble for Africa, sexual politics, Skype, Stephen Hawking, Steven Pinker, structural adjustment programs, Suez crisis 1956, the built environment, Tim Cook: Apple, time dilation, transatlantic slave trade, trickle-down economics, very high income, wealth creators, wikimedia commons, working poor

What’s more, you believe that your high salary is merely a fair market reward for your long hours at your desk, and for what you imagine to be your immense talent. You have heard that next year, in 1997, liquid crystal displays will be the next big thing; and you believe that there will always be a next big thing. You also know that stock prices can, in the short term, take what’s called a random walk. No one without insider knowledge (which is illegal to use) can know what is about to happen. However, you have access to over $1 billion of your clients’ money. And you have a very simple strategy: “construct a large, suitably leveraged, market-neutral equity portfolio and then systematically expand it in the morning and contract it in the afternoon, day after day.”42 Although your index is based largely on the NASDAQ, you tweak a few components to reflect your talent.

And they could peak at a far lower value if the U.K. economy were to slow down more quickly. The price of houses will not keep rising forever. Nothing can. A graph that uses absolute values suggests runaway prices and greater and greater crashes to come. In contrast, a graph that shows relative changes and uses a log scale suggests much more regularity and points to price falls in the past being of at least the same order of magnitude as the two most recent periods of actual housing price falls. The deceleration in price rise from 1972 to 1974 was greater than that from 1979 to 1981. The early 1980s price slowdown was, in turn, comparable to the falls from 1988 to 1990.

Almost every decade since the 1970s has seen a smaller proportionate rise in U.K. and U.S. housing prices compared with the decade before it. The capital appreciation of housing, just as an investment, is falling. Soon we might even start thinking of a house as a home—not as a retirement investment for the wealthy. Before we return to the subject of house prices, consider something a little different—gold. In figure 52 the gold price timeline has not been adjusted for inflation, and so the price appears to rise and rise. But each period of increase is always abruptly brought to a halt by a crash, followed normally by a period of price stability for a few years when the price appears to oscillate around a fixed point, before it again begins to climb upward.


Adam Smith: Father of Economics by Jesse Norman

active measures, Alan Greenspan, Andrei Shleifer, balance sheet recession, bank run, banking crisis, Basel III, Bear Stearns, behavioural economics, Berlin Wall, Black Swan, Branko Milanovic, Bretton Woods, British Empire, Broken windows theory, business cycle, business process, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, centre right, cognitive dissonance, collateralized debt obligation, colonial exploitation, Corn Laws, Cornelius Vanderbilt, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, David Brooks, David Ricardo: comparative advantage, deindustrialization, electricity market, Eugene Fama: efficient market hypothesis, experimental economics, Fall of the Berlin Wall, Fellow of the Royal Society, financial engineering, financial intermediation, frictionless, frictionless market, future of work, George Akerlof, Glass-Steagall Act, Hyman Minsky, income inequality, incomplete markets, information asymmetry, intangible asset, invention of the telescope, invisible hand, Isaac Newton, Jean Tirole, John Nash: game theory, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, lateral thinking, loss aversion, low interest rates, market bubble, market fundamentalism, Martin Wolf, means of production, mirror neurons, money market fund, Mont Pelerin Society, moral hazard, moral panic, Naomi Klein, negative equity, Network effects, new economy, non-tariff barriers, Northern Rock, Pareto efficiency, Paul Samuelson, Peter Thiel, Philip Mirowski, price mechanism, principal–agent problem, profit maximization, public intellectual, purchasing power parity, random walk, rent-seeking, Richard Thaler, Robert Shiller, Robert Solow, Ronald Coase, scientific worldview, seigniorage, Socratic dialogue, South Sea Bubble, special economic zone, speech recognition, Steven Pinker, The Chicago School, The Myth of the Rational Market, The Nature of the Firm, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, time value of money, transaction costs, transfer pricing, Veblen good, Vilfredo Pareto, Washington Consensus, working poor, zero-sum game

It means, in effect, that markets are memoryless. If past events played any role in predicting price movements, then a sufficiently savvy investor could in principle game the market and make a riskless profit. But the theory’s insistence on informational efficiency makes this impossible. If an asset’s price is too low, given new information, investors will buy the asset; if too high, they will sell it. Prices are therefore deemed to follow what statisticians call a random walk. They move up and down according to no discernible pattern, and at any given moment there is as much chance that a price will go down as up, and vice versa. There are no free lunches, no riskless profits to be gained, and no investor, however expert or naive, can do better over time than the market.

Moreover, the market is self-regulating; its balancing adjustments occur automatically as a result of trading, without any external intervention. As Smith puts it, ‘The natural price, therefore, is, as it were, the central price, to which the prices of all commodities are continually gravitating.’ In normal markets with freely working supply and demand, the natural price will thus equal the real price. Smith’s idea is of a possible equilibrium between supply and demand, though he generally uses the very similar language of ‘balance’. But his whole picture is dynamic. In normal markets prices tend towards equilibrium, but they may not achieve it for any length of time; and the equilibrium or balancing point constantly changes as markets shift and evolve.

Where land is privately owned the landlords will demand rent for use of the land. This enables Smith to analyse what he calls the real price of a given commodity in terms of rent, wages and profit, or of the returns—including profit—demanded by land, labour and capital respectively. When a market is operating competitively, if the market price of grain, say, falls below the real price, then the drop in expected returns will cause landowners to withdraw the use of land, labourers their work and employers their capital. When market prices exceed real prices, however, the same process works in reverse, generating profits and stimulating new supply.


pages: 470 words: 130,269

The Marginal Revolutionaries: How Austrian Economists Fought the War of Ideas by Janek Wasserman

"World Economic Forum" Davos, Abraham Wald, Albert Einstein, American Legislative Exchange Council, anti-communist, battle of ideas, Berlin Wall, Bretton Woods, business cycle, collective bargaining, Corn Laws, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, different worldview, Donald Trump, experimental economics, Fall of the Berlin Wall, floating exchange rates, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, Gunnar Myrdal, housing crisis, Internet Archive, invisible hand, John von Neumann, Joseph Schumpeter, laissez-faire capitalism, liberal capitalism, low interest rates, market fundamentalism, mass immigration, means of production, Menlo Park, military-industrial complex, Mont Pelerin Society, New Journalism, New Urbanism, old-boy network, Paul Samuelson, Philip Mirowski, price mechanism, price stability, public intellectual, RAND corporation, random walk, rent control, road to serfdom, Robert Bork, rolodex, Ronald Coase, Ronald Reagan, Silicon Valley, Simon Kuznets, The Chicago School, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, trade liberalization, union organizing, urban planning, Vilfredo Pareto, Washington Consensus, zero-sum game, éminence grise

After John von Neumann’s tragic death in 1957, Morgenstern grew close with another Viennese, the mathematician Kurt Gödel. He mentored the Nobel laureate Martin Shubik and produced late-career work on the unpredictability of the stock market with Clive Granger, another Nobelist. The latter work, developed with Burton Malkiel, inspired Malkiel’s well-known random walk theory concerning the stock market. Morgenstern believed his work, especially Theory of Games, was of the greatest significance for his standing within his Viennese cohort and for economics in general.26 Morgenstern was therefore left deeply disappointed when the Nobel Memorial Prize overlooked him year after year.

According to the marginalists, it is not the costs incurred in producing the iPhones but the price that consumers are willing to pay that fixes the phones’ utility. If Apple chose to produce only one thousand devices, the price would skyrocket because of excessive—near desperate—demand for the latest phone. Conversely, if Apple (foolishly) tried to up production of phones so that there was one for every person on earth (7.6 billion at last count), the price would correspondingly plummet, as Apple frantically sought buyers to clear its inventory. In the final analysis, then, Apple determines price and production levels on the basis of consumer demand.

Since inflation and deflation unevenly affect economic participants, discrete classes of individuals are impacted differently. Because of the baleful effects of price instability, modern states turned to monetary interventions to forestall crises. Ironically these actions only exacerbated problems. By manipulating the price of money through the creation of currency, the state did not change the fundamental values of existing commodities; instead, it distorted the relationship between prices and value. Mises therefore rejected deflationary and inflationary fiduciary measures, though the latter for him was the graver problem. Rising wages and prices produced an illusion of economic growth, setting the economy up for a fall.57 Mises devoted much of Theory to exploring problems created by expanding the money supply and raising interest rates beyond their “natural” rates.


pages: 493 words: 132,290

Vultures' Picnic: In Pursuit of Petroleum Pigs, Power Pirates, and High-Finance Carnivores by Greg Palast

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", anti-communist, back-to-the-land, bank run, Berlin Wall, Bernie Madoff, British Empire, capital asset pricing model, capital controls, centre right, Chelsea Manning, classic study, clean water, collateralized debt obligation, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, currency risk, disinformation, Donald Trump, energy security, Exxon Valdez, Glass-Steagall Act, invisible hand, junk bonds, means of production, Myron Scholes, Nelson Mandela, offshore financial centre, Pepto Bismol, random walk, Ronald Reagan, sensible shoes, Seymour Hersh, transfer pricing, uranium enrichment, Washington Consensus, Yogi Berra

No more picking stocks, a fool’s errand. Financial rewards would be small, but risk would vanish. The world’s financial panics would be left to history, and all economic booms and busts smoothed into calm waves. I met Black not long after he’d put this Drunk’s Random Walk into an academic paper with his friend Myron Scholes. They called it the Capital Asset Pricing Model.19 About the same time, a newly expanding investment bank, a small house on the edge of the financial universe, Goldman Sachs, also fell in love with Dr. Black’s Magic Model, and hired all his best students and, eventually, Dr. Black himself.

To understand why, forget everything you think you know about oil companies. They are not hunting for oil, they are hunting for profits. BP grabbed the Caspian Sea contract in 1994 when the price of oil was a joke, down as low as $15 a barrel. The Contract of the Century gave BP the right to drill the oil, but more important, the right not to drill it, to keep it off the market, to squeeze the world price higher. Capitalism’s first commandment is: The lower the supply, the higher the price. And Baku was the perfect place to lower the supply. Baba could bitch, but BP had him by the shopkas. Wars are fought over oil—not only to get it, a story you know well—but also to keep it from getting to market.

The Boys in Houston called for keeping Iraq in OPEC and therefore limiting Iraq’s oil output. The plan would “enhance [Iraq’s] relationship with OPEC” and keep the price of oil higher than the Washington Monument. It was the “Enhance OPEC” plan that rolled with the tanks into Baghdad. This wasn’t a war of blood for oil. It was a war of blood for no oil, to limit the flow.4 Once Iraq’s oil fields were capped and contained by war and OPEC, the price of oil bumped from $20 to $40 a barrel, then jumped to $100+ a barrel. Only then did BP open the spigot in the Caspian. It was 2004 and I was in mortal combat with my latest book manuscript when the new volunteer, a Ms.


pages: 436 words: 127,642

When Einstein Walked With Gödel: Excursions to the Edge of Thought by Jim Holt

Ada Lovelace, Albert Einstein, Andrew Wiles, anthropic principle, anti-communist, Arthur Eddington, Benoit Mandelbrot, Bletchley Park, Brownian motion, cellular automata, Charles Babbage, classic study, computer age, CRISPR, dark matter, David Brooks, Donald Trump, Dr. Strangelove, Eddington experiment, Edmond Halley, everywhere but in the productivity statistics, Fellow of the Royal Society, four colour theorem, Georg Cantor, George Santayana, Gregor Mendel, haute couture, heat death of the universe, Henri Poincaré, Higgs boson, inventory management, Isaac Newton, Jacquard loom, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Large Hadron Collider, Long Term Capital Management, Louis Bachelier, luminiferous ether, Mahatma Gandhi, mandelbrot fractal, Monty Hall problem, Murray Gell-Mann, new economy, Nicholas Carr, Norbert Wiener, Norman Macrae, Paradox of Choice, Paul Erdős, Peter Singer: altruism, Plato's cave, power law, probability theory / Blaise Pascal / Pierre de Fermat, quantum entanglement, random walk, Richard Feynman, Robert Solow, Schrödinger's Cat, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, Skype, stakhanovite, Stephen Hawking, Steven Pinker, Thorstein Veblen, Turing complete, Turing machine, Turing test, union organizing, Vilfredo Pareto, Von Neumann architecture, wage slave

Even if we succeed in achieving some immediate good, there is no telling what the remoter consequences of our altruism will be. All we know is that these consequences will extend far into the future, beyond the purview of our will. Owing to the contingent and chaotic nature of cause and effect, the balance of good over evil involves something like a random walk, with unforeseen reversals at every stage. (Consider the doctor who successfully delivered the fourth child of Klara and Alois Hitler after the couple’s first three infants had tragically sickened and died.) It is the unknowability of the distant effects of our actions that led G. E. Moore to say, in his Principia Ethica, that “no sufficient reason has ever yet been found for considering one action more right or more wrong than another.”

The diagram was almost identical in shape to the one Mandelbrot was about to present in his lecture, yet it concerned not wealth distribution but price jumps on the New York Cotton Exchange. Why should the pattern of ups and downs in the market for cotton bear such a striking resemblance to the wildly unequal way wealth was spread through society? This was certainly not consistent with the orthodox model of financial markets, which was originally proposed in 1900 by a French mathematician named Louis Bachelier (who had copied it from the physics of a gas in equilibrium). According to the Bachelier model, price variation in a stock or commodity market is supposed to be smooth and mild; fluctuations in price, arranged by size, should line up nicely in a classic bell curve.

