quantitative trading / quantitative finance

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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, Black-Scholes formula, Bonfire of the Vanities, 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, discounted cash flows, disintermediation, diversification, Donald Knuth, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, full employment, George Akerlof, Gordon Gekko, hiring and firing, implied volatility, index fund, interest rate derivative, interest rate swap, John von Neumann, linear programming, Loma Prieta earthquake, Long Term Capital Management, margin call, market friction, market microstructure, martingale, merger arbitrage, Myron Scholes, Nick Leeson, P = NP, pattern recognition, Paul Samuelson, pensions crisis, performance metric, prediction markets, profit maximization, purchasing power parity, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Richard Stallman, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, sorting algorithm, statistical arbitrage, statistical model, stem cell, Steven Levy, stochastic process, 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

Peter was running a proprietary trading group that was considered by everyone to be the crème de la crème of the equities division. I did not know—and still do not know—exactly what they did, but whenever his group was mentioned, people were abuzz with excitement. Peter was outgoing and friendly and made an effort to get to know the quant research guys. We became friends and he was eventually a big influence on my decision to pursue quantitative trading. Quant Research and the Mathematics of Portfolio Trading I want to start with some general comments about how I view research in financial mathematics. I believe that one incredibly important application of quantitative research is in improving business processes. Many business units in finance are governed by rules of thumb and best practices that have never been fully analyzed. I believe this was certainly the case in program trading.


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, 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, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, 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, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, 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, Sergey Aleynikov, short selling, 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

As stocks tumbled, pressure increased on portfolio insurers to sell futures, racing to keep up with the widely gapping market in a devastating feedback loop. The arbs scrambled to put on their trades but were overwhelmed: futures and stocks were falling in unison. Chaos ruled. Fischer Black watched the disaster with fascination from his perch at Goldman Sachs in New York, where he’d taken a job managing quantitative trading strategies. Robert Jones, a Goldman trader, dashed into Black’s office to report on the carnage. “I put in an order to sell at market and it never filled,” he said, describing a frightening scenario in which prices are falling so fast there seems to be no set point where a trade can be executed. “Wow, really?” Black said, clapping his hands with glee. “This is history in the making!” In the final seventy-five minutes of trading on October 19, the decline hit full throttle as portfolio insurance sellers dumped futures and sell orders flowed in from brokerage accounts around the country.

In short order, he helped design BARRA’s best-selling Alphabuilder system, a PC-based software program that could analyze expected returns for stock portfolios. Then he quit. “Who the fuck are you, and why the fuck do you get an office?” “I’m fucking Peter Muller, and I’m fucking pleased to meet you.” Muller stared bullets at the wiseass Morgan Stanley salesman who’d barged into his office as though he owned it. Muller had only recently begun setting up a quantitative trading group at Morgan, and this was the reception he got? It had been like this since the day he arrived at the bank. After accepting a job at Morgan, and with it an incredible increase in pay, he’d given notice at BARRA and taken six weeks of R&R, spending most of it in Kauai, the lush, westernmost island of Hawaii. The transition from the placid green gardens of Kauai to the rock-’em-sock-’em trading floor of Morgan Stanley in midtown Manhattan had been a rude shock.

The Canadian acrobat squad Cirque du Soleil performed. Disco diva Donna Summer sang. Guests dangled from helium balloons. The party in Paris included festivities at the Louvre and a rehearsal dinner at the Musée d’Orsay. It was good to be Ken Griffin. Perhaps too good. MULLER Just as Griffin was starting up Citadel in Chicago, Peter Muller was hard at work at Morgan Stanley in New York trying to put together his own quantitative trading outfit using the models he’d devised at BARRA. In 1991, he pulled the trigger, flipping on the computers. It was a nightmare. Nothing worked. The sophisticated trading models he’d developed at BARRA were brilliant in theory. But when Muller actually traded with them, he ran into all sorts of problems. The execution wasn’t fast enough. Trading costs were lethal. Small bugs in a program could screw up an order.


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

algorithmic trading, asset allocation, backtesting, 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, latency arbitrage, margin call, market design, market fragmentation, market fundamentalism, market microstructure, martingale, natural language processing, offshore financial centre, pattern recognition, price discovery process, price discrimination, price stability, quantitative trading / quantitative finance, random walk, Sharpe ratio, statistical arbitrage, statistical model, stochastic process, Tobin tax, transaction costs, two-sided market, yield curve

He is a fellow of the Econometric Society and has served as an associate editor of numerous economics journals. David recently co-authored the book Networks, Crowds and Markets: Reasoning About a Highly Connected World, which combines scientific perspectives from economics, computing and information science, sociology and applied mathematics to describe the emerging field of network science. Marcos López de Prado is head of quantitative trading and research at HETCO, the trading arm of Hess Corporation, a Fortune 100 company. Previously, Marcos was head of global quantitative research at Tudor Investment Corporation, where he also led high-frequency futures trading. In addition to more than 15 years of investment management experience, Marcos has received several academic appointments, including postdoctoral research fellow of RCC at Harvard University, visiting scholar at Cornell University, and research affiliate at Lawrence Berkeley National Laboratory (US Department of Energy’s Office of Science).

Michael Kearns is professor of computer and information science at the University of Pennsylvania, where he holds secondary appointments in the statistics and operations and information management departments of the Wharton School. His research interests include machine learning, algorithmic game theory, quantitative finance and theoretical computer science. Michael also has extensive experience working with quantitative trading and statistical arbitrage groups, including at Lehman Brothers, Bank of America and SAC Capital. David Leinweber was a co-founder of the Center for Innovative Financial Technology at Lawrence Berkeley National Laboratory. Previously, he was visiting fellow at the Hass School of Business and x i i i i i i “Easley” — 2013/10/8 — 11:31 — page xi — #11 i i ABOUT THE AUTHORS at Caltech.

Albert’s research focuses on securities trading, liquidity, asset pricing and financial econometrics. He has published in the Journal of Finance, Journal of Business and Economic Statistics and Journal of Financial and Quantitative Analysis, among others. He has been a member of the Group of Economic Advisors of the European Securities and Market Authority (ESMA) since 2011. Yuriy Nevmyvaka has extensive experience in quantitative trading and statistical arbitrage, including roles as portfolio manager and head of groups at SAC Capital, Bank of America and Lehman Brothers. He has also published extensively on topics in algorithmic trading and market microstructure, and is a visiting scientist in the computer and information science department at the University of Pennsylvania. Yuriy holds a PhD in computer science from Carnegie Mellon University. xi i i i i i i “Easley” — 2013/10/8 — 11:31 — page xii — #12 i i HIGH-FREQUENCY TRADING Richard B.