But Mandelbrot, returning to IBM and sifting with the aid of its computers through a century of data from the New York Cotton Exchange, found a far more volatile pattern, one dominated by a small number of extreme swings. A power law seemed to be at work. Moreover, financial markets behaved roughly the same on all timescales. When Mandelbrot took a price chart and zoomed in from a year to a month to a single day, the wiggliness of the line did not change. In other words, price histories were self-similar—like a cauliflower. “The very heart of finance,” Mandelbrot concluded, “is fractal.” The fractal model of financial markets that Mandelbrot went on to develop has never caught on with finance professors, who still by and large cling to the efficient market hypothesis.


pages: 497 words: 153,755

The Power of Gold: The History of an Obsession by Peter L. Bernstein

Alan Greenspan, Albert Einstein, Atahualpa, bread and circuses, Bretton Woods, British Empire, business cycle, California gold rush, central bank independence, double entry bookkeeping, Edward Glaeser, Everybody Ought to Be Rich, falling living standards, financial innovation, floating exchange rates, Francisco Pizarro, German hyperinflation, Hernando de Soto, Isaac Newton, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, large denomination, liquidity trap, long peace, low interest rates, Money creation, money: store of value / unit of account / medium of exchange, old-boy network, Paul Samuelson, price stability, profit motive, proprietary trading, random walk, rising living standards, Ronald Reagan, seigniorage, the market place, The Wealth of Nations by Adam Smith, Thomas Malthus, too big to fail, trade route

This observation hardly does justice to a complicated and often fascinating story related, as centuries earlier, to the ability of the East to absorb huge quantities of the precious metals. See, especially, Flandreau, 1996, and Kindleberger, 1989. 'A technical statement of this point in economist-speak would be as follows: "Box Jenkins procedures at both quarterly and annual frequencies identify the price level over 1870-1914 as a random walk with little drift, and inflation consequently as approximately zero-mean white noise, in both the United States and the United Kingdom" (see Barsky and DeLong, 1991, p. 824). 'The French did not pay the indemnity in specie. They issued a perpetual bond (a bond with no maturity), skillfully underwritten by the Rothschilds, that had many buyers outside of France.

As late as 1861, Italian circulation consisted of 75 percent coin."47 When money is in short supply, people try to economize on the amount they spend for goods and services. The usual result is a declining price level. That is precisely what happened during the fifteenth century. Reliable estimates indicate that prices for commodities throughout western Europe fell by anywhere from 20 percent to 50 percent between 1400 and 1500. In Aragon, for example, the index of prices fell about 20 percent." The price of English wheat fell by half between 1360 and 1500, while the price of rye in Frankfurt dropped even faster.49 Similar trends in the Low Countries and Italy demonstrate that this was a universal phenomenon in fifteenth-century Europe.

Regardless of the difficulties that monarchs may have had with their finances in the sixteenth century, affairs in the private sector reached a far more sophisticated level than at any time in the past. The Price Revolution defined the tone of the entire century. A pattern of rising prices was first visible in Italy and Germany from about 1470, the low point for the decline in prices that had set in following the Black Death in 1349. Then, like another kind of plague, inflation infected Europe in a series of steps. It took hold in England and France during the 1480s, extended to Iberia in the next decade, and appeared in eastern Europe in the early 1500s. Although prices did not rise in every single year, for agricultural prices in particular are characteristically volatile because of weather variations, the low point reached in each decline tended to be higher than the previous low, while each high point tended to set a record on the upside.33 Anyone who has lived through an inflationary period can testify that inflation is always unsettling because it clouds the future with uncertainty, but the shock of sustained inflation to the inhabitants of Europe in the sixteenth century was shattering.


pages: 434 words: 117,327

Can It Happen Here?: Authoritarianism in America by Cass R. Sunstein

active measures, affirmative action, Affordable Care Act / Obamacare, airline deregulation, anti-communist, anti-globalists, availability heuristic, behavioural economics, Black Lives Matter, Brexit referendum, business cycle, Cambridge Analytica, Cass Sunstein, cognitive load, David Brooks, disinformation, Donald Trump, driverless car, Edward Snowden, Estimating the Reproducibility of Psychological Science, failed state, fake news, Filter Bubble, Francis Fukuyama: the end of history, Garrett Hardin, ghettoisation, illegal immigration, immigration reform, Isaac Newton, job automation, Joseph Schumpeter, Long Term Capital Management, microaggression, Nate Silver, Network effects, New Journalism, night-watchman state, nudge theory, obamacare, Paris climate accords, post-truth, Potemkin village, random walk, Richard Thaler, road to serfdom, Ronald Reagan, seminal paper, Steve Bannon, TED Talk, the scientific method, Tragedy of the Commons, Tyler Cowen, War on Poverty, WikiLeaks, World Values Survey

For all these reasons, the political status quo could survive indefinitely without any fundamental change in either the size or practical influence of existing intolerant communities. Shocks along the way could give one community a temporary boost. An Islamist attack might lift nativist support, or a mass shooting of African-Americans might energize identitarians. If such horrors followed a random walk, and the two intolerant communities continued to sow alienation through initiatives that frighten broad constituencies, the present political balance would likely persist. Millions would continue caring more about blocking despised opponents than about alleviating social maladies. Massive dysfunctions, such as schools that fail to prepare students for the global economy, could just fester.

If, say, Congress takes away subsidies and the individual mandate, the cost of health insurance will rise, leading healthier people to drop their insurance. The costs of covering the remaining, sicker, people will then also rise, causing some companies to raise prices or to drop out of the market, thereby reducing competition and raising prices even further. Costs then go up further, leading other consumers to drop coverage, and so on. Death spirals can result, destroying the whole market. A commonsense leader like Trump is untroubled by such imponderables. He just makes a strong, simple, and clear decision and lets the chips fall where they may.

Milton Friedman, in his 1980 book Free to Choose, made related arguments about how economic freedom underpins political freedom, so I’m going to consider these views as a kind of composite, while again noting that some differences remain among these authors. In the composite model, one economic intervention creates some problems from its unintended secondary consequences, and that in turn leads to another intervention, which in turn creates the perceived need for yet further interventions. For instance, you can imagine a price control leading to a decline in product quality, which in turn motivates further government regulations on business and product quality, which in turn renders many businesses unprofitable, and which eventually leads to yet further control or even nationalization. Eventually government controls so much of the economy that political liberties are lost too.


pages: 476 words: 132,042

What Technology Wants by Kevin Kelly

Albert Einstein, Alfred Russel Wallace, Apollo 13, Boeing 747, Buckminster Fuller, c2.com, carbon-based life, Cass Sunstein, charter city, classic study, Clayton Christensen, cloud computing, computer vision, cotton gin, Danny Hillis, dematerialisation, demographic transition, digital divide, double entry bookkeeping, Douglas Engelbart, Edward Jenner, en.wikipedia.org, Exxon Valdez, Fairchild Semiconductor, Ford Model T, George Gilder, gravity well, Great Leap Forward, Gregor Mendel, hive mind, Howard Rheingold, interchangeable parts, invention of air conditioning, invention of writing, Isaac Newton, Jaron Lanier, Joan Didion, John Conway, John Markoff, John von Neumann, Kevin Kelly, knowledge economy, Lao Tzu, life extension, Louis Daguerre, Marshall McLuhan, megacity, meta-analysis, new economy, off grid, off-the-grid, out of africa, Paradox of Choice, performance metric, personalized medicine, phenotype, Picturephone, planetary scale, precautionary principle, quantum entanglement, RAND corporation, random walk, Ray Kurzweil, recommendation engine, refrigerator car, rewilding, Richard Florida, Rubik’s Cube, Silicon Valley, silicon-based life, skeuomorphism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Stewart Brand, Stuart Kauffman, technological determinism, Ted Kaczynski, the built environment, the long tail, the scientific method, Thomas Malthus, Vernor Vinge, wealth creators, Whole Earth Catalog, Y2K, yottabyte

Bob Bakker, the model for the dino guy in Jurassic Park and real-life dinosaur expert, claims, “This striking case of iterative parallelism and convergence [in the six dino lineages] . . . is a powerful argument that observed long-term changes in the fossil record are the result of directional natural selection, not a random walk through genetic drift.” Way back in 1897, paleontologist Henry Osborn, an early dinosaur and mammal expert, wrote: “My study of teeth in a great many phyla of Mammalia in past times has convinced me that there are fundamental predispositions to vary in certain directions; that the evolution of teeth is marked out beforehand by hereditary influences which extend back hundreds of thousands of years.”

The mind does, but it had not yet been fully unleashed. A world without technology had enough to sustain survival but not enough to transcend it. Only when the mind, liberated by language and enabled by the technium, transcended the constraints of nature 50,000 years ago did greater realms of possibility open up. There was a price to pay for this transcendence, but what we gained by this embrace was civilization and progress. We are not the same folks who marched out of Africa. Our genes have coevolved with our inventions. In the past 10,000 years alone, in fact, our genes have evolved 100 times faster than the average rate for the previous 6 million years.

One UN report found that households in the older slums of Bangkok have on average 1.6 televisions, 1.5 cell phones, and a refrigerator; two-thirds have a washing machine and CD player; and half have a fixed-line phone, a video player, and a motor scooter. In the favelas of Rio, the first generation of squatters had a literacy rate of only 5 percent, but 94 percent of their kids were literate. There is a price to pay for that growth. As vibrant and dynamic as cities are, their edges can be unpleasant. To enter a slum you need to walk down shit lane. There is human excrement rotting on the sidewalk, urine flowing in the gutter, and garbage piled up in heaps. I’ve done it many times in the sprawling shantytowns of the developing world, and it is no fun—and less so for the residents who must endure this every day.


Trade Your Way to Financial Freedom by van K. Tharp

asset allocation, backtesting, book value, Bretton Woods, buy and hold, buy the rumour, sell the news, capital asset pricing model, commodity trading advisor, compound rate of return, computer age, distributed generation, diversification, dogs of the Dow, Elliott wave, high net worth, index fund, locking in a profit, margin call, market fundamentalism, Market Wizards by Jack D. Schwager, passive income, prediction markets, price stability, proprietary trading, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Ronald Reagan, Savings and loan crisis, Sharpe ratio, short selling, Tax Reform Act of 1986, transaction costs

NOTES 1. Karl Popper, Objective Knowledge: An Evolutionary Approach (Oxford: Clarendon Press, 1972). 2. Jack Schwager, “William Eckhardt: The Mathematician,” The New Market Wizards: Conversations with America’s Top Traders (New York: HarperCollins, 1992), p. 114. 3. For example, Burton G. Malkiel, A Random Walk Down Wall Street, 8th ed. (New York: Norton, 2004). 4. These stories were made up, but they are typical examples of what you might read to explain the action of the market. 5. Larry Williams, The Definitive Guide to Futures Trading, Vol. II (Brightwaters, N.Y.: Windsor Books, 1989), p. 202. 6.

In a very efficient market, the total price movement will be equal to the price movement between the two time periods. The ratio would be 1.0 because there is no noise. For example, if the price moved up 10 points in a 10-day period and the price moved up by 1 point each day, then you’d have a ratio of 10/(10 × 1) = 1.0. In a very inefficient market, there would be a very small total price movement and a lot of daily price movement. The resulting ratio would tend to go toward zero. For example, if the price only moved 1 point over a 10-day period, but the price moved up or down by 10 points each day, then you’d have a ratio of 1/(10 × 10) = 0.01.

That research indicates that, if you want to analyze the value of the euro versus the U.S. dollar (EUR/USD), for instance, you not only have to look at euro data but also at the data for other related markets to find hidden patterns and relationships that influence the EUR/USD relationship: • Australian dollar/U.S. dollar (AUD/USD) • Australian dollar/Japanese yen (AUD/JPY) • British pound • Euro/Canadian dollar (EUR/CAD) • Gold • Nasdaq 100 Index • British pound/Japanese yen (GBP/JPY) • British pound/U.S. dollar (GBP/USD) • Japanese yen The intermarket relationships among various currencies may be rather obvious, but the impact of stock indices, U.S. T-notes, or crude oil prices on a forex pair may seem like more of a reach. But research has shown that these related markets do have an important influence on a target forex market and can provide early insights into the forex market’s future price direction. Some analysts like to perform correlation studies of two related markets, measuring the degree to which the prices of one market move in relation to the prices of the second market. Two markets are considered perfectly correlated if the price change of the second market can be forecasted precisely from the price change of the first market.


pages: 566 words: 163,322

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World by Ruchir Sharma

"World Economic Forum" Davos, Asian financial crisis, backtesting, bank run, banking crisis, Berlin Wall, Bernie Sanders, BRICs, business climate, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, centre right, colonial rule, commodity super cycle, corporate governance, creative destruction, crony capitalism, currency peg, dark matter, debt deflation, deglobalization, deindustrialization, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, driverless car, Edward Glaeser, Elon Musk, eurozone crisis, failed state, Fall of the Berlin Wall, falling living standards, financial engineering, Francis Fukuyama: the end of history, Freestyle chess, Gini coefficient, global macro, Goodhart's law, guns versus butter model, hiring and firing, hype cycle, income inequality, indoor plumbing, industrial robot, inflation targeting, Internet of things, Japanese asset price bubble, Jeff Bezos, job automation, John Markoff, Joseph Schumpeter, junk bonds, Kenneth Rogoff, Kickstarter, knowledge economy, labor-force participation, Larry Ellison, lateral thinking, liberal capitalism, low interest rates, Malacca Straits, Mark Zuckerberg, market bubble, Mary Meeker, mass immigration, megacity, megaproject, Mexican peso crisis / tequila crisis, middle-income trap, military-industrial complex, mittelstand, moral hazard, New Economic Geography, North Sea oil, oil rush, oil shale / tar sands, oil shock, open immigration, pattern recognition, Paul Samuelson, Peter Thiel, pets.com, plutocrats, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, Ronald Coase, Ronald Reagan, savings glut, secular stagnation, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Simon Kuznets, smart cities, Snapchat, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Steve Jobs, tacit knowledge, tech billionaire, The Future of Employment, The Wisdom of Crowds, Thomas Malthus, total factor productivity, trade liberalization, trade route, tulip mania, Tyler Cowen: Great Stagnation, unorthodox policies, Washington Consensus, WikiLeaks, women in the workforce, work culture , working-age population

Financial Times Alphaville, May 13, 2015. Lowther, Ed. “A Short History of the Pound.” BBC News, February 14, 2014. “NRIs Sent Home $65 Billion in Past Six Months:Lord Swraj Paul.” Press Trust of India, April 22, 2015. “Pushing the Limits of International Trade Policy.” World Bank, 2014. Sanyal, Sanjeev. “The Random Walk: Mapping the World’s Prices 2015.” Deutsche Bank Research, April 14, 2015. Sharma, Ruchir. “And Then There Were None.” Economic Times, September 5, 2000. ——. “Why Europe Will Bounce Back in 2013.” Financial Times, December 18, 2012. ——. “Don’t Expect Emerging Markets to Be Flooded in Cheap Money.” Financial Times, May 20, 2013.

Any period of high growth may be doomed if it is accompanied by rapidly rising inflation. High growth is far more durable if consumer prices are rising slowly or even if they are falling as the result of a positive supply shock or good deflation. However, deflation in asset prices is almost always a negative sign for the economy and is usually preceded by a rapid run-up in the price of houses and stocks. In today’s globalized world, in which cross-border trade and money flows often tend to restrain consumer prices but magnify asset prices, watching the price of stocks and houses is as important as tracking the price of onions. * Defined by Jordà, Schularick, and Taylor as at least one standard deviation. 8 CHEAP IS GOOD Does the country feel cheap or expensive?

For all its progress in pulling people up into the middle class, Brazil has inadvertently built a disappointing low-growth, high-inflation economy—the opposite of China’s high-growth, low-inflation miracle economy of recent decades. Victory in the War on Inflation The general rule—high consumer price inflation is a bad sign—is particularly useful for spotting outliers in a world where most countries have won the war on inflation. In the 1970s the OPEC embargo sent oil prices skyrocketing. Food prices rose sharply too. As workers came to expect spiking prices at the gas pump and grocery store, they began demanding regular wage hikes to meet their basic needs, which pushed companies to hike prices for all kinds of consumer goods. The vicious “wage-price” spiral began, driving the inflation rate into the double digits in rich countries like the United States, and stagflation set in.


pages: 586 words: 160,321

The Euro and the Battle of Ideas by Markus K. Brunnermeier, Harold James, Jean-Pierre Landau

"there is no alternative" (TINA), Affordable Care Act / Obamacare, Alan Greenspan, asset-backed security, bank run, banking crisis, battle of ideas, Bear Stearns, Ben Bernanke: helicopter money, Berlin Wall, Bretton Woods, Brexit referendum, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, collective bargaining, credit crunch, Credit Default Swap, cross-border payments, currency peg, currency risk, debt deflation, Deng Xiaoping, different worldview, diversification, Donald Trump, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, financial deregulation, financial repression, fixed income, Flash crash, floating exchange rates, full employment, Future Shock, German hyperinflation, global reserve currency, income inequality, inflation targeting, information asymmetry, Irish property bubble, Jean Tirole, Kenneth Rogoff, Les Trente Glorieuses, low interest rates, Martin Wolf, mittelstand, Money creation, money market fund, Mont Pelerin Society, moral hazard, negative equity, Neil Kinnock, new economy, Northern Rock, obamacare, offshore financial centre, open economy, paradox of thrift, pension reform, Phillips curve, Post-Keynesian economics, price stability, principal–agent problem, quantitative easing, race to the bottom, random walk, regulatory arbitrage, rent-seeking, reserve currency, risk free rate, road to serfdom, secular stagnation, short selling, Silicon Valley, South China Sea, special drawing rights, tail risk, the payments system, too big to fail, Tyler Cowen, union organizing, unorthodox policies, Washington Consensus, WikiLeaks, yield curve

For an important contribution to this discussion, see for example Alberto Alesina and Silvia Ardagna, “Large Changes in Fiscal Policy: Taxes versus Spending,” in Tax Policy and the Economy, vol. 24, edited by Jeffrey Brown (Chicago: University of Chicago Press; NBER, 2010). 14. See for instance Paul Krugman, “Getting Trendy,” New York Times, July 23, 2010. Last accessed January 4, 2016, from http://krugman.blogs.nytimes.com/2010/07/23/getting-trendy. 15. John Cochrane, “How Big Is the Random Walk in GNP?” Journal of Political Economy 96(5) (1988): 893–920. 16. Blanchard, Cerutti, and Summers, “Inflation and Activity.” 17. Lawrence Summers, in a speech at an IMF research conference on November 8, 2013, http://ftalphaville.ft.com/2013/11/18/1696762/summers-on-bubbles-and-secular-stagnation-forever/. 18.

If supply does not react, demand shocks will simply translate into higher prices and not move output much. That is, instead of quantities, prices react. If, instead, prices are sluggish to respond (they are “sticky”), then quantities—like GDP and employment—also respond. In that case, an increase in prices is accompanied by a decrease in unemployment. This negative relationship between inflation and unemployment is known among economists as the Phillips curve. The more flexible the prices, the more a given boost to demand is reflected in higher prices rather than higher employment. Thus, in a world with quite flexible prices, government spending multipliers are likely to be quite small, and vice versa.

From this short-term rate, the central bank indirectly, through expectations of future rate cuts or hikes, affects long-term yields and bond prices as well as risk premia. The literature distinguishes between the interest rate, exchange rate, and the risk-taking channels. VARIOUS TRANSMISSION MECHANISMS In (New) Keynesian models, the nominal interest rate matters because of price and wage rigidities. As prices and wages only adjust slowly, quantities have to adjust. That is, price rigidities allow demand shocks to depress output and lead to underemployment. In contrast, in a world in which prices always flexibly adjust, the interest rate is constantly at its natural—or “Wicksellian”—level, and the economy is always at full employment.


pages: 598 words: 169,194

Bernie Madoff, the Wizard of Lies: Inside the Infamous $65 Billion Swindle by Diana B. Henriques

accounting loophole / creative accounting, airport security, Albert Einstein, AOL-Time Warner, banking crisis, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, break the buck, British Empire, buy and hold, centralized clearinghouse, collapse of Lehman Brothers, computerized trading, corporate raider, diversified portfolio, Donald Trump, dumpster diving, Edward Thorp, financial deregulation, financial engineering, financial thriller, fixed income, forensic accounting, Gordon Gekko, index fund, locking in a profit, low interest rates, mail merge, merger arbitrage, messenger bag, money market fund, payment for order flow, plutocrats, Ponzi scheme, Potemkin village, proprietary trading, random walk, Renaissance Technologies, riskless arbitrage, Ronald Reagan, Savings and loan crisis, short selling, short squeeze, Small Order Execution System, source of truth, sovereign wealth fund, too big to fail, transaction costs, traveling salesman

We were very tough”: Ibid. 55 “We had medium-sized, small clients, individual clients”: Ibid. 55 “All I could say to you is, at one point in time”: Avellino-Bienes SEC Transcript, p. 53. 56 “I don’t like to give out financial statements”: Ibid. 56 “Michael Bienes and Frank Avellino . . . have their own assets”: Ibid. 4. THE BIG FOUR 57 typically did no better than a portfolio of stocks chosen utterly at random: See Burton G. Malkiel, A Random Walk Down Wall Street (New York: W. W. Norton, 1973). 57 Chais was a courtly gentleman: Picard v. Chais Complaint, Declaration of Stanley Chais in support of his request for a temporary restraining order, p. 3. 57 her “peaches-and-cream complexion” and “tidily coiffed” blond hair: Lewis Funke, “News of the Rialto: Inside Musicals,” New York Times, Apr. 27, 1969. 57 the daughter of a Broadway playwright and was a promising playwright herself: Louis Calta, “News of the Rialto: A Guest Shot Back Home,” New York Times, July 24, 1966. 58 decided to invest some of his own money in arbitrage: First BLM Interview. 58 including his father-in-law, Saul Alpern: The retired Saul Alpern set up a shell business in 1983 in Florida called the Onondaga Investment Company.

In 2009 dollars, Madoff owed his father-in-law more than $200,000. 30 high-risk “short sales”: In its orthodox form, short-selling is the practice of borrowing shares of stock (specifically, ones you think are going to decline in price) and selling them. If the price falls as you anticipated, you can buy cheaper shares to replace the ones you borrowed and pocket the difference as your profit. If the price goes up, you wind up buying more expensive shares to replace the borrowed ones and you incur potentially open-ended losses. For example, if you borrow and sell shares priced at $10 each and the price falls to $1, your profit is $9 a share. But if the price goes up and up without limit, to $20 or $40 or $100 a share, your losses climb right along with it.

A writer visiting the bureau in the late 1950s thought “the manufacture of The Sheets is perhaps the most amazing operation in the financial market.” Every weekday, in a stunning feat of manpower and logistics, bureau clerks manually collected price lists for nearly eight thousand stocks submitted by about two thousand over-the-counter dealers. The clerks collated the prices by stock name, entered the data on mimeograph stencils, printed the catalogues, and got them out the door to several thousand firms around the US in a matter of hours. Only dealers with full-service subscriptions, priced at about $460 a year, could submit price quotes. That was a big expense for Madoff in his first few years in business, so he relied on day-old Pink Sheets collected from another brokerage firm’s offices on the same floor at 40 Exchange Place.


pages: 420 words: 143,881

The Blind Watchmaker; Why the Evidence of Evolution Reveals a Universe Without Design by Richard Dawkins

Boeing 747, epigenetics, Eratosthenes, Fellow of the Royal Society, Gregor Mendel, lateral thinking, Menlo Park, pattern recognition, phenotype, random walk, silicon-based life, Steven Pinker, the long tail

And provided we postulate a sufficiently large series of sufficiently finely graded intermediates, we shall be able to derive anything from anything else, without invoking astronomical improbabilities. We are allowed to do this only if there has been sufficient time to fit all the intermediates in. And also only if there is a mechanism for guiding each step in some particular direction, otherwise the sequence of steps will career off in an endless random walk. It is the contention of the Darwinian world-view that both these provisos are met, and that slow, gradual, cumulative natural selection is the ultimate explanation for our existence. If there are versions of the evolution theory that deny slow gradualism, and deny the central role of natural selection, they may be true in particular cases.

They are bought at a steeply increasing price. The price is measured as what economists call ‘opportunity cost’. The opportunity cost of something is measured as the sum of all the other things that you have to forgo in order to have that something. The cost of sending a child to a private, fee-paying school is all the things that you can’t afford to buy as a result: the new car that you can’t afford, the holidays in the sun that you can’t afford (if you’re so rich that you can afford all these things easily, the opportunity cost, to you, of sending your child to a private school may be next to nothing). The price, to a cheetah, of growing larger leg muscles is all the other things that the cheetah could have done with the materials and energy used to make the leg muscles, for instance make more milk for cubs.

A brain that is processing 200 distinct echoes per second might not find surplus capacity for thinking about anything else. Even the ticking-over rate of about 10 pulses per second is probably quite costly, but much less so than the maximum rate of 200 per second. An individual bat that boosted its tickover rate would pay an additional price in energy, etc., which would not be justified by the increased sonar acuity. When the only moving object in the immediate vicinity is the bat itself, the apparent world is sufficiently similar in successive tenths of seconds that it need not be sampled more frequently than this. When the salient vicinity includes another moving object, particularly a flying insect twisting and turning and diving in a desperate attempt to shake off its pursuer, the extra benefit to the bat of increasing its sample rate more than justifies the increased cost.


Money and Government: The Past and Future of Economics by Robert Skidelsky

"Friedman doctrine" OR "shareholder theory", Alan Greenspan, anti-globalists, Asian financial crisis, asset-backed security, bank run, banking crisis, banks create money, barriers to entry, Basel III, basic income, Bear Stearns, behavioural economics, Ben Bernanke: helicopter money, Big bang: deregulation of the City of London, book value, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, constrained optimization, Corn Laws, correlation does not imply causation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, David Graeber, David Ricardo: comparative advantage, debt deflation, Deng Xiaoping, Donald Trump, Eugene Fama: efficient market hypothesis, eurozone crisis, fake news, financial deregulation, financial engineering, financial innovation, Financial Instability Hypothesis, forward guidance, Fractional reserve banking, full employment, Gini coefficient, Glass-Steagall Act, Goodhart's law, Growth in a Time of Debt, guns versus butter model, Hyman Minsky, income inequality, incomplete markets, inflation targeting, invisible hand, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kenneth Rogoff, Kondratiev cycle, labour market flexibility, labour mobility, land bank, law of one price, liberal capitalism, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, long and variable lags, low interest rates, market clearing, market friction, Martin Wolf, means of production, Meghnad Desai, Mexican peso crisis / tequila crisis, mobile money, Modern Monetary Theory, Money creation, Mont Pelerin Society, moral hazard, mortgage debt, new economy, Nick Leeson, North Sea oil, Northern Rock, nudge theory, offshore financial centre, oil shock, open economy, paradox of thrift, Pareto efficiency, Paul Samuelson, Phillips curve, placebo effect, post-war consensus, price stability, profit maximization, proprietary trading, public intellectual, quantitative easing, random walk, regulatory arbitrage, rent-seeking, reserve currency, Richard Thaler, rising living standards, risk/return, road to serfdom, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, shareholder value, short selling, Simon Kuznets, structural adjustment programs, technological determinism, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, tontine, too big to fail, trade liberalization, value at risk, Washington Consensus, yield curve, zero-sum game

Available at: http://www.econ.ucl.ac.uk/ downloads/denardi/Sargent_Interview.pdf [Accessed 4 July 2017]. Evening Standard (2009), Darling forecast savaged by IMF’s dire predictions. Evening Standard, 22 April. Fama, E. F. (1991), Efficient capital markets: II. Journal of Finance, 46 (5), pp. 1575–617. Fama, E. F. (1995 (1965)), Random walks in stock market prices. Financial Analysts Journal, 51 (1), pp. 75–80. Reprinted from Financial Analysts Journal, September/October 1965, 21 (5), pp. 55–9. Fazi, T. (2015), QE in the Eurozone has failed. Pieria. Available at: http://www. pieria.co.uk/articles/qe_in_the_Eurozone_has_failed [Accessed 11 July 2017].