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

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

The computer algorithms are designed and perhaps programmed by the traders themselves, based on the historical performance of the encoded strategy tested against historical financial data. Is quantitative trading just a fancy name for technical analysis, then? Granted, a strategy based on technical analysis can be part of a quantitative trading system if it can be fully encoded as computer programs. However, not all technical analysis can be regarded as quantitative trading. For example, certain chartist techniques such as “look for the formation of a head and shoulders pattern” might not be included in a quantitative trader’s arsenal because they are quite subjective and may not be quantifiable. Yet quantitative trading includes more than just technical analysis. Many quantitative trading systems incorporate fundamental data in their inputs: numbers such as revenue, cash flow, debt-toequity ratio, and others.

This is also a dangerous emotion to bring to independent quantitative trading. As I hope to persuade you in this chapter and in the rest of the book, instant wealth is not the objective of quantitative trading. The ideal independent quantitative trader is therefore someone who has some prior experience with finance or computer programming, who has enough savings to withstand the inevitable losses and periods without income, and whose emotion has found the right balance between fear and greed. THE BUSINESS CASE FOR QUANTITATIVE TRADING A lot of us are in the business of quantitative trading because it is exciting, intellectually stimulating, financially rewarding, or perhaps it is the only thing we are good at doing. But for others who may have alternative skills and opportunities, it is worth pondering whether quantitative trading is the best business for you.

J AC K E T D ES I G N : PAU L M c C A RT H Y J AC K E T A RT: © D O N R E LY E A —STEVE HALPERN, founder, HCC Capital, LLC How to Build Your Own Algorithmic Trading Business trader and consultant who advises clients on how Quantitative Trading or sophisticated theories. Instead, he highlights the Wiley Trading B y some estimates, quantitative (or algorithmic) trading now accounts for over one-third of trading volume in the United States. While institutional traders continue to implement this highly effective approach, many independent traders—with Quantitative Trading limited resources and less computing power—have wondered if they can still challenge powerful industry professionals at their own game? The answer is “yes,” and in Quantitative Trading, author Dr. Ernest Chan, a respected independent trader and consultant, will show you how. Whether you’re an independent “retail” trader looking to start your own quantitative trading business or an individual who aspires to work as a quantitative trader at a major financial institution, this practical guide contains the information you need to succeed.


pages: 349 words: 134,041

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives by Satyajit Das

accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, 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, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial innovation, fixed income, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Meriwether, locking in a profit, Long Term Capital Management, 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, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk-adjusted returns, risk/return, Satyajit Das, shareholder value, short selling, 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

In the 1980s, research evolved into entertainment. Analysts became a species of trained performing animal. A few firms, like Salomon Brothers, continued to undertake serious research, producing high quality material and introducing sophisticated analytical tools, but they were the exceptions. Serious research moved into the private domain and was mainly applied to prop trading. Salomon’s research effort drove the quantitative trading of its Arbitrage Group and subsequently that of its alumni in Long Term Capital Management (LTCM). But for the most part, the research distributed by investment banks to clients evolved into ‘puffery’ designed to get the client to trade. Analysts were expected to build profile for the firm and for themselves. In the 1980s, economics and the world of money become part of everyday life; Dr Gloom and Dr Doom became household names as they DAS_C03.QXP 8/7/06 4:25 PM Page 63 2 N Beautiful lies – the ‘sell’ side 63 appeared regularly on primetime TV.


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

algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, 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 trading, text mining, transaction costs, Vanguard fund, yield curve

Griffin and Sunny Mahajan 20 Fundamental Analysis and Alpha Research By Xinye Tang and Kailin Qi 21 Introduction to Momentum Alphas By Zhiyu Ma, Arpit Agarwal, and Laszlo Borda 22 The Impact of News and Social Media on Stock Returns By Wancheng Zhang 23 Stock Returns Information from the Stock Options Market By Swastik Tiwari and Hardik Agarwal 24 Institutional Research 101: Analyst Reports By Benjamin Ee, Hardik Agarwal, Shubham Goyal, Abhishek Panigrahy, and Anant Pushkar 69 77 83 89 95 101 111 121 127 133 135 141 149 155 159 169 179 Contentsix 25 26 27 28 29 30 Event-Driven Investing By Prateek Srivastava Intraday Data in Alpha Research By Dusan Timotity Intraday Trading By Rohit Kumar Jha Finding an Index Alpha By Glenn DeSouza ETFs and Alpha Research By Mark YikChun Chan Finding Alphas on Futures and Forwards By Rohit Agarwal, Rebecca Lehman, and Richard Williams 195 207 217 223 231 241 PART IV NEW HORIZON – WEBSIM 31 Introduction to WebSim By Jeffrey Scott 251 253 PART V A FINAL WORD 32 The Seven Habits of Highly Successful Quants By Richard Hu and Chalee Asavathiratham 263 265 References Index 273 291 Preface Much has changed since we published the first edition of Finding Alphas, in 2015. The premise of that edition – that we considered these techniques “the future of trading” – is more true today than it ever was. In the intervening four years, we at WorldQuant have seen remarkable growth in our development of predictive algorithms for quantitative trading – we call them “alphas” – powered by an ever-rising volume and variety of available data, an explosion in computer hardware and software, and increasingly sophisticated techniques that allow us to create and deploy a higher volume and quality of alphas. Today, at WorldQuant, we have produced over 20 million alphas, a number that continues to grow exponentially as we hunt for ever-weaker predictive signals.

The more alphas you have, the better you can describe reality and the more predictive you can be. But change is a constant, and the task is never done. Igor Tulchinsky June 2019 Preface (to the Original Edition) This book is a study of the process of finding alphas. The material is presented as a collection of essays, providing diverse viewpoints from successful quants on the front lines of quantitative trading. A wide variety of topics is covered, ranging from theories about the existence of alphas, to the more concrete and technical aspects of alpha creation. Part I presents a general introduction to alpha creation and is followed by a brief account of the alpha life cycle and insights on cutting losses. Part II focuses more on the technical side of alpha design, such as the dos and don’ts of information research, key steps to developing an alpha, and the evaluation and improvement of quality alphas.