Mon e y, t h e G r e at De c e i v e r The central claim of the classical dichotomy is that the value of money (or average level of prices) makes no difference to the relative prices of goods and services. If all prices go up together, it makes no difference to the price ratios at which goods exchange. If this is so, attention to the quantity of money might seem redundant. However, experience showed that while the value of money or price level itself didn’t matter, changes in it did. Rising prices were associated with prosperity; falling prices with dearth. This correlation led a group of seventeenth-century thinkers called mercantilists to identify money with wealth.

Formally, ‘market efficiency requires that in setting the prices of securities at any time t-1, the market correctly uses all available information. For simplicity, assume that the prices at t-1 depend only on the characteristics of the joint distribution of prices to 420 No t e s 13 14 15 16 17 18 19 20 21 22 23 24 be set at t. Market efficiency then requires that in setting prices at t-1, the market correctly uses all available information to assess the joint distribution of prices at t. Formally, in an efficient market, f(Pt|φt − 1) = fm(Pt|φt − 1m), where Pt=(p1t,...,pnt) is the vector of prices of securities at time t, φt-1 is the set of information available at t-1, φt − 1m is the set of information used by the market, fm(Pt|φt − 1m) is the market assessed density function for Pt, and f(Pt|φt − 1) is the true density function implied by φt − 1.’


pages: 661 words: 185,701

The Future of Money: How the Digital Revolution Is Transforming Currencies and Finance by Eswar S. Prasad

access to a mobile phone, Adam Neumann (WeWork), Airbnb, algorithmic trading, altcoin, bank run, barriers to entry, Bear Stearns, Ben Bernanke: helicopter money, Bernie Madoff, Big Tech, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bretton Woods, business intelligence, buy and hold, capital controls, carbon footprint, cashless society, central bank independence, cloud computing, coronavirus, COVID-19, Credit Default Swap, cross-border payments, cryptocurrency, deglobalization, democratizing finance, disintermediation, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, eurozone crisis, fault tolerance, fiat currency, financial engineering, financial independence, financial innovation, financial intermediation, Flash crash, floating exchange rates, full employment, gamification, gig economy, Glass-Steagall Act, global reserve currency, index fund, inflation targeting, informal economy, information asymmetry, initial coin offering, Internet Archive, Jeff Bezos, Kenneth Rogoff, Kickstarter, light touch regulation, liquidity trap, litecoin, lockdown, loose coupling, low interest rates, Lyft, M-Pesa, machine readable, Mark Zuckerberg, Masayoshi Son, mobile money, Money creation, money market fund, money: store of value / unit of account / medium of exchange, Network effects, new economy, offshore financial centre, open economy, opioid epidemic / opioid crisis, PalmPilot, passive investing, payday loans, peer-to-peer, peer-to-peer lending, Peter Thiel, Ponzi scheme, price anchoring, profit motive, QR code, quantitative easing, quantum cryptography, RAND corporation, random walk, Real Time Gross Settlement, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk tolerance, risk/return, Robinhood: mobile stock trading app, robo advisor, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seigniorage, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, smart contracts, SoftBank, special drawing rights, the payments system, too big to fail, transaction costs, uber lyft, unbanked and underbanked, underbanked, Vision Fund, Vitalik Buterin, Wayback Machine, WeWork, wikimedia commons, Y Combinator, zero-sum game

Within a week of Charles Schwab’s announcement, other major brokerage firms such as E*TRADE, Interactive Brokers, and TD Ameritrade had also dropped commissions on trades to zero. Another such online advisor, Wealthfront, was set up in 2008 and soon hired Burton Malkiel, a Princeton University finance professor, as its chief investment officer. Malkiel’s influential 1973 book, A Random Walk down Wall Street, launched the passive investing revolution four decades ago. His basic thesis was that the typical investor would be better off buying and holding low-cost index funds rather than trading in individual securities or investing in actively managed index funds. There might be some gains to undertaking a carefully crafted set of investments in other financial assets, but high fees and trading costs would render any gains in returns modest at best.

Much like many stocks whose prices fizzle after IPOs, the post-ICO price trajectories of these offerings have drawn attention. The prices of many of these tokens seem to take a tumble soon after their ICOs. Any analysis of price developments is tricky; these prices are so volatile that the day and even the minute they are measured can make a big difference. EOS tokens were trading at about $13.68 on June 4, 2018. By September 4, 2018, the price had fallen by more than half, to $6.54. Telegram tokens bucked the trend, with a price increase in the first few months after its ICO, followed by intense bouts of price volatility (with a high of $4.90 and a low of $1.92) before settling down in late 2019 and early 2020 at around the ICO price.

For instance, consider a situation in which a user places an order to buy some tokens for a particular price and then, seeing the price of the token drop, tries to cancel the order. Seeing the open buy order at a price above the prevailing one, a bot might offer a high transaction fee to induce a miner to complete the transaction before the cancellation order can be processed. The bot would buy the tokens at the lower price and complete the user’s original transaction at the higher price. The bot makes a profit on the difference, and the user is stuck with tokens purchased at an unfavorable price. Bots often try and front-run each other, resulting in back-and-forth contests to profit from users’ mistakes.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, Anthropocene, anti-communist, artificial general intelligence, autism spectrum disorder, autonomous vehicles, backpropagation, barriers to entry, Bayesian statistics, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, Computing Machinery and Intelligence, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, Demis Hassabis, demographic transition, different worldview, Donald Knuth, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, en.wikipedia.org, endogenous growth, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, general purpose technology, Geoffrey Hinton, Gödel, Escher, Bach, hallucination problem, Hans Moravec, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John Markoff, John von Neumann, knowledge worker, Large Hadron Collider, longitudinal study, machine translation, megaproject, Menlo Park, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Nick Bostrom, Norbert Wiener, NP-complete, nuclear winter, operational security, optical character recognition, paperclip maximiser, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, search costs, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, Strategic Defense Initiative, strong AI, superintelligent machines, supervolcano, synthetic biology, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, time dilation, Tragedy of the Commons, transaction costs, trolley problem, Turing machine, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, World Values Survey, zero-sum game

16 However, it may not be necessary to make detailed predictions about the system’s entire future trajectory in order to identify an intervention that can be reasonably expected to increase the chances of a certain long-term outcome. One might, for example, consider only the relatively near-term and predictable effects in a detailed way, selecting an action that does well in regard to those, while modeling the system’s behavior beyond the predictability horizon as a random walk. There may, however, be a moral case for de-emphasizing or refraining from second-guessing moves. Trying to outwit one another looks like a zero-sum game—or negative-sum, when one considers the time and energy that would be dissipated by the practice as well as the likelihood that it would make it generally harder for anybody to discover what others truly think and to be trusted when expressing their own opinions.17 A full-throttled deployment of the practices of strategic communication would kill candor and leave truth bereft to fend for herself in the backstabbing night of political bogeys.

At some point, the high-frequency traders started withdrawing from the market, drying up liquidity while prices continued to fall. At 2:45 p.m., trading on the E-Mini was halted by an automatic circuit breaker, the exchange’s stop logic functionality. When trading was restarted, a mere five seconds later, prices stabilized and soon began to recover most of the losses. But for a while, at the trough of the crisis, a trillion dollars had been wiped off the market, and spillover effects had led to a substantial number of trades in individual securities being executed at “absurd” prices, such as one cent or 100,000 dollars. After the market closed for the day, representatives of the exchanges met with regulators and decided to break all trades that had been executed at prices 60% or more away from their pre-crisis levels (deeming such transactions “clearly erroneous” and thus subject to post facto cancellation under existing trade rules).70 The retelling here of this episode is a digression because the computer programs involved in the Flash Crash were not particularly intelligent or sophisticated, and the kind of threat they created is fundamentally different from the concerns we shall raise later in this book in relation to the prospect of machine superintelligence.

Analytic systems use an assortment of data-mining techniques and time series analysis to scan for patterns and trends in securities markets or to correlate historical price movements with external variables such as keywords in news tickers. Financial news providers sell newsfeeds that are specially formatted for use by such AI programs. Other systems specialize in finding arbitrage opportunities within or between markets, or in high-frequency trading that seeks to profit from minute price movements that occur over the course of milliseconds (a timescale at which communication latencies even for speed-of-light signals in optical fiber cable become significant, making it advantageous to locate computers near the exchange).


pages: 651 words: 180,162

Antifragile: Things That Gain From Disorder by Nassim Nicholas Taleb

"World Economic Forum" Davos, Air France Flight 447, Alan Greenspan, Andrei Shleifer, anti-fragile, banking crisis, Benoit Mandelbrot, Berlin Wall, biodiversity loss, Black Swan, business cycle, caloric restriction, caloric restriction, Chuck Templeton: OpenTable:, commoditize, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, fail fast, financial engineering, financial independence, Flash crash, flying shuttle, Gary Taubes, George Santayana, Gini coefficient, Helicobacter pylori, Henri Poincaré, Higgs boson, high net worth, hygiene hypothesis, Ignaz Semmelweis: hand washing, informal economy, invention of the wheel, invisible hand, Isaac Newton, James Hargreaves, Jane Jacobs, Jim Simons, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, knowledge economy, language acquisition, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, Marc Andreessen, Mark Spitznagel, meta-analysis, microbiome, money market fund, moral hazard, mouse model, Myron Scholes, Norbert Wiener, pattern recognition, Paul Samuelson, placebo effect, Ponzi scheme, Post-Keynesian economics, power law, principal–agent problem, purchasing power parity, quantitative trading / quantitative finance, Ralph Nader, random walk, Ray Kurzweil, rent control, Republic of Letters, Ronald Reagan, Rory Sutherland, Rupert Read, selection bias, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, synthetic biology, tacit knowledge, tail risk, Thales and the olive presses, Thales of Miletus, The Great Moderation, the new new thing, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Malthus, too big to fail, transaction costs, urban planning, Vilfredo Pareto, Yogi Berra, Zipf's Law

Quarterly Journal of Economics 124(4): 1593–1638. Mandelbrot, Benoît B., 1983, The Fractal Geometry of Nature. W. H. Freeman. Mandelbrot, Benoît B., 1997, Fractals and Scaling in Finance: Discontinuity, Concentration, Risk. New York: Springer-Verlag. Mandelbrot, Benoît B., and N. N. Taleb, 2010, “Random Jump, Not Random Walk.” In Richard Herring, ed., The Known, the Unknown, and the Unknowable. Princeton, N.J.: Princeton University Press. Mansel, P., 2012, Levant. Hachette. Marglin, S. A., 1996, “Farmers, Seedsmen, and Scientists: Systems of Agriculture and Systems of Knowledge.” In Frédérique Apffel-Marglin and Stephen A.

All he knew is that suckers exist. If you asked any intelligent “analyst” or journalist at the time, he would have predicted a rise in the price of oil in the event of war. But that causal link was precisely what Tony could not take for granted. So he bet against it: they are all prepared for a rise in oil from war, so the price must have adjusted to it. War could cause a rise in oil prices, but not scheduled war—since prices adjust to expectations. It has to be “in the price,” as he said. Indeed, on the news of war, oil collapsed from around $39 a barrel to almost half that value, and Tony turned his investment of three hundred thousand into eighteen million dollars.

But assume now that in a two-country world, a country specialized in wine, hoping to sell its specialty in the market to the other country, and that suddenly the price of wine drops precipitously. Some change in taste caused the price to change. Ricardo’s analysis assumes that both the market price of wine and the costs of production remain constant, and there is no “second order” part of the story. Click here for a larger image of this table. The logic: The table above shows the cost of production, normalized to a selling price of one unit each, that is, assuming that these trade at equal price (1 unit of cloth for 1 unit of wine). What looks like the paradox is as follows: that Portugal produces cloth cheaper than Britain, but should buy cloth from there instead, using the gains from the sales of wine.


pages: 319 words: 105,949

Skyfaring: A Journey With a Pilot by Mark Vanhoenacker

Airbus A320, Boeing 747, British Empire, Cape to Cairo, computer age, dark matter, digital map, Easter island, Edmond Halley, Joan Didion, John Harrison: Longitude, Louis Blériot, Maui Hawaii, Nelson Mandela, out of africa, phenotype, place-making, planetary scale, Ralph Waldo Emerson, random walk, the built environment, transcontinental railway, Year of Magical Thinking

The designers of these light-boxes speak of the body frame, the local level frame and the earth frame. They deal in gravitational vectors, the transport rate, the earth rate, and days referenced not to the sun but to sidereal time, the rotation of the planet against the background light of distant stars. The engineers responsible for inertial systems talk of random walk and coasting, northing and easting, and the spherical harmonic expansions. There is another dimension to the poetry of inertial systems on many commercial aircraft. They require a few minutes of perfectly motionless concentration and reflection on the ground before each flight. This moment of Zen, or a sort of preflight meditation that a nervous flyer might practice, is called alignment.

In the lobby are a vending machine and a glass-topped counter through which I can see several shelves of maps and navigation tools for sale. On the wall behind is a bulletin board with all-capital letters stuck to it, of the type that you see in delis and diners. Here it’s a menu, too, a list of the aviation services provided at this airfield, and their prices. I know these prices by heart. I’ve been saving from my paper route and restaurant job, and now I’m here for my first flying lesson. It’s early autumn, one of those clear, warm, bone-dry, and mosquito-free New England days, of the sort that draw people to Northern California when they realize they can enjoy them there the whole year.


pages: 480 words: 138,041

The Book of Woe: The DSM and the Unmaking of Psychiatry by Gary Greenberg

addicted to oil, Albert Einstein, Asperger Syndrome, autism spectrum disorder, back-to-the-land, David Brooks, Edward Jenner, impulse control, invisible hand, Isaac Newton, John Snow's cholera map, Kickstarter, late capitalism, longitudinal study, Louis Pasteur, McMansion, meta-analysis, neurotypical, phenotype, placebo effect, random walk, selection bias, statistical model, theory of mind, Winter of Discontent

Nor will it be because the attempt to catalog our suffering is doomed to be a fool’s errand, that our troubles will always outdistance our attempts to take their measure. It will be because the Keystone Kops bungled the job. Only naiveté or animus toward psychiatry or a writer’s fervent wish for drama could make someone read more into the unfolding events than incompetence, to see the DSM-5 as anything other than one more step in the long, random walk of human folly. But there is a reason insiders trot out the one-bad-apple defense when disasters occur. It distracts from the more disturbing truth—in this case, that a profession that has been struggling to establish its credentials for more than a century, that has lurched from crisis to crisis, always for the same reason, always because it cannot make good on its claim to be treating diseases as other doctors do—that such a profession has something rotten at its foundation: its have-it-both-ways, real-until-it-isn’t diagnostic manual.