Part III explores ad hoc topics in alpha design, including alpha design for various asset classes like futures and currencies, the development of momentum alphas, and the effect of news and social media on stock returns. In Part IV, we introduce you to WebSim, a web-based alpha development tool. We invite all quant enthusiasts to utilize this free tool to learn about alpha backtesting (also known as alpha simulation) and ultimately to create their own alphas. Finally, in Part V, we present an inspirational essay for all quants who are ready to explore the world of quantitative trading. Acknowledgments In these pages, we present a collection of chapters on the algorithmic-­ based process of developing alphas. The authors of these chapters are WorldQuant’s founder, directors, managers, portfolio managers, and quantitative researchers. This book has two key objectives: to present as many state-of-the-art viewpoints as possible on defining an alpha, and the techniques involved in finding and testing alphas.


pages: 402 words: 110,972

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

AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, 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, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, 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, 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, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

This system, which was kept going strong for 12 years in the form of Investment Technology Group (ITG)’s QuantEx, used AI techniques Perils and Pr omise of Evolutionary Computation on Wall Str eet 189 to allow people to build intelligent graph-watching assistants, but it didn’t do any learning. There were all sorts of uses for it, ranging from market surveillance to proprietary trading, but it just did what you told it to do, no matter how stupid that might be. If you had actually found the holy grail of quantitative trading, but had made only one little mistake—you were buying when you should sell, and selling when you should buy—the system would never notice. But a genetic algorithm would notice this kind of sign error, along with a host of other mistakes and miscalibrations. It seemed to be an ideal tool for tuning, refining, and evolving quantitative investment and trading strategies. In 1989, Henry Lichstein, of Citibank, who was also on the board of the Santa Fe Institute, introduced me to the genetic algorithm.


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The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, blockchain, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, obamacare, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, 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 Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine

Tartaglia had wanted the option of taking his team to a rival if their bonuses ever disappointed. As a result, Morgan Stanley didn’t have strong legal grounds to stop Frey’s trading. With some trepidation, he ignored Morgan Stanley’s continuing threats and began trading. * * * = By 1990, Simons had high hopes Frey and Kepler might find success with their stock trades. He was even more enthused about his own Medallion fund and its quantitative-trading strategies in bond, commodity, and currency markets. Competition was building, however, with some rivals embracing similar trading strategies. Simons’s biggest competition figured to come from David Shaw, another refugee of the Morgan Stanley APT group. After leaving the bank in 1988, the thirty-six-year-old Shaw, who had received his PhD from Stanford University, was courted by Goldman Sachs and was unsure whether to accept the job offer.

“Show me the proof,” he told Simons at the time, asking for evidence that Volfbeyn and Belopolsky had taken Renaissance’s proprietary information. Privately, Englander wondered if Simons’s true fear was the possibility of additional departures from his firm, rather than any theft. Simons wouldn’t share much with his rival. He and Renaissance sued Englander’s firm, as well as Volfbeyn and Belopolsky, while the traders brought countersuits against Renaissance. Amid the hostilities, Volfbeyn and Belopolsky set up their own quantitative-trading system, racking up about $100 million of profits while becoming, as Englander told a colleague, some of the most successful traders Englander had encountered. At Renaissance, Volfbeyn and Belopolsky had signed nondisclosure agreements prohibiting them from using or sharing Medallion’s secrets. They had refused to sign noncompete agreements, though, viewing the firm as underhanded for slipping them in a pile of other papers to be signed, according to a colleague.

Automated trading by computers is a scary concept for many, much as airplanes flown by autopilot and self-driving cars can frighten, despite evidence that those machines improve safety. There’s reason to believe computer traders can amplify or accelerate existing trends. Author and former risk manager Richard Bookstaber has argued that risks today are significant because the embrace of quant models is “system-wide across the investment world,” suggesting that future troubles for these investors would have more impact than in the past.12 As more embrace quantitative trading, the very nature of financial markets could change. New types of errors could be introduced, some of which have yet to be experienced, making them harder to anticipate. Until now, markets have been driven by human behavior, reflecting the dominant roles played by traders and investors. If machine learning and other computer models become the most influential factors in markets, they may become less predictable and maybe even less stable, since human nature is roughly constant while the nature of this kind of computerized trading can change rapidly.


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

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, bank run, beat the dealer, Benoit Mandelbrot, Black-Scholes formula, Bretton Woods, Brownian motion, business cycle, buy and hold, capital asset pricing model, card file, Cass Sunstein, collateralized debt obligation, complexity theory, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discovery of the americas, diversification, diversified portfolio, Edward Glaeser, Edward Thorp, endowment effect, Eugene Fama: efficient market hypothesis, experimental economics, financial innovation, Financial Instability Hypothesis, fixed income, floating exchange rates, George Akerlof, Henri Poincaré, Hyman Minsky, implied volatility, impulse control, index arbitrage, index card, index fund, information asymmetry, invisible hand, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, libertarian paternalism, linear programming, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, market bubble, market design, Myron Scholes, New Journalism, Nikolai Kondratiev, Paul Lévy, Paul Samuelson, pension reform, performance metric, Ponzi scheme, prediction markets, 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, Robert Shiller, rolodex, Ronald Reagan, shareholder value, Sharpe ratio, short selling, side project, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, statistical model, stocks for the long run, 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, value at risk, Vanguard fund, Vilfredo Pareto, volatility smile, Yogi Berra


pages: 483 words: 141,836

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

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, Asian financial crisis, Atul Gawande, backtesting, Basel III, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Black Swan, business cycle, capital asset pricing model, central bank independence, Checklist Manifesto, corporate governance, creative destruction, credit crunch, Credit Default Swap, disintermediation, distributed generation, diversification, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, experimental subject, financial innovation, illegal immigration, implied volatility, index fund, Long Term Capital Management, loss aversion, margin call, market clearing, market fundamentalism, market microstructure, 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, pre–internet, quantitative trading / quantitative finance, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

A few years ago Paul told me he had ceased being a person and had transformed into a brand. Antti Ilmanen wrote an excellent guide to the theory and practice of quant strategies, Expected Returns: An Investor’s Guide to Harvesting Market Rewards. More technical works on the subject are Algorithmic Trading and DMA: An Introduction to Direct Access Trading Strategies by Barry Johnson, Inside the Black Box: The Simple Truth about Quantitative Trading by Rishi K. Narang, and Multifractal Volatility: Theory, Forecasting, and Pricing by Laurent E. Calvet. The view of quantitative finance described in Red-Blooded Risk has a lot of overlap with two pathbreaking but eccentric works: The Handbook of Portfolio Mathematics: Formulas for Optimal Allocation & Leverage by Ralph Vince and Finding Alpha: The Search for Alpha When Risk and Return Break Down by Eric Falkenstein.



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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 intermediation, fixed income, high net worth, implied volatility, index arbitrage, information asymmetry, interest rate swap, inventory management, law of one price, Long Term Capital Management, Louis Bachelier, margin call, market friction, market microstructure, martingale, Myron Scholes, New Journalism, p-value, paper trading, performance metric, profit motive, purchasing power parity, quantitative trading / quantitative finance, random walk, Renaissance Technologies, 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, trade route, transaction costs, value at risk, yield curve, zero-sum game

The second part reviews the statistical and econometric foundations of the common types of 6 HIGH-FREQUENCY TRADING high-frequency strategies. The third part addresses the details of modeling high-frequency trading strategies. The fourth part describes the steps required to build a quality high-frequency trading system. The fifth and last part addresses the issues of running, monitoring, and benchmarking highfrequency trading systems. The book includes numerous quantitative trading strategies with references to the studies that first documented the ideas. The trading strategies discussed illustrate practical considerations behind high-frequency trading. Chapter 10 considers strategies of the highest frequency, with position-holding periods of one minute or less. Chapter 11 looks into a class of high-frequency strategies known as the market microstructure models, with typical holding periods seldom exceeding 10 minutes.

As with technical trading, fundamental trading entails buying a security the price of which was deemed too low relative to its analytically determined fundamental value and selling a security the price of which is considered too high. Like technical trading, fundamental trading can also be applied at any frequency, although price formation or microstructure effects may result in price anomalies at ultrahigh frequencies. Finally, quant (short for quantitative) trading refers to making portfolio allocation decisions based on scientific principles. These principles may be fundamental or technical or can be based on simple statistical relationships. The main difference between quant analyses and technical or fundamental styles is that quants use little or no discretionary judgments, whereas fundamental analysts may exercise discretion in rating the management of the company, for example, and technical analysts may “see” various shapes appearing in the charts.


pages: 584 words: 187,436

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

Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, 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, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, Paul Samuelson, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs

Much like the Simons team, he pursued numerical precision with a zealous intensity: His staff soon discovered that it was no good telling him that a programming task might take three to eight weeks; you had to say that it would take 5.25, but with an error of two weeks.7 Yet for all these similarities, there were differences between Shaw and Simons too. These proved to be significant. Shaw got into finance via Morgan Stanley’s proprietary trading desk, which hired him to create a computer system to support its quantitative trading. It was 1986, and big things were stirring at Morgan. The firm’s secretive Analytical Proprietary Trading unit ran a computerized effort to profit from short-run liquidity effects in stock markets. As Michael Steinhardt had discovered in the 1970s, a big sell order from a pension fund could push a stock’s price out of line; provided that there was no information behind the sale—that is, provided that the pension fund was selling because it needed cash rather than because it was reacting to bad news—Steinhardt could profit by buying and holding the stock until it rose back to its previous level.

Medallion therefore closed to new outside investors in 1993, and by the 2000s the $6 billion or so in the fund consisted almost entirely of employees’ money.34 But the very existence of Medallion had a halo effect on the rest of the industry, offsetting the blow to the reputation of black-box trading administered by the collapse of Long-Term Capital. Each time Simons’s picture appeared on the cover of a financial magazine, more eager institutional money flooded into quantitative trading systems. Simons himself capitalized on this phenomenon. In 2005 he launched a new venture, the Renaissance Institutional Equities Fund, which was designed to absorb an eye-popping $100 billion in institutional savings. The only way this huge amount could be manageable was to branch out from short-term trading into more liquid longer-term strategies—and since pure pattern recognition works best for short-term trades, it followed that Simons was offering a fund that would rely on different sorts of signal—ones that might already have been mined by D.


pages: 741 words: 179,454

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

affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, 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, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, 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 independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, 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, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, 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, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, 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 Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game


pages: 733 words: 179,391

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

"Robert Solow", 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, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, 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 Shiller, Sam Peltzman, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, survivorship 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, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

The market is never completely efficient, but it certainly has a tendency to become more efficient over time.” In evolutionary terms, the markets were adapting. In fact, the markets may have been adapting specifically to the presence of D. E. Shaw & Co., although Shaw modestly downplays that possibility: “Over time, things evolved. I don’t know how much of 240 • Chapter 7 that was due to our influence. The general comment I can make is that quantitative trading became more challenging with every passing year.” In fact, Shaw inspired legions of talented computer scientists, mathematicians, and other quants to pursue careers in finance, raising the level of play in this highly competitive field. After transforming the hedge fund industry into a quantitative discipline that now employs thousands of engineers, Shaw decided to apply his intellectual gifts to another field.


pages: 442 words: 39,064

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pages: 515 words: 132,295

Makers and Takers: The Rise of Finance and the Fall of American Business by Rana Foroohar

accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, additive manufacturing, Airbnb, algorithmic trading, Alvin Roth, Asian financial crisis, asset allocation, bank run, Basel III, bonus culture, Bretton Woods, British Empire, business cycle, buy and hold, call centre, Capital in the Twenty-First Century by Thomas Piketty, 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, David Graeber, deskilling, Detroit bankruptcy, diversification, Double Irish / Dutch Sandwich, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial deregulation, financial intermediation, Frederick Winslow Taylor, George Akerlof, gig economy, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, 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, John Markoff, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, Kickstarter, knowledge economy, labor-force participation, London Whale, Long Term Capital Management, 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, quantitative easing, quantitative trading / quantitative finance, race to the bottom, Ralph Nader, Rana Plaza, RAND corporation, random walk, rent control, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, 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, technology bubble, 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, trickle-down economics, Tyler Cowen: Great Stagnation, Vanguard fund, zero-sum game