Indeed, the “professional insane”—doctors, lawyers, teachers, and the like—were uniquely subject to the demands that “arise from excessive culture and overburden the mental powers.” Which is why, he thought, 3.75 percent of them were on the rolls in Massachusetts. “From all this survey5,” Jarvis concluded in 1872, “we are irresistibly drawn to the conclusion that insanity is a part of the price we are paying for the imperfection of our civilization.” Jarvis’s conclusion made the particulars of his patients’ afflictions less important than their demographics and geography and economics, and their relief more a matter of social than medical remedy. This may well have reflected some idealism on his part and a sense that psychiatry’s job was to help perfect civilization rather than to cure individuals.

Psychiatry may have been low-hanging fruit for Grassley, but it was even riper picking for the pharmaceutical industry. • • • “With every new revelation46, our credibility with patients has been damaged, and we have to protect that first and foremost,” former APA president Steven Sharfstein told The New York Times in the aftermath of the Grassley investigation. “The price we pay for these kinds of revelations is credibility,” E. Fuller Torrey, one of the country’s most influential psychiatrists, chimed in, “and we just can’t afford to lose any more of that in this field.” These doctors probably didn’t know just how closely they were echoing the lament of Thomas Salmon.


pages: 1,171 words: 309,640

To Sleep in a Sea of Stars by Christopher Paolini

back-to-the-land, clean water, Colonization of Mars, cryptocurrency, dark matter, friendly fire, gravity well, heat death of the universe, hive mind, independent contractor, low earth orbit, mandelbrot fractal, megastructure, random walk, risk tolerance, time dilation, Vernor Vinge

“How long until they realize the Wallfish isn’t in front of them?” Kira asked. He shrugged. “No idea. Best-case scenario, a couple of hours. Worst case, sometime in the next thirty minutes. Either way, it should still be enough time to get out of their FTL sensor range.” “And then what?” A flicker of sly cunning crossed Falconi’s face. “We take a random walk, that’s what.” He jerked his thumb toward the aft of the ship. “The UMC gave us more than enough antimatter to fly to Bughunt and back. We’re using the spare to make a few extra hops, changing course each time, to throw off anyone trying to follow us.” “But,” said Kira, trying to visualize the whole arrangement in her head, “they can still flash trace us, right?”

Being once again trapped in his nutrient bath with no way to contact the outside world would be a nightmare. She shuddered at the thought. “Who cares if he’s happy?” Falconi growled. He ran a hand through his hair. “Right now we have to get out of Sol before we get blown up. Can you set up a new course?” “Yes, sir.” “Do it, then. Program another random walk. Three jumps should do it.” Hwa-jung returned to her seat and concentrated on her overlays. A minute later, the free-fall alert sounded and the sense of crushing weight vanished as the engines cut out. The Soft Blade kept Kira welded to the back of her chair as the Wallfish reoriented itself.

“You are Astradhari, Ms. Kira.” “Somehow I doubt that, but … I do like the name. The Varunastra.” The doc smiled slightly and handed her a towel. “It is named after the god Varuna. He who made it.” “And what is the price for using the Varunastra?” said Kira as she wiped the gel off her arm. “There’s always a price for using the weapons of the gods.” Vishal put away the ultrasound. “There is no price per se, Ms. Kira, but it must be used with great care.” “Why?” The doc seemed reluctant to answer, but at last he said, “If you lose control of the Varunastra, it can destroy you.” “Is that so?” said Kira.


pages: 626 words: 181,434

I Am a Strange Loop by Douglas R. Hofstadter

Albert Einstein, Andrew Wiles, Benoit Mandelbrot, Brownian motion, Charles Babbage, double helix, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Hans Moravec, Isaac Newton, James Watt: steam engine, John Conway, John von Neumann, language acquisition, mandelbrot fractal, pattern recognition, Paul Erdős, place-making, probability theory / Blaise Pascal / Pierre de Fermat, publish or perish, random walk, Ronald Reagan, self-driving car, Silicon Valley, telepresence, Turing machine

Analogously, a brain is a thinking machine, and if we’re interested in understanding what thinking is, we don’t want to focus on the trees (or their leaves!) at the expense of the forest. The big picture will become clear only when we focus on the brain’s large-scale architecture, rather than doing ever more fine-grained analyses of its building blocks. At some point a billion years or so ago, natural selection, in its usual random-walk fashion, bumped into cells that contracted rhythmically, and little beings possessing such cells did well for themselves because the cells’ contractions helped send useful stuff here and there inside the being itself. Thus, by accident, were pumps born, and in the abstract design space of all such proto-pumps, nature favored designs that were more efficient.

With my hopefully amusing little list (which I pared down from a much longer one), I am trying to get across the flavor of most adults’ daily mental reality — the bread-and-butter sorts of symbols that are likely to be awakened from dormancy in one’s brain as one goes about one’s routines, talking with friends and colleagues, sitting at a traffic light, listening to radio programs, flipping through magazines in a dentist’s waiting room, and so on. My list is a random walk through an everyday kind of mental space, drawn up in order to give a feel for the phenomena in which we place the most stock and in which we most profoundly believe (sour grapes and wild goose chases being quite real to most of us), as opposed to the forbidding and inaccessible level of quarks and gluons, or the only slightly more accessible level of genes and ribosomes and transfer RNA — levels of “reality” to which we may pay lip service but which very few of us ever think about or talk about.

In truth, this concept involves dozens and dozens of other concepts, among which are the following: “grocery cart”, “line”, “customers”, “to wait”, “candy rack”, “candy bar”, “tabloid newspaper”, “movie stars”, “trashy headlines”, “sordid scandals”, “weekly TV schedule”, “soap opera”, “teenager”, “apron”, “nametag”, “cashier”, “mindless greeting”, “cash register”, “keyboard”, “prices”, “numbers”, “addition”, “scanner”, “bar code”, “beep”, “laser”, “moving belt”, “frozen food”, “tin can”, “vegetable bag”, “weight”, “scale”, “discount coupon”, “rubber separator bar”, “to slide”, “bagger”, “plastic bag”, “paper bag”, “plastic money”, “paper money”, “to load”, “to pay”, “credit card”, “debit card”, “to swipe”, “receipt”, “ballpoint pen”, “to sign”, and on and on.


pages: 340 words: 91,416

Lost in Math: How Beauty Leads Physics Astray by Sabine Hossenfelder

Adam Curtis, Albert Einstein, Albert Michelson, anthropic principle, Arthur Eddington, Brownian motion, clockwork universe, cognitive bias, cosmic microwave background, cosmological constant, cosmological principle, crowdsourcing, dark matter, data science, deep learning, double helix, game design, Henri Poincaré, Higgs boson, income inequality, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, Johannes Kepler, Large Hadron Collider, Murray Gell-Mann, Nick Bostrom, random walk, Richard Feynman, Schrödinger's Cat, Skype, Stephen Hawking, sunk-cost fallacy, systematic bias, TED Talk, the scientific method

The 1/f spectrum has—theoretically—no typical time scale to it, contrary to the expectation that verse meters or beats mark the type of music. The study therefore reveals that sound patterns in music have self-similarities or “correlations” that stretch over all time scales. White noise would show a constant spectrum and no correlation between fluctuations. A random walk moving a melody along adjacent pitches would have a strong correlation and a 1/f spectrum. Somewhere in between, Voss and Clarke showed, are Bach, the Beatles, and everything else you hear on the radio.2 Intuitively this means that good music lives on the edge between predictability and unpredictability.

The psychologist Irvin Yalom has identified meaninglessness as one of our four existential fears, and we work hard to avoid it.14 Indeed, many cognitive shortcomings, such as our predisposition for wishful thinking (which psychologists prefer to call “motivated cognition”), are there to protect us from the harshness of reality. But as a scientist, you have to let go of comforting delusions. This isn’t always easy. What your equations reveal might not be what you hoped for—and the price can be steep. Joe is one of the most intellectually honest people I know, always willing to go with an argument regardless of whether he likes where it takes him—as the examples with the black hole firewall and the multiverse demonstrate. This makes him an exceptionally clear thinker, though sometimes he doesn’t like at all the conclusions logic has forced on him.


pages: 624 words: 180,416

For the Win by Cory Doctorow

anti-globalists, barriers to entry, book value, Burning Man, company town, creative destruction, double helix, Internet Archive, inventory management, lateral thinking, loose coupling, Maui Hawaii, microcredit, New Journalism, off-the-grid, planned obsolescence, Ponzi scheme, post-materialism, printed gun, random walk, reality distortion field, RFID, San Francisco homelessness, Silicon Valley, skunkworks, slashdot, speech recognition, stem cell, Steve Jobs, Steve Wozniak, supply-chain management, technoutopianism, time dilation, union organizing, wage slave, work culture

He ignored them as he lurched around the box’s perimeter until he came to a far corner, then another hatch slid away and the little man reached inside and tugged out the plug and the end of the power-cord. He hugged the plug to his chest and began to wander around Sammy’s desk, clearly looking for an electrical outlet. “It’s a random-walk search algorithm,” one of the Imagineers said. “Watch this.” After a couple of circuits of Sammy’s desk the little robot went to the edge and jumped, hanging on to the power-cable, which unspooled slowly from the box like a belay-line, gently lowering the man to the ground. A few minutes later, he had found the electrical outlet and plugged in the box.

“That brings us back to the question of your relationship with Kodacell. They want to do what, exactly, with you?” “Well, we’ve been playing with some mass-production techniques, the three-dee printer and so on. When Kettlebelly called me, he said that he wanted to see about using the scanner and so on to make a lot of these things, at a low price-point. It’s pretty perverse when you think about it: using modern technology to build replicas of obsolete technology rescued from the dump, when these replicas are bound to end up back here at the dump!” He laughed. He had nice laugh-lines around his eyes. “Anyway, it’s something that Lester and I had talked about for a long time, but never really got around to.

In a good market, you invent something and you charge all the market will bear for it. Someone else figures out how to do it cheaper, or decides they can do it for a slimmer margin—not the same thing, you know, in the first case someone is more efficient and in the second they’re just less greedy or less ambitious. They do it and so you have to drop your prices to compete. Then someone comes along who’s less greedy or more efficient than both of you and undercuts you again, and again, and again, until eventually you get down to a kind of firmament, a baseline that you can’t go lower than, the cheapest you can produce a good and stay in business. That’s why straightpins, machine screws and reams of paper all cost basically nothing, and make damned little profit for their manufacturers.


pages: 728 words: 182,850

Cooking for Geeks by Jeff Potter

3D printing, A Pattern Language, air gap, carbon footprint, centre right, Community Supported Agriculture, Computer Numeric Control, crowdsourcing, Donald Knuth, double helix, en.wikipedia.org, European colonialism, fear of failure, food miles, functional fixedness, hacker house, haute cuisine, helicopter parent, Internet Archive, iterative process, Kickstarter, lolcat, Parkinson's law, placebo effect, random walk, Rubik’s Cube, slashdot, stochastic process, TED Talk, the scientific method

It’s really amazing, and while the thought of "cheese or cream-like" eggs might not have you racing off to the kitchen, it’s really worth a try! In a bowl, crack two or three eggs and whisk thoroughly to combine the whites and yolks. Don’t add any salt or other seasonings; do this with just eggs. Transfer to a nonstick pan on a burner set to heat as low as possible. Stir continuously with a silicone spatula, doing a "random walk" so that your spatula hits all parts of the pan. And low heat means really low heat: there’s no need for the pan to exceed 160°F / 71°C, because enough of the proteins in both the yolks and whites denature below that temperature and the proteins will weep some of their water as they get hotter.

Around the world, advances in technology have increased crop yields and improved the quality of life for many, although there are still many in starving conditions. What happens to those families who are just barely making ends meet when the prices of food exceed what they can afford? Non-GMO foods are not inherently more expensive, but the economics to date have tended to make the price of GMO foods cheaper. The quick-serve industry is not saying "we want GMO foods"; they’re simply buying what’s most economical, because in a price-sensitive market, the chains need to keep prices down to remain in business. For a glimpse into the interconnectedness of our food system, search online for Louise Fresco’s touching TED talk, "On Feeding the Whole World" (http://www.ted.com/talks/louise_fresco_on_feeding_the_whole_world.html).