pages: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

3Com Palm IPO, Albert Einstein, asset allocation, beat the dealer, Bernie Madoff, Black Swan, Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, 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 innovation, George Santayana, German hyperinflation, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Meriwether, John Nash: game theory, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, margin call, Mason jar, merger arbitrage, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, 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, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stocks for the long run, survivorship bias, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration


pages: 302 words: 86,614

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

Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, 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, 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, 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, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Richard Florida, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, survivorship bias, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game


pages: 719 words: 104,316

pages: 389 words: 109,207

pages: 651 words: 180,162

Antifragile: Things That Gain From Disorder by Nassim Nicholas Taleb

Air France Flight 447, Andrei Shleifer, banking crisis, Benoit Mandelbrot, Berlin Wall, Black Swan, business cycle, Chuck Templeton: OpenTable:, commoditize, creative destruction, credit crunch, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, discrete time, double entry bookkeeping, Emanuel Derman, epigenetics, financial independence, Flash crash, Gary Taubes, George Santayana, Gini coefficient, Henri Poincaré, high net worth, hygiene hypothesis, Ignaz Semmelweis: hand washing, informal economy, invention of the wheel, invisible hand, Isaac Newton, James Hargreaves, Jane Jacobs, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, Marc Andreessen, meta analysis, meta-analysis, microbiome, money market fund, moral hazard, mouse model, Myron Scholes, Norbert Wiener, pattern recognition, Paul Samuelson, placebo effect, Ponzi scheme, 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, selection bias, Silicon Valley, six sigma, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, stochastic process, stochastic volatility, 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



pages: 431 words: 132,416


pages: 545 words: 137,789

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

"Robert Solow", Albert Einstein, Andrei Shleifer, anti-communist, asset allocation, asset-backed security, availability heuristic, bank run, banking crisis, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black-Scholes formula, Blythe Masters, Bretton Woods, British Empire, business cycle, capital asset pricing model, 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 innovation, Financial Instability Hypothesis, financial intermediation, full employment, George Akerlof, 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, Kenneth Arrow, Kickstarter, laissez-faire capitalism, Landlord’s Game, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, market bubble, market clearing, mental accounting, Mikhail Gorbachev, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, Myron Scholes, Naomi Klein, negative equity, Network effects, Nick Leeson, Northern Rock, paradox of thrift, Pareto efficiency, Paul Samuelson, Ponzi scheme, price discrimination, price stability, principal–agent problem, profit maximization, 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 Shiller, Ronald Coase, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical model, technology bubble, The Chicago School, The Great Moderation, The Market for Lemons, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, unorthodox policies, value at risk, Vanguard fund, Vilfredo Pareto, wealth creators, zero-sum game


pages: 162 words: 50,108

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The Scandal of Money by George Gilder

Affordable Care Act / Obamacare, bank run, Bernie Sanders, bitcoin, blockchain, borderless world, Bretton Woods, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, Claude Shannon: information theory, Clayton Christensen, cloud computing, corporate governance, cryptocurrency, currency manipulation / currency intervention, Daniel Kahneman / Amos Tversky, Deng Xiaoping, disintermediation, Donald Trump, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, glass ceiling, Home mortgage interest deduction, index fund, indoor plumbing, industrial robot, inflation targeting, informal economy, Innovator's Dilemma, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, John von Neumann, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, Law of Accelerating Returns, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, money: store of value / unit of account / medium of exchange, mortgage tax deduction, obamacare, Paul Samuelson, Peter Thiel, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, reserve currency, road to serfdom, Robert Gordon, Robert Metcalfe, Ronald Reagan, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, secular stagnation, seigniorage, Silicon Valley, smart grid, South China Sea, special drawing rights, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, time value of money, too big to fail, transaction costs, trickle-down economics, Turing machine, winner-take-all economy, yield curve, zero-sum game


pages: 554 words: 158,687

Profiting Without Producing: How Finance Exploits Us All by Costas Lapavitsas

"Robert Solow", Andrei Shleifer, asset-backed security, bank run, banking crisis, Basel III, borderless world, Branko Milanovic, Bretton Woods, business cycle, capital controls, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, computer age, conceptual framework, corporate governance, credit crunch, Credit Default Swap, David Graeber, David Ricardo: comparative advantage, disintermediation, diversified portfolio, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, financial deregulation, financial independence, financial innovation, financial intermediation, financial repression, Flash crash, full employment, global value chain, global village, High speed trading, Hyman Minsky, income inequality, inflation targeting, informal economy, information asymmetry, intangible asset, job satisfaction, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, liberal capitalism, London Interbank Offered Rate, low skilled workers, M-Pesa, market bubble, means of production, money market fund, moral hazard, mortgage debt, Network effects, new economy, oil shock, open economy, pensions crisis, price stability, Productivity paradox, profit maximization, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, race to the bottom, regulatory arbitrage, reserve currency, Robert Shiller, Robert Shiller, savings glut, Scramble for Africa, secular stagnation, shareholder value, Simon Kuznets, special drawing rights, Thales of Miletus, The Chicago School, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, Tobin tax, too big to fail, total factor productivity, trade liberalization, transaction costs, union organizing, value at risk, Washington Consensus, zero-sum game


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Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better by Andrew Palmer

Affordable Care Act / Obamacare, algorithmic trading, Andrei Shleifer, asset-backed security, availability heuristic, bank run, banking crisis, Black-Scholes formula, bonus culture, break the buck, Bretton Woods, call centre, Carmen Reinhart, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, David Graeber, diversification, diversified portfolio, Edmond Halley, Edward Glaeser, endogenous growth, Eugene Fama: efficient market hypothesis, eurozone crisis, family office, financial deregulation, financial innovation, fixed income, Flash crash, Google Glasses, Gordon Gekko, high net worth, housing crisis, Hyman Minsky, implied volatility, income inequality, index fund, information asymmetry, Innovator's Dilemma, interest rate swap, Kenneth Rogoff, Kickstarter, late fees, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, margin call, Mark Zuckerberg, McMansion, money market fund, mortgage debt, mortgage tax deduction, Myron Scholes, negative equity, Network effects, Northern Rock, obamacare, payday loans, peer-to-peer lending, Peter Thiel, principal–agent problem, profit maximization, quantitative trading / quantitative finance, railway mania, randomized controlled trial, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, short selling, Silicon Valley, Silicon Valley startup, Skype, South Sea Bubble, sovereign wealth fund, statistical model, Thales of Miletus, transaction costs, Tunguska event, unbanked and underbanked, underbanked, Vanguard fund, web application


pages: 360 words: 85,321

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Value of Everything: An Antidote to Chaos The by Mariana Mazzucato