Pairing is to give you more pleasure. Any tips for a consumer speaking with a sommelier? Well, certainly telling sommeliers what you like. Also, a lot of people feel like they need to dance around price. There is an easy way to do this if you’re entertaining guests and you don’t want to make a big show of not spending a lot of money. When I’m talking to a guest I will have the list open right there so they can run their finger along the name of the wine to the price. They tell me what they’re interested in spending. If you wanted to say, "We’re looking for something around $100," that’s fine, too, but this is kind of a genteel way of doing it.


pages: 678 words: 216,204

The Wealth of Networks: How Social Production Transforms Markets and Freedom by Yochai Benkler

affirmative action, AOL-Time Warner, barriers to entry, bioinformatics, Brownian motion, business logic, call centre, Cass Sunstein, centre right, clean water, commoditize, commons-based peer production, dark matter, desegregation, digital divide, East Village, Eben Moglen, fear of failure, Firefox, Free Software Foundation, game design, George Gilder, hiring and firing, Howard Rheingold, informal economy, information asymmetry, information security, invention of radio, Isaac Newton, iterative process, Jean Tirole, jimmy wales, John Markoff, John Perry Barlow, Kenneth Arrow, Lewis Mumford, longitudinal study, machine readable, Mahbub ul Haq, market bubble, market clearing, Marshall McLuhan, Mitch Kapor, New Journalism, optical character recognition, pattern recognition, peer-to-peer, power law, precautionary principle, pre–internet, price discrimination, profit maximization, profit motive, public intellectual, radical decentralization, random walk, Recombinant DNA, recommendation engine, regulatory arbitrage, rent-seeking, RFID, Richard Stallman, Ronald Coase, scientific management, search costs, Search for Extraterrestrial Intelligence, SETI@home, shareholder value, Silicon Valley, Skype, slashdot, social software, software patent, spectrum auction, subscription business, tacit knowledge, technological determinism, technoutopianism, The Fortune at the Bottom of the Pyramid, the long tail, The Nature of the Firm, the strength of weak ties, Timothy McVeigh, transaction costs, vertical integration, Vilfredo Pareto, work culture , Yochai Benkler

What we are seeing on the network is that filtering for both relevance and accreditation has become the object of widespread practices of mutual pointing, of peer review, of pointing to original sources of claims, and its complement, the social practice that those who have some ability to evaluate the claims in fact do comment on them. The second element is a contingent but empirically confirmed observation of how users actually use the network. As a descriptive matter, information flow in the network is much more ordered than a simple random walk in the cacophony of information flow would suggest, and significantly less centralized than the mass media environment was. Some sites are much more visible and widely read than others. This is true both when one looks at the Web as a whole, and when one looks at smaller clusters of similar sites or users who tend to cluster.

[pg 109] 214 A market transaction, in order to be efficient, must be clearly demarcated as to what it includes, so that it can be priced efficiently. That price must then be paid in equally crisply delineated currency. Even if a transaction initially may be declared to involve sale of "an amount reasonably required to produce the required output," for a "customary" price, at some point what was provided and what is owed must be crystallized and fixed for a formal exchange. The crispness is a functional requirement of the price system. It derives from the precision and formality of the medium of exchange--currency--and the ambition to provide refined representations of the comparative value of marginal decisions through denomination in an exchange medium that represents these incremental value differences.

They are, rather, generalized judgments by the vendor as to what terms are most attractive for it that the market will bear. Unlike rival economic goods, information goods sold at a positive price in reliance on copyright are, by definition, priced above marginal cost. The information itself is nonrival. Its marginal cost is zero. Any transaction priced above the cost of communication is evidence of some market power in the hands of the provider, used to price based on value and elasticity of demand, not on marginal cost. Moreover, the vast majority of users are unlikely to pay close attention to license details they consider to be boilerplate.


pages: 721 words: 197,134

Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić

Albert Einstein, algorithmic bias, backpropagation, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application

This can go around many times; each technique is used to probe slightly different aspects of data—to ask a slightly different question of the data. What is essentially being described here is a voyage of discovery that makes modern data mining exciting. Still, data mining is not a random application of statistical and machine-learning methods and tools. It is not a random walk through the space of analytic techniques but a carefully planned and considered process of deciding what will be most useful, promising, and revealing. It is important to realize that the problem of discovering or estimating dependencies from data or discovering totally new data is only one part of the general experimental procedure used by scientists, engineers, and others who apply standard steps to draw conclusions from the data.

Two features describe customers: The first feature is the number of items the customers bought, and the second feature shows the price they paid for each. TABLE 9.1. Sample Set of Clusters Consisting of Similar Objects Number of Items Price Cluster 1 2 1700 3 2000 4 2300 Cluster 2 10 1800 12 2100 11 2500 Cluster 3 2 100 3 200 3 350 Customers in Cluster 1 purchase a few high-priced items; customers in Cluster 2 purchase many high-priced items; and customers in Cluster 3 purchase few low-priced items. Even this simple example and interpretation of a cluster’s characteristics shows that clustering analysis (in some references also called unsupervised classification) refers to situations in which the objective is to construct decision boundaries (classification surfaces) based on unlabeled training data set.

One good case study is that of U.S. economist Orley Ashenfelter, who used data-mining techniques to analyze the quality of French Bordeaux wines. Specifically, he sought to relate auction prices to certain local annual weather conditions, in particular, rainfall and summer temperatures. His finding was that hot and dry years produced the wines most valued by buyers. Ashenfelter’s work and analytical methodology resulted in a deluge of hostile invective from established wine-tasting experts and writers. There was a fear of losing a lucrative monopoly, and the reality that a better informed market is more difficult to manipulate on pricing. Another interesting study is that of U.S. baseball analyst William James, who applied analytical methods to predict which of the players would be most successful in the game, challenging the traditional approach.


pages: 420 words: 130,714

Science in the Soul: Selected Writings of a Passionate Rationalist by Richard Dawkins

agricultural Revolution, Alfred Russel Wallace, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Boeing 747, book value, Boris Johnson, David Attenborough, Donald Trump, double helix, Drosophila, epigenetics, fake news, Fellow of the Royal Society, Ford Model T, Google Earth, Gregor Mendel, John Harrison: Longitude, Kickstarter, lone genius, Mahatma Gandhi, mental accounting, Necker cube, Neil Armstrong, nuclear winter, out of africa, p-value, phenotype, place-making, placebo effect, precautionary principle, public intellectual, random walk, Ray Kurzweil, Richard Feynman, Search for Extraterrestrial Intelligence, stem cell, Stephen Hawking, Steve Wozniak, Steven Pinker, Stuart Kauffman, the long tail, the scientific method, twin studies, value engineering

Random drift may make it easier for selection to do its job by assisting the escape from local optima, but it is still selection that is determining the rise of adaptive complexity.*17 Recently paleontologists have come up with fascinating results when they perform computer simulations of ‘random phylogenies’. These random walks through evolutionary time produce trends that look uncannily like real ones, and it is disquietingly easy, and tempting, to read into the random phylogenies apparently adaptive trends which, however, are not there. But this does not mean that we can admit random drift as an explanation of real adaptive trends.

Unfortunately, it depends whose intuition you choose. Where aims (if not methods) are concerned, your own intuitions coincide with mine. I wholeheartedly share your aim of long-term stewardship of our planet, with its diverse and complex biosphere.*3 But what about the instinctive wisdom in Saddam Hussein’s black heart?*4 What price the Wagnerian wind that rustled Hitler’s twisted leaves? The Yorkshire Ripper heard religious voices in his head urging him to kill. How do we decide which intuitive inner voices to heed? This, it is important to say, is not a dilemma that science can solve. My own passionate concern for world stewardship is as emotional as yours.

‘Jarvis,’ I sang out, as I latchkeyed self into the old headquarters, shedding hat and stick on my way through the hall to consult the oracle. ‘I say, Jarvis, what about these buses?’ ‘Sir?’ ‘You know, Jarvis, the buses, the “What is this that roareth thus?”*2 brigade, the bendy buses, the conveyances with the kink amidships. What’s going on? What price the bendy bus campaign?’ ‘Well, sir, I understand that, while flexibility is often considered a virtue, these particular omnibuses have not given uniform satisfaction. Mayor Johnson…’ ‘Never mind Mayor Johnson, Jarvis. Consign Boris to the back burner and bend the bean to the buses. I’m not referring to their bendiness per se, if that is the right expression.’


Growth: From Microorganisms to Megacities by Vaclav Smil

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, agricultural Revolution, air freight, Alan Greenspan, American Society of Civil Engineers: Report Card, Anthropocene, Apollo 11, Apollo Guidance Computer, autonomous vehicles, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Boeing 747, Bretton Woods, British Empire, business cycle, caloric restriction, caloric restriction, carbon tax, circular economy, colonial rule, complexity theory, coronavirus, decarbonisation, degrowth, deindustrialization, dematerialisation, demographic dividend, demographic transition, Deng Xiaoping, disruptive innovation, Dissolution of the Soviet Union, Easter island, endogenous growth, energy transition, epigenetics, Fairchild Semiconductor, Ford Model T, general purpose technology, Gregor Mendel, happiness index / gross national happiness, Helicobacter pylori, high-speed rail, hydraulic fracturing, hydrogen economy, Hyperloop, illegal immigration, income inequality, income per capita, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, Isaac Newton, James Watt: steam engine, knowledge economy, Kondratiev cycle, labor-force participation, Law of Accelerating Returns, longitudinal study, low interest rates, mandelbrot fractal, market bubble, mass immigration, McMansion, megacity, megaproject, megastructure, meta-analysis, microbiome, microplastics / micro fibres, moral hazard, Network effects, new economy, New Urbanism, old age dependency ratio, optical character recognition, out of africa, peak oil, Pearl River Delta, phenotype, Pierre-Simon Laplace, planetary scale, Ponzi scheme, power law, Productivity paradox, profit motive, purchasing power parity, random walk, Ray Kurzweil, Report Card for America’s Infrastructure, Republic of Letters, rolodex, Silicon Valley, Simon Kuznets, social distancing, South China Sea, synthetic biology, techno-determinism, technoutopianism, the market place, The Rise and Fall of American Growth, three-masted sailing ship, total factor productivity, trade liberalization, trade route, urban sprawl, Vilfredo Pareto, yield curve

Average biovolumes of arthropods have shown no clear growth trend for half a billion years, while the average biovolumes of Mammalia grew by about three orders of magnitude during the past 150 million years (figure 2.15). Several animal groups (including marine animals, terrestrial mammals, and non-avian dinosaurs) show size increase over their evolution (confirming Cope’s rule) but statistical analyses make it clear that unbiased random walk is the best evolutionary trend for five animal phyla (Brachipoda, Chordata, Echinodermata, Foraminifera, Mollusca), while stasis captures best the evolution of arthropod sizes. Figure 2.15 Evolutionary trends of biovolumes for Dinosauria and Mammalia. Simplified from Smith et al. (2016). Growth of Domesticated Animals By far the greatest changes of animal growth have resulted from domestication, whose origins go back more than 10 millennia.

Other ways to judge the improving performance of vacuum tubes is to look at their falling prices and at the ownership of new electronic consumer products that their advances made possible. Price declines are commonly highest during the early stages of commercial diffusion and, according to Okamura (1995), prices of Japanese receiving tubes (the country was an early pioneer of mm-wave power generation) dropped by more than 90% between 1925 and 1940. In 1924, four years after the beginning of licensed US radio broadcasting, a high-quality best-selling model, RCA AR-812, was priced at $220, just $45 less than the Runabout version of the dependable Ford Model T (Radiomuseum 2017).

Data on the average power of newly sold US cars are available from 1975 when the mean was 106 kW; it declined (due to a spike in oil prices and a sudden preference for smaller vehicles) to 89 kW by 1981, but then (as oil prices retreated) it kept on rising, passing 150 kW in 2003 and reaching the record level of nearly 207 kW in 2013, with the 2015 mean only slightly lower at 202 kW (Smil 2014b; USEPA 2016b). During the 112 years between 1903 and 2015 the average power of light-duty vehicles sold in the US thus rose roughly 34 times. The growth trajectory was linear, average gain was 1.75 kW/year, and notable departures from the trend in the early 1980s and after 2010 were due, respectively, to high oil prices and to more powerful (heavier) SUVs (figure 3.9).


pages: 450 words: 569

ANSI Common LISP by Paul Graham

Donald Knuth, functional programming, general-purpose programming language, L Peter Deutsch, Paul Graham, premature optimization, Ralph Waldo Emerson, random walk

As an 8.8 EXAMPLE: RANDOM TEXT 139 example, this section shows how to write a program to generate random text. The first part of the program will read a sample text (the larger the better), accumulating information about the likelihood of any given word following another. The second part will take random walks through the network of words built in the first, after each word making a weighted random choice among the words that followed it in the original sample. The resulting text will always be locally plausible, because any two words that occur together will be two words that occurred together in the input text.

We can specify the type of method combination to be used by a generic function with a :method-combinat ion clause in a call to def generic: (defgeneric p r i c e (x) (:method-combination +)) Now the p r i c e method will use + method combination; any def met hods for p r i c e must have + as the second argument. If we define some classes with prices, ( d e f c l a s s j a c k e t () ( ) ) ( d e f c l a s s t r o u s e r s () ()) ( d e f c l a s s s u i t (jacket t r o u s e r s ) ()) (defmethod p r i c e + ( ( j k j a c k e t ) ) 350) (defmethod p r i c e + ( ( t r t r o u s e r s ) ) 200) then when we ask for the price of an instance of s u i t , we get the sum of the applicable p r i c e methods: > ( p r i c e (make-instance 550 'suit)) The following symbols can be used as the second argument to def method or in the : met hod-combination option to defgeneric: + and append list max min nconc or progn You can also use standard, which yields standard method combination.


pages: 846 words: 232,630

Darwin's Dangerous Idea: Evolution and the Meanings of Life by Daniel C. Dennett

Albert Einstein, Alfred Russel Wallace, anthropic principle, assortative mating, buy low sell high, cellular automata, Charles Babbage, classic study, combinatorial explosion, complexity theory, computer age, Computing Machinery and Intelligence, conceptual framework, Conway's Game of Life, Danny Hillis, double helix, Douglas Hofstadter, Drosophila, finite state, Garrett Hardin, Gregor Mendel, Gödel, Escher, Bach, heat death of the universe, In Cold Blood by Truman Capote, invention of writing, Isaac Newton, Johann Wolfgang von Goethe, John von Neumann, junk bonds, language acquisition, Murray Gell-Mann, New Journalism, non-fiction novel, Peter Singer: altruism, phenotype, price mechanism, prisoner's dilemma, QWERTY keyboard, random walk, Recombinant DNA, Richard Feynman, Rodney Brooks, Schrödinger's Cat, selection bias, Stephen Hawking, Steven Pinker, strong AI, Stuart Kauffman, the scientific method, theory of mind, Thomas Malthus, Tragedy of the Commons, Turing machine, Turing test

In the long run, natural selection — redesign at the genotype level — will tend to follow the lead o/and confirm the directions taken by the individual organisms' successful explorations — redesign at the individual or phenotype level. The way I have just described the Baldwin Effect certainly keeps Mind to {79} FIGURE 3.2 a minimum, if not altogether out of the picture; all it requires is some brute, mechanical capacity to stop a random walk when a Good Thing comes along, a minimal capacity to "recognize" a tiny bit of progress, to "learn" something by blind trial and error. In fact, I have put it in behavioristic terms. What Baldwin discovered was that creatures capable of "reinforcement learning" not only do better individually than creatures that are entirely "hard-wired"; their species will evolve faster because of its greater capacity to discover design improvements in the neighborhood.6 This is not how Baldwin described the effect he proposed.