"Robert Solow", activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, bank run, banks create money, Basel III, Berlin Wall, Big bang: deregulation of the City of London, bonus culture, Bretton Woods, business cycle, butterfly effect, buy and hold, Buy land – they’re not making it any more, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cleantech, Corn Laws, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, David Ricardo: comparative advantage, debt deflation, European colonialism, fear of failure, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, full employment, G4S, George Akerlof, Google Hangouts, Growth in a Time of Debt, high net worth, Hyman Minsky, income inequality, index fund, informal economy, interest rate derivative, Internet of things, invisible hand, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour market flexibility, laissez-faire capitalism, light touch regulation, liquidity trap, London Interbank Offered Rate, margin call, Mark Zuckerberg, market bubble, means of production, money market fund, negative equity, Network effects, new economy, Northern Rock, obamacare, offshore financial centre, Pareto efficiency, patent troll, Paul Samuelson, peer-to-peer lending, Peter Thiel, profit maximization, quantitative easing, quantitative trading / quantitative finance, QWERTY keyboard, rent control, rent-seeking, Sand Hill Road, shareholder value, sharing economy, short selling, Silicon Valley, Simon Kuznets, smart meter, Social Responsibility of Business Is to Increase Its Profits, software patent, stem cell, Steve Jobs, The Great Moderation, The Spirit Level, The Wealth of Nations by Adam Smith, Thomas Malthus, Tobin tax, too big to fail, trade route, transaction costs, two-sided market, very high income, Vilfredo Pareto, wealth creators, Works Progress Administration, zero-sum game


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, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, buy and hold, call centre, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, corporate governance, Credit Default Swap, cross-subsidies, dematerialisation, disruptive innovation, diversification, diversified portfolio, Edward Lloyd's coffeehouse, Elon Musk, Eugene Fama: efficient market hypothesis, eurozone crisis, financial innovation, financial intermediation, financial thriller, fixed income, Flash crash, forward guidance, Fractional reserve banking, full employment, George Akerlof, German hyperinflation, Goldman Sachs: Vampire Squid, Growth in a Time of Debt, 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, John Meriwether, light touch regulation, London Whale, Long Term Capital Management, loose coupling, low cost airline, low cost carrier, M-Pesa, market design, millennium bug, mittelstand, 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, peer-to-peer lending, performance metric, Peter Thiel, Piper Alpha, Ponzi scheme, price mechanism, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, random walk, regulatory arbitrage, Renaissance Technologies, rent control, risk tolerance, road to serfdom, Robert Shiller, Robert Shiller, Ronald Reagan, Schrödinger's Cat, 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, Washington Consensus, We are the 99%, Yom Kippur War



pages: 1,829 words: 135,521

pages: 475 words: 155,554

The Default Line: The Inside Story of People, Banks and Entire Nations on the Edge by Faisal Islam

Asian financial crisis, asset-backed security, balance sheet recession, bank run, banking crisis, Basel III, Ben Bernanke: helicopter money, Berlin Wall, Big bang: deregulation of the City of London, Boris Johnson, British Empire, capital controls, carbon footprint, Celtic Tiger, central bank independence, centre right, collapse of Lehman Brothers, credit crunch, Credit Default Swap, crony capitalism, dark matter, deindustrialization, Deng Xiaoping, disintermediation, energy security, Eugene Fama: efficient market hypothesis, eurozone crisis, financial deregulation, financial innovation, financial repression, floating exchange rates, forensic accounting, forward guidance, full employment, G4S, ghettoisation, global rebalancing, global reserve currency, hiring and firing, inflation targeting, Irish property bubble, Just-in-time delivery, labour market flexibility, light touch regulation, London Whale, Long Term Capital Management, margin call, market clearing, megacity, Mikhail Gorbachev, mini-job, mittelstand, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, North Sea oil, Northern Rock, offshore financial centre, open economy, paradox of thrift, Pearl River Delta, pension reform, price mechanism, price stability, profit motive, quantitative easing, quantitative trading / quantitative finance, race to the bottom, regulatory arbitrage, reserve currency, reshoring, Right to Buy, rising living standards, Ronald Reagan, savings glut, shareholder value, sovereign wealth fund, The Chicago School, the payments system, too big to fail, trade route, transaction costs, two tier labour market, unorthodox policies, uranium enrichment, urban planning, value at risk, WikiLeaks, working-age population, zero-sum game


pages: 807 words: 154,435

Radical Uncertainty: Decision-Making for an Unknowable Future by Mervyn King, John Kay

"Robert Solow", Airbus A320, Albert Einstein, Albert Michelson, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Arthur Eddington, autonomous vehicles, availability heuristic, banking crisis, Barry Marshall: ulcers, battle of ideas, Benoit Mandelbrot, bitcoin, Black Swan, Bonfire of the Vanities, Brownian motion, business cycle, business process, capital asset pricing model, central bank independence, collapse of Lehman Brothers, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, Donald Trump, easy for humans, difficult for computers, Edmond Halley, Edward Lloyd's coffeehouse, Edward Thorp, Elon Musk, Ethereum, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, fear of failure, feminist movement, financial deregulation, George Akerlof, germ theory of disease, Hans Rosling, Ignaz Semmelweis: hand washing, income per capita, incomplete markets, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Jeff Bezos, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Snow's cholera map, John von Neumann, Kenneth Arrow, Long Term Capital Management, loss aversion, Louis Pasteur, mandelbrot fractal, market bubble, market fundamentalism, Moneyball by Michael Lewis explains big data, Nash equilibrium, Nate Silver, new economy, Nick Leeson, Northern Rock, oil shock, Paul Samuelson, peak oil, Peter Thiel, Philip Mirowski, Pierre-Simon Laplace, popular electronics, price mechanism, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, railway mania, RAND corporation, rent-seeking, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Coase, sealed-bid auction, shareholder value, Silicon Valley, Simon Kuznets, Socratic dialogue, South Sea Bubble, spectrum auction, Steve Ballmer, Steve Jobs, Steve Wozniak, Tacoma Narrows Bridge, Thales and the olive presses, Thales of Miletus, The Chicago School, the map is not the territory, The Market for Lemons, The Nature of the Firm, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Thomas Malthus, Toyota Production System, transaction costs, ultimatum game, urban planning, value at risk, World Values Survey, Yom Kippur War, zero-sum game