And the creatures that don't have eyes at all are neither better nor worse on any absolute scale of design; their lineage has just never been given this problem to solve. It is this same variability in luck in the various lineages that makes it impossible to define a single Archimedean point from which global progress could be measured. Is it progress when you have to work an extra job to pay for the high-priced mechanic you have to hire to fix your car when it breaks because it is too complex for you to fix in the way you used to fix your old clunker? Who is to say? Some lineages get trapped in (or are lucky enough to wander into — take your pick) a path in Design Space in which complexity begets complexity, in an arms race of competitive design.

It can't fix anything, except by selecting and discarding. So in every evolutionary process — and hence in every true evolutionary explanation — there is always a faint but disconcerting odor of something dicey. I will call this phenomenon bait-and-switch, after the shady practice of attracting customers by advertising something at a bargain price and then, when you've lured them to the store, trying to sell them a substitute. Unlike that practice, evolutionary bait-and-switch is not really nefarious; it just seems to be, because it doesn't explain what at first you thought you wanted explained. It subtly changes the topic. We saw the ominous shadow of bait-and-switch in its purest form in chapter 2, in the weird wager that I can produce somebody who wins ten consecutive coin-tosses without a loss.


pages: 804 words: 212,335

Revelation Space by Alastair Reynolds

game design, glass ceiling, gravity well, Kuiper Belt, planetary scale, random walk, statistical model, time dilation, VTOL

Weightless, he jetted the suit into the chamber, towards the dancing jewel and the source of searingly beautiful light. When Volyova came around, it was to the sound of the radar warning siren, which meant that the Infinity was preparing to re-aim its grasers. It would not take it more than a few seconds to do so, even allowing for her random-walk evasive manoeuvre. She glanced at the hull health indicator and saw that they were down to only a few remaining millimetres of sacrificial metal, that the chaff throwers were depleted, and that — realistically — they could withstand no more than one or two additional bursts of graser-strike. 'Are we still here?'

The Knowledge crashed home, vast and impassive as a glacier, something she could never begin to forget. And she knew something else, which was, she supposed, the whole point of this exercise. She understood why Sylveste had to die. And why — if it took the death of a planet to ensure his death — that was an entirely reasonable price to pay. Guards came just as Sylveste was falling into shallow dreams, exhausted by the latest operation. 'Wake up, sleepy-head,' said the taller of the two, a stocky man with a drooping grey moustache. 'What have you come for?' 'Now that would spoil the surprise,' said the other guard, a weaselly individual hefting a rifle.

And she had no doubt that those hypothetical future investigators would come to a totally wrong conclusion regarding the layer's origin. It would never occur to them that it had been put there by an act of conscious volition... Volyova had slept only a few hours in the last thirty, but her nervous energy currently seemed limitless. She would, of course, pay a price for it at some point in the near future, but for now she felt like she was careering, imbued with unstoppable momentum. Even so, she did not immediately snap to alertness when Hegazi steered his chair next to hers. 'What is it?' 'I'm getting something which might very much be our boy.' 'Sylveste?'


Fateful Triangle: The United States, Israel, and the Palestinians (Updated Edition) (South End Press Classics Series) by Noam Chomsky

active measures, American ideology, anti-communist, Ayatollah Khomeini, Berlin Wall, centre right, colonial rule, David Brooks, disinformation, European colonialism, facts on the ground, Fall of the Berlin Wall, information security, Monroe Doctrine, New Journalism, public intellectual, random walk, Ronald Reagan, Silicon Valley, strikebreaker, Suez crisis 1956, the market place, Thomas L Friedman

The officer we sought could not be reached at once (he was engaged in wiretapping, we were casually informed), but when he arrived, we explained what had happened and he called the patrol and ordered them to drop the matter. Luckily, there was “protection” in this case. Classics in Politics: The Fateful Triangle Noam Chomsky The Palestinian Uprising 818 The pattern is common. Israeli journalist Tom Segev reports what happened when an Arab lawyer told him that a random walk through Jerusalem would yield ample evidence of intimidation and humiliation of Arabs. Skeptical, Segev walked with him through Jerusalem, where he was stopped repeatedly by Border Guards to check his identification papers. One ordered him: “Come here, jump.” Laughing, he dropped the papers on the road and ordered the lawyer to pick them up.

Laqueur did not draw the further conclusion that the industrial and agricultural resources of the West might also be internationalized, “not on behalf of a few corporations, but for the benefit of the rest of mankind,” even though “by the end of 1973, U.S. wheat exports cost three times as much per ton as they had little more than a year before,” to cite just one illustration of the sharp rise in commodity prices that preceded or accompanied the rise of oil prices. Those who perceive an inconsistency need only be reminded of the crucial distinction between significant and insignificant people. As discussed earlier, Palestinians are not only “insignificant people” but are much lower in the ranking, because they interfere with the plans of the world’s most “significant people”: privileged Americans and Israeli Jews (as long as they keep their place).

For FY 1983, the Reagan administration requested almost $2.5 billion for Israel out of a total aid budget of $8.1 billion, including $500 million in outright grants and $1.2 billion in low-interest loans.4 In addition, there is a regular pattern of forgiving loans, offering weapons at special discount prices, and a variety of other devices, not to mention the taxdeductible “charitable” contributions (in effect, an imposed tax), used in ways to which we return.5 Not content with this level of assistance from the American taxpayer, one of the Senate’s most prominent liberal Democrats, Alan Cranston of California, “proposed an amendment to the foreign aid bill to establish the principle that American economic assistance to Israel would not be less than the amount of debt Israel repays to the United States,” a commitment to cover “all Israeli debts and future debts,” as Senator Charles Percy commented.6 This was before the Lebanon war.


pages: 1,051 words: 334,334

Gravity's Rainbow by Thomas Pynchon

centre right, classic study, company town, Eratosthenes, experimental subject, invisible hand, Isaac Newton, ought to be enough for anybody, plutocrats, random walk

Shiny black India-rubber cables snake away into the forest to connect the ground equipment with the Dutch grid's 380 volts. Erwartung. . . . For some reason he finds it harder these days to remember. What is framed, dirt-blurry, in the prisms, the ritual, the daily iteration inside these newly cleared triangles in the forests, has taken over what used to be memory's random walk, its innocent image-gathering. His time away, with Katje and Gottfried, has become shorter and more precious as the tempo of firings quickens. Though the boy is in Blicero's unit, the captain hardly sees him when they're on duty—a flash of gold helping the surveyors chain the kilometers out to the transmitting station, the guttering brightness of his hair in the wind, vanishing among trees. . . .

Springer's wearing a suit of fine tweed this morning, very Savile Row, fits perfectly— "Närrisch needed your help." "You don't know what you're talking about." His eyes are steelies that never lose. His laugh, subtitled Humoring the Fools, is Mittel-europäisch and mirthless. "All right, all right. How much do you want?" "Everything's got a price, right?" But he's not being noble here, no, what it is is that his own price has just occurred to him, and he needs to shim the talk here, give it a second to breathe and develop. "Everything." "What's the deal?" "A minor piracy. Pick up one package for me while I cover you." He looks at his watch, hamming it up. "O.K., get me a discharge, I'll come with you."

But the stimulus, somehow, must be the rocket, some precursor wraith, some rocket's double present for Slothrop in the percentage of smiles on a bus, menstrual cycles being operated upon in some mysterious way—what does make the little doxies do it for free? Are there fluctuations in the sexual market, in pornography or prostitutes, perhaps tying in to prices on the Stock Exchange itself, that we clean-living lot know nothing about? Does news from the front affect the itch between their pretty thighs, does desire grow directly or inversely as the real chance of sudden death—damn it, what cue, right in front of our eyes, that we haven't the subtlety of heart to see?


pages: 1,351 words: 385,579

The Better Angels of Our Nature: Why Violence Has Declined by Steven Pinker

1960s counterculture, affirmative action, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, availability heuristic, behavioural economics, Berlin Wall, Boeing 747, Bonfire of the Vanities, book value, bread and circuses, British Empire, Broken windows theory, business cycle, California gold rush, Cass Sunstein, citation needed, classic study, clean water, cognitive dissonance, colonial rule, Columbine, computer age, Computing Machinery and Intelligence, conceptual framework, confounding variable, correlation coefficient, correlation does not imply causation, crack epidemic, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Brooks, delayed gratification, demographic transition, desegregation, Doomsday Clock, Douglas Hofstadter, Dr. Strangelove, Edward Glaeser, en.wikipedia.org, European colonialism, experimental subject, facts on the ground, failed state, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, fudge factor, full employment, Garrett Hardin, George Santayana, ghettoisation, Gini coefficient, global village, Golden arches theory, Great Leap Forward, Henri Poincaré, Herbert Marcuse, Herman Kahn, high-speed rail, Hobbesian trap, humanitarian revolution, impulse control, income inequality, informal economy, Intergovernmental Panel on Climate Change (IPCC), invention of the printing press, Isaac Newton, lake wobegon effect, libertarian paternalism, long peace, longitudinal study, loss aversion, Marshall McLuhan, mass incarceration, McMansion, means of production, mental accounting, meta-analysis, Mikhail Gorbachev, mirror neurons, moral panic, mutually assured destruction, Nelson Mandela, nuclear taboo, Oklahoma City bombing, open economy, Peace of Westphalia, Peter Singer: altruism, power law, QWERTY keyboard, race to the bottom, Ralph Waldo Emerson, random walk, Republic of Letters, Richard Thaler, Ronald Reagan, Rosa Parks, Saturday Night Live, security theater, Skinner box, Skype, Slavoj Žižek, South China Sea, Stanford marshmallow experiment, Stanford prison experiment, statistical model, stem cell, Steven Levy, Steven Pinker, sunk-cost fallacy, technological determinism, The Bell Curve by Richard Herrnstein and Charles Murray, the long tail, The Wealth of Nations by Adam Smith, theory of mind, Timothy McVeigh, Tragedy of the Commons, transatlantic slave trade, trolley problem, Turing machine, twin studies, ultimatum game, uranium enrichment, Vilfredo Pareto, Walter Mischel, WarGames: Global Thermonuclear War, WikiLeaks, women in the workforce, zero-sum game

The exception that proves the rule is the insular societies that are starved of ideas from the rest of the world and muzzled by governmental and clerical repression of the press: these are also the societies that most stubbornly resist humanism and cling to their tribal, authoritarian, and religious ideologies (chapter 6). But even these societies may not be able to withstand the liberalizing currents of the new electronic Republic of Letters forever. The metaphor of an escalator, with its implication of directionality superimposed on the random walk of ideological fashion, may seem Whiggish and presentist and historically naïve. Yet it is a kind of Whig history that is supported by the facts. We saw that many liberalizing reforms that originated in Western Europe or on the American coasts have been emulated, after a time lag, by the more conservative parts of the world (chapters 4, 6, and 7).

When care and harm are extended outside intimate circles, then they are simply a part of the logic of fairness.)176 Fiske’s final relational model is Market Pricing: the system of currency, prices, rents, salaries, benefits, interest, credit, and derivatives that powers a modern economy. Market Pricing depends on numbers, mathematical formulas, accounting, digital transfers, and the language of formal contracts. Unlike the other three relational models, Market Pricing is nowhere near universal, since it depends on literacy, numeracy, and other recently invented information technologies. The logic of Market Pricing remains cognitively unnatural as well, as we saw in the widespread resistance to interest and profits until the modern era.

The logic of Market Pricing remains cognitively unnatural as well, as we saw in the widespread resistance to interest and profits until the modern era. One can line up the models, Fiske notes, along a scale that more or less reflects their order of emergence in evolution, child development, and history: Communal Sharing > Authority Ranking > Equality Matching > Market Pricing. Market Pricing, it seems to me, is specific neither to markets nor to pricing. It really should be lumped with other examples of formal social organization that have been honed over the centuries as a good way for millions of people to manage their affairs in a technologically advanced society, but which may not occur spontaneously to untutored minds.177 One of these institutions is the political apparatus of democracy, where power is assigned not to a strongman (Authority) but to representatives who are selected by a formal voting procedure and whose prerogatives are delineated by a system of laws.


pages: 945 words: 292,893

Seveneves by Neal Stephenson

Apollo 13, Biosphere 2, clean water, Colonization of Mars, Danny Hillis, digital map, double helix, epigenetics, fault tolerance, Fellow of the Royal Society, Filipino sailors, gravity well, hydroponic farming, Isaac Newton, Jeff Bezos, kremlinology, Kuiper Belt, low earth orbit, machine readable, microbiome, military-industrial complex, Neal Stephenson, orbital mechanics / astrodynamics, phenotype, Potemkin village, pre–internet, random walk, remote working, selection bias, side project, Silicon Valley, Skype, Snow Crash, space junk, statistical model, Stewart Brand, supervolcano, tech billionaire, TED Talk, the scientific method, Tunguska event, VTOL, zero day, éminence grise

Once Dinah had pawed those out of the way she was able to more fully take in the scene: Maxim, jammed in a narrow human-sized tunnel through a mass of vitamins that had been packed into the Soyuz until it couldn’t hold any more. Someone down in Tyuratam had had the foresight to cram in a few folded-up garbage bags. Taking the hint, Dinah peeled one of them open and used it to corral all the items that had escaped so far and were threatening to go on a random walk around Izzy. Then she began raking out more. Lots of stuff escaped, but most of it went into the bag. Maxim eased himself out into the Hub for a stretch. He’d been crammed into this thing for six hours. Dinah, who was smaller, went into the space he’d vacated and began throwing vitamins out to him; he just held up a garbage bag to catch them.

“You know, now, the decision I made. Which was to suffer for the greater good. Because society will go astray if there are not those who, like me, imagine many outcomes. Let those scenarios run rampant in their minds. Anticipate the worst that could happen. Take steps to prevent it. If the price of that—the price of having a head full of dark imaginings—is personal suffering, then so be it.” “But would you wish that on your progeny?” “Of course not,” Julia said. “If there were a way to have one without the other—the foresight without the misery—I would take it in a heartbeat.” “We only need a few people of this mentality,” Tekla said.

Until a string of beeps came out of the hissing speaker zip-tied to the bulkhead, and her eyes went momentarily out of focus as her brain decoded the dots and dashes into a string of letters and numbers: her father’s call sign. “Not now, Pa,” she muttered, with a guilty daughter’s glance at the brass-and-oak telegraph key he had given her—a Victorian relic purchased at great price on eBay, during a bidding war that had placed Rufus into pitched battle against a host of science museums and interior decorators. LOOK AT THE MOON “Not now, Pa, I know the moon’s pretty, I’m right in the middle of debugging this method . . .” OR WHAT USED TO BE IT “Huh?” And then she brought her face close to the window and twisted her neck to find the moon.


pages: 1,201 words: 233,519

Coders at Work by Peter Seibel

Ada Lovelace, Bill Atkinson, bioinformatics, Bletchley Park, Charles Babbage, cloud computing, Compatible Time-Sharing System, Conway's Game of Life, Dennis Ritchie, domain-specific language, don't repeat yourself, Donald Knuth, fallacies of distributed computing, fault tolerance, Fermat's Last Theorem, Firefox, Free Software Foundation, functional programming, George Gilder, glass ceiling, Guido van Rossum, history of Unix, HyperCard, industrial research laboratory, information retrieval, Ken Thompson, L Peter Deutsch, Larry Wall, loose coupling, Marc Andreessen, Menlo Park, Metcalfe's law, Multics, no silver bullet, Perl 6, premature optimization, publish or perish, random walk, revision control, Richard Stallman, rolodex, Ruby on Rails, Saturday Night Live, side project, slashdot, speech recognition, systems thinking, the scientific method, Therac-25, Turing complete, Turing machine, Turing test, type inference, Valgrind, web application

Taking something apart and looking at it is how you learn to build your own. At least for me. I've read very few books about computers. My experience has been digging through source code or reference manuals. I've got a goal and, alright, to do this I need to know what this thing does and what this thing does. And I'll just sort of random-walk through that until I find where I'm going. Seibel: Have you read Knuth's, The Art of Computer Programming? Zawinski: I haven't. And that's one of those things where, I really probably should have. But I never did. Seibel: It's tough going—you need a lot of math to really grok it. Zawinski: And I'm not a math person at all.

Everything we built was because the site was falling over and we were working all night to build a new infrastructure thing. We bought one NetApp ever. We asked, “How much does it cost?” and they're like, “Tell us about your business model.” “We have paid accounts.” “How many customers do you have? What do you charge?” You just see them multiplying. “The price is: all the disposable income you have without going broke.” We're like, “Fuck you.” But we needed it, so we bought one. We weren't too impressed with the I/O on it and it was way too expensive and there was still a single point of failure. They were trying to sell us a configuration that would be high availability and we were like, “Fuck it.

Those were the hard bits. Seibel: Because they're expensive. Peyton Jones: They're so expensive—yes. You could get the electrical parts for the kind of money students could afford but printers were typically big, fridge-sized line printer things. They had a lot of mechanics in them that made them completely out of our price bracket. That and storage devices—any kind of permanent storage device tended to be tricky. So we tended to have computers with a keyboard, a screen, and not much else. And some kind of primitive tape mechanism. Seibel: You guys were building these computers from scratch in '76 to '79. Isn't that about the same time the Altair was coming out?


pages: 1,234 words: 356,472

Pandora's Star by Peter F. Hamilton

Apollo 11, carbon-based life, clean water, corporate governance, disinformation, Magellanic Cloud, megacity, Neil Armstrong, nuclear winter, operational security, plutocrats, random walk, rolodex, Rubik’s Cube, stem cell, the scientific method, trade route, urban sprawl

There are some calls you can’t make sitting here. I’d like you to take over as general coordinator today.” “Me?” “Yes, you have the qualifications, you’ve taken command of raids before.” “Okay.” He was trying not to smile. “Maggie, you’re with me.” They caught up with Adam Elvin as he was taking a slow, seemingly random walk through Burghal Park. He did something similar most mornings, an amble through a wide-open space where it was difficult for the team to follow unobtrusively on foot. Paula and Maggie waited in the back of a ten-seater car that was parked at the north end of Burghal Park. The team had the rest of their vehicles spaced evenly around the perimeter, with three officers on foot using their retinal inserts to track his position, never getting closer than five hundred meters, boxing him the whole time.

In freefall the grain will simply sit there, it has to be physically pushed out. And do you know why they do it?” “The market,” Nigel Murphy said with a hint of weariness. “Damn right: the market. If there’s ever a glut of food, the prices go down. Commodity traders can’t have that; they can’t sell at enough profit to pay for the gamble they’ve made on the work of others, so the market demands less food to go around. The grain trains roll through the zero-end gateways, and people pay higher prices for basic food. Any society which allows that to happen is fundamentally wrong. And grain is just the tiniest part of the abuse people are subject to thanks to the capitalist market economy.”

Eight months later, those were then exchanged for Gansu Construction shares when Morton agreed to the buyout. All very standard. Then they just sat there until she was re-lifed, at which point she transferred them back to her accountant on Oaktier.” “What about the dividends?” “Gansu was an excellent deal. They’ve paid dividends every four months, and the share price has gone up twelve times their original price in that time—Morton is a good director. The money went straight into the bank’s long-term investment account, which also did reasonably well over seventeen years, although the percentage was lower than most managed funds. No money was ever taken out; it stayed there and grew for her.


pages: 1,737 words: 491,616

Rationality: From AI to Zombies by Eliezer Yudkowsky

Albert Einstein, Alfred Russel Wallace, anthropic principle, anti-pattern, anti-work, antiwork, Arthur Eddington, artificial general intelligence, availability heuristic, backpropagation, Bayesian statistics, behavioural economics, Berlin Wall, Boeing 747, Build a better mousetrap, Cass Sunstein, cellular automata, Charles Babbage, cognitive bias, cognitive dissonance, correlation does not imply causation, cosmological constant, creative destruction, Daniel Kahneman / Amos Tversky, dematerialisation, different worldview, discovery of DNA, disinformation, Douglas Hofstadter, Drosophila, Eddington experiment, effective altruism, experimental subject, Extropian, friendly AI, fundamental attribution error, Great Leap Forward, Gödel, Escher, Bach, Hacker News, hindsight bias, index card, index fund, Isaac Newton, John Conway, John von Neumann, Large Hadron Collider, Long Term Capital Management, Louis Pasteur, mental accounting, meta-analysis, mirror neurons, money market fund, Monty Hall problem, Nash equilibrium, Necker cube, Nick Bostrom, NP-complete, One Laptop per Child (OLPC), P = NP, paperclip maximiser, pattern recognition, Paul Graham, peak-end rule, Peter Thiel, Pierre-Simon Laplace, placebo effect, planetary scale, prediction markets, random walk, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Rubik’s Cube, Saturday Night Live, Schrödinger's Cat, scientific mainstream, scientific worldview, sensible shoes, Silicon Valley, Silicon Valley startup, Singularitarianism, SpaceShipOne, speech recognition, statistical model, Steve Jurvetson, Steven Pinker, strong AI, sunk-cost fallacy, technological singularity, The Bell Curve by Richard Herrnstein and Charles Murray, the map is not the territory, the scientific method, Turing complete, Turing machine, Tyler Cowen, ultimatum game, X Prize, Y Combinator, zero-sum game

Since the Great War of 1957, countries have been reluctant to openly endorse or condemn heaps of large size, since this leads so easily to war. Indeed, some Pebblesorting philosophers—who seem to take a tangible delight in shocking others with their cynicism—have entirely denied the existence of pebble-sorting progress; they suggest that opinions about pebbles have simply been a random walk over time, with no coherence to them, the illusion of progress created by condemning all dissimilar pasts as incorrect. The philosophers point to the disagreement over pebbles of large size, as proof that there is nothing that makes a heap of size 91 really incorrect—that it was simply fashionable to build such heaps at one point in time, and then at another point, fashionable to condemn them.

I looked at his program, and it went something like this: 10 Preheat oven to 350 20 Combine all ingredients in a large mixing bowl 30 Mix until smooth * * * An introductory programming student once asked me to look at his program and figure out why it was always churning out zeroes as the result of a simple computation. I looked at the program, and it was pretty obvious: begin read("Number of Apples", apples) read("Number of Carrots", carrots) read("Price for 1 Apple", a_price) read("Price for 1 Carrot", c_price) write("Total for Apples", a_total) write("Total for Carrots", c_total) write("Total", total) total = a_total + c_total a_total = apples * a_price c_total = carrots * c_price end Me: “Well, your program can’t print correct results before they’re computed.” Him: “Huh? It’s logical what the right solution is, and the computer should reorder the instructions the right way.”

3 A 29/36 chance to win $2. A 7/36 chance to win $9. While the average prices (equivalence values) placed on these options were $1.25 and $2.11 respectively, their mean attractiveness ratings were 13.2 and 7.5. Both the prices and the attractiveness rating were elicited in a context where subjects were told that two gambles would be randomly selected from those rated, and they would play the gamble with the higher price or higher attractiveness rating. (Subjects had a motive to rate gambles as more attractive, or price them higher, that they would actually prefer to play.) The gamble worth more money seemed less attractive, a classic preference reversal.


Engineering Security by Peter Gutmann

active measures, address space layout randomization, air gap, algorithmic trading, Amazon Web Services, Asperger Syndrome, bank run, barriers to entry, bitcoin, Brian Krebs, business process, call centre, card file, cloud computing, cognitive bias, cognitive dissonance, cognitive load, combinatorial explosion, Credit Default Swap, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Debian, domain-specific language, Donald Davies, Donald Knuth, double helix, Dr. Strangelove, Dunning–Kruger effect, en.wikipedia.org, endowment effect, false flag, fault tolerance, Firefox, fundamental attribution error, George Akerlof, glass ceiling, GnuPG, Google Chrome, Hacker News, information security, iterative process, Jacob Appelbaum, Jane Jacobs, Jeff Bezos, John Conway, John Gilmore, John Markoff, John von Neumann, Ken Thompson, Kickstarter, lake wobegon effect, Laplace demon, linear programming, litecoin, load shedding, MITM: man-in-the-middle, Multics, Network effects, nocebo, operational security, Paradox of Choice, Parkinson's law, pattern recognition, peer-to-peer, Pierre-Simon Laplace, place-making, post-materialism, QR code, quantum cryptography, race to the bottom, random walk, recommendation engine, RFID, risk tolerance, Robert Metcalfe, rolling blackouts, Ruby on Rails, Sapir-Whorf hypothesis, Satoshi Nakamoto, security theater, semantic web, seminal paper, Skype, slashdot, smart meter, social intelligence, speech recognition, SQL injection, statistical model, Steve Jobs, Steven Pinker, Stuxnet, sunk-cost fallacy, supply-chain attack, telemarketer, text mining, the built environment, The Death and Life of Great American Cities, The Market for Lemons, the payments system, Therac-25, too big to fail, Tragedy of the Commons, Turing complete, Turing machine, Turing test, Wayback Machine, web application, web of trust, x509 certificate, Y2K, zero day, Zimmermann PGP

If you stop and reason through it rationally, it’s just as clear that there’s no more chance of getting heads than tails. Depending on which approach you take it’s possible to flip-flop between the two points of view, seeing first one and then the other alternative as the obvious answer. The gambler’s fallacy is even more evident in the stock market, which essentially follows a random walk [284] with just enough apparent short-term predictability to fool people, and provides an ongoing source of material (and amusement) for psychologists. A huge number of studies, far too many to go through here, have explored this effect in more detail, with applied psychology professor Keith Stanovich providing a good survey [167].

A nasty side-effect of zero-risk bias in security is that if you do eventually manage to reduce some particular security risk to zero or close to it, the resulting system frequently ends up being completely unusable or unfit for any larger purpose, so that people will avoid it and use something else instead [60]. The number zero holds a special appeal for humans7. There’s a phenomenon related to zero-risk bias called the zero-price effect in which people place disproportionately high value in things priced at zero, or more specifically FREE!!! (with the capitals and exclamation marks and everything), so that paying (for example) one cent for something won’t change their buying patterns much but paying nothing at all changes them radically [61][62]. This is typically exploited by marketers through an offer of some FREE!!!

Indeed, cynics would say that this was exactly the problem that certificates and CAs were supposed to solve in the first place, and that “high-assurance” certificates are just a way of charging a second time for an existing service. A few years ago certificates still cost several hundred dollars, but now that the shifting baseline of certificate prices and quality has moved to the point where you can get them for $9.95 (or even for nothing at all) the big commercial CAs have had to reinvent themselves by defining a new standard and convincing the market to go back to the prices paid in the good old days. User Conditioning 73 This déjà-vu-all-over-again approach can be seen by examining Verisign’s certificate practice statement (CPS), the document that governs its certificate issuance.