pages: 543 words: 157,991

pages: 654 words: 191,864

pages: 700 words: 201,953

The Social Life of Money by Nigel Dodd

accounting loophole / creative accounting, bank run, banking crisis, banks create money, Bernie Madoff, bitcoin, blockchain, borderless world, Bretton Woods, BRICs, business cycle, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computer age, conceptual framework, credit crunch, cross-subsidies, David Graeber, debt deflation, dematerialisation, disintermediation, eurozone crisis, fiat currency, financial exclusion, financial innovation, Financial Instability Hypothesis, financial repression, floating exchange rates, Fractional reserve banking, German hyperinflation, Goldman Sachs: Vampire Squid, Hyman Minsky, illegal immigration, informal economy, interest rate swap, Isaac Newton, John Maynard Keynes: Economic Possibilities for our Grandchildren, joint-stock company, Joseph Schumpeter, Kickstarter, Kula ring, laissez-faire capitalism, land reform, late capitalism, liberal capitalism, liquidity trap, litecoin, London Interbank Offered Rate, M-Pesa, Marshall McLuhan, means of production, mental accounting, microcredit, mobile money, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, negative equity, new economy, Nixon shock, Occupy movement, offshore financial centre, paradox of thrift, payday loans, Peace of Westphalia, peer-to-peer, peer-to-peer lending, Ponzi scheme, post scarcity, postnationalism / post nation state, predatory finance, price mechanism, price stability, quantitative easing, quantitative trading / quantitative finance, remote working, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Satoshi Nakamoto, Scientific racism, seigniorage, Skype, Slavoj Žižek, South Sea Bubble, sovereign wealth fund, special drawing rights, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, Veblen good, Wave and Pay, Westphalian system, WikiLeaks, Wolfgang Streeck, yield curve, zero-coupon bond


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, Bretton Woods, Brownian motion, business cycle, capital asset pricing model, Cass Sunstein, collective bargaining, colonial exploitation, compound rate of return, conceptual framework, 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, 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, Louis Bachelier, 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, quantitative trading / quantitative finance, random walk, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, sovereign wealth fund, spice trade, stochastic process, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, time value of money, too big to fail, trade liberalization, trade route, transatlantic slave trade, tulip mania, wage slave


pages: 1,088 words: 228,743

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

Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity 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 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, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, 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, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, 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


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

3Com Palm IPO, accounting loophole / creative accounting, Airbus A320, Asian financial crisis, asset allocation, asset-backed security, banking crisis, Bernie Madoff, big-box store, Black-Scholes formula, break the buck, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, 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-subsidies, discounted cash flows, disintermediation, diversified portfolio, equity premium, eurozone crisis, 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, Kenneth Rogoff, law of one price, linear programming, Livingstone, I presume, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, market friction, money market fund, moral hazard, Myron Scholes, new economy, Nick Leeson, Northern Rock, offshore financial centre, 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 tolerance, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, Silicon Valley, Skype, Steve Jobs, 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, yield curve, zero-coupon bond, zero-sum game, Zipcar


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, 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, p-value, paper trading, performance metric, prediction markets, 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

At www.packt.com, you can also read a collection of free technical articles, sign up for a range of free newsletters, and receive exclusive discounts and offers on Packt books and eBooks. Contributors About the authors Sebastien Donadio is the Chief Technology Officer at Tradair, responsible for leading the technology. He has a wide variety of professional experience, including being head of software engineering at HC Technologies, partner and technical director of a high-frequency FX firm, a quantitative trading strategy software developer at Sun Trading, working as project lead for the Department of Defense. He also has research experience with Bull SAS, and an IT Credit Risk Manager with Société Générale while in France. He has taught various computer science courses for the past ten years in the University of Chicago, NYU and Columbia University. His main passion is technology but he is also a scuba diving instructor and an experienced rock-climber.

He has authored Practical Big Data Analytics and co-authored Hands-on Data Science with R. Apart from his role at RxDataScience, and is also currently affiliated with Imperial College, London. Ratanlal Mahanta is currently working as a quantitative analyst at bittQsrv, a global quantitative research company offering quant models for its investors. He has several years of experience in the modeling and simulation of quantitative trading. Ratanlal holds a master's degree in science in computational finance, and his research areas include quant trading, optimal execution, and high-frequency trading. He has over 9 years' work experience in the finance industry, and is gifted at solving difficult problems that lie at the intersection of the market, technology, research, and design. Jiri Pik is an artificial intelligence architect & strategist who works with major investment banks, hedge funds, and other players.

In our book Learn Algorithmic Trading, we provide a broad audience with the knowledge and hands-on practical experience required to build a good understanding of how modern electronic trading markets and market participants operate, as well as how to go about designing, building, and operating all the components required to build a practical and profitable algorithmic trading business using Python. You will be introduced to algorithmic trading and setting up the environment required to perform tasks throughout the book. You will learn the key components of an algorithmic trading business and the questions you need to ask before embarking on an automated trading project. Later, you will learn how quantitative trading signals and trading strategies are developed. You will get to grips with the workings and implementation of some well-known trading strategies. You will also understand, implement, and analyze more sophisticated trading strategies, including volatility strategies, economic release strategies, and statistical arbitrage. You will learn how to build a trading bot from scratch using the algorithms built in the previous sections.


pages: 224 words: 13,238

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

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

A new wave of applications that provide full trading suites, such as portfolio modeling, trade blotter, and pre- and post-trade compliance, are being offered by firms such as Tradeware, Portware, Bloomberg, Reuters, and Europeanbased vendors such as Trading Screen. These products, which were once expensive to implement and maintain, are now becoming accessible to new entrants due to price pressure, for example, hedge funds and smaller investment management firms. Portware and FlexTrade are focusing on hedge funds with solutions that allow users to customize quantitative trading strategies alongside traditional risk arbitrage and long/short strategies. As the market for high-priced custom implementation becomes saturated, vendors will shift their focus to midtier asset managers where once only the largest financial firms could justify the expense. More players will implement electronic access to markets integrating trading and portfolio management suites. Total market spending for trading systems was $445 million in 2004, and potentially can reach $701 million in 2007 according to Celent.2 Analysis Identification Placement Routing Bulge Bracket Large Agency Brokers Algo Agency Brokers Data Mgmt OMS Enabler DMA Networks Exhibit 15.2 2 Competitive landscape.

FlexSIMULATOR Enables clients to build and test trading strategies using real-time and historical tick data. . eFlexTRADER Hosted version of FlexTRADER accessible via the Internet. Sell-side firms can market this product to their own clients to attract additional order flow. Portware Portware is a leading provider of buy-side and sell-side trade and execution management software for basket, single-asset and automated quantitative trading. Portware Professional, its core product, is a centralized platform for trade and execution management. Portware was founded in 2000 and is headquartered in New York, with an office in London. Portware Professional is an order management system, capable of handling both single-asset and portfolio/basket trading with multiuser support. Some of the key features and functionality of Portware Professional include the following: 1.


The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns, Aaron Roth

23andMe, affirmative action, algorithmic trading, Alvin Roth, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, Edward Snowden, Elon Musk, Filter Bubble, general-purpose programming language, Google Chrome, ImageNet competition, Lyft, medical residency, Nash equilibrium, Netflix Prize, p-value, Pareto efficiency, performance metric, personalized medicine, pre–internet, profit motive, quantitative trading / quantitative finance, RAND corporation, recommendation engine, replication crisis, ride hailing / ride sharing, Robert Bork, Ronald Coase, self-driving car, short selling, sorting algorithm, speech recognition, statistical model, Stephen Hawking, superintelligent machines, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

We’ve spent many hours talking to lawyers, regulators, economists, criminologists, social scientists, technology industry professionals, and many others about the issues raised in these pages. We’ve provided testimony and input to congressional committees, corporations, and government agencies on algorithmic privacy and fairness. And between us we have extensive, hands-on professional experience in areas including quantitative trading and finance; legal, regulatory, and algorithmic consulting; and technology investing and start-ups—all of which are beginning to confront the social issues that are our themes here. We are, in short, modern computer scientists. We also know what we are not, and should not pretend to be. We are not lawyers or regulators. We are not judges, police officers, or social workers. We are not on the front lines, directly seeing and helping the people who suffer harms from privacy or fairness violations by algorithms.

It turns out that the model minimizing this weighted penalty must be one of the points on the Pareto frontier. If we then change the weightings—say, to 1/4 times error plus 3/4 times the unfairness score—we will find another point on the Pareto frontier. So by exploring different combinations of our two objectives, we “reduce” our problem to the single-objective case and can trace out the entire frontier. While the idea of considering cold, quantitative trade-offs between accuracy and fairness might make you uncomfortable, the point is that there is simply no escaping the Pareto frontier. Machine learning engineers and policymakers alike can be ignorant of it or refuse to look at it. But once we pick a decision-making model (which might in fact be a human decision-maker), there are only two possibilities. Either that model is not on the Pareto frontier, in which case it’s a “bad” model (since it could be improved in at least one measure without harm in the other), or it is on the frontier, in which case it implicitly commits to a numerical weighting of the relative importance of error and unfairness.


pages: 280 words: 73,420

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague

algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, buy and hold, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

Credit Suisse, a large provider of HFT solutions, hawked algorithms to traders with names like “Guerilla” and “Sniper” to detect big orders in both the public markets and in dark pools, where mutual funds, pensions, and other big buyers and sellers attempt to trade without rippling the markets. A third strategy, called event trading, tried to capitalize on the news of the day and predict which direction the markets would take in reaction to the latest development. Harvey Houtkin used to instruct his trading students, “The trend is your friend,” and this was a variation of that theme. Large quantitative-trading firms such as Medallion engaged heavily in this type of momentum trading. The fourth strategy was old-fashioned arbitrage, in which the traders attempted to find price discrepancies between seemingly unrelated instruments, like stocks and sugar futures, for instance. Generally speaking, the algorithms compared data of past stock and commodities movements to build an understanding of how they might behave in the present.

These strategies exacerbated market volatility by driving stocks much higher or lower than they would have moved if investors merely were weighing the underlying fundamentals of the securities. Yaroshevsky saw things occurring in the equities market that had never occurred in the past, like the market beginning to rise robustly during a recession, as it did early in 2009. The rise was driven purely by quantitative trading, he argued. Credit was first extended by the regulators at the Federal Reserve to the bankers and “into the more capable hands of the quantitative geniuses,” he chuckled. Their trading drove the market higher and culminated in the Flash Crash. It wasn’t deliberate market manipulation, in his view. So many Quants employed the same momentum strategies that the market simply became divorced from its fundamentals.


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

Fundamental quant investing considers many of the same factors as discretionary traders, seeking to buy cheap stocks and short sell expensive ones, but the difference is that fundamental quants do so systematically using computer systems. While discretionary trading has the advantages of a tailored analysis of each trade and the use of soft information such as private conversations, its labor-intensive method implies that only a limited number of securities can be analyzed in depth, and the discretion exposes the trader to psychological biases. Quantitative trading has the advantage of being able to apply a trading idea to thousands of securities around the globe, benefiting from significant diversification. Furthermore, quants can apply their trading ideas with the discipline of a robot. Discipline is important for all traders, but as the saying goes, Have a rule. Always follow the rule, but know when to break it. Even quants sometimes need to “break the rule,” for example, if they realize that there are problems in the data feed or if sudden important events happen that are outside the realm of the models, such as the failure of the investment bank Lehman Brothers in 2008.

Typically discretionary equity investors buy more stocks than they sell short, but the reverse is true for dedicated short bias hedge funds. Dedicated short bias hedge funds focus on findings stocks that are about to go down, looking for frauds, overstated earnings, or poor business plans. Dedicated short bias hedge funds rely on a fundamental analysis of companies in a similar way to other discretionary equity investors. Discretionary trading can be seen in contrast to quantitative trading, which invests systematically based on a model. Both types of traders may seek lots of data and use valuation models, but whereas discretionary traders make their final trading decisions based on human judgment, quantitative investors trade systematically with minimal human interference. Quantitative investors gather data, check the data, feed it into a model, and let the model send trades to the exchanges.1 Quants try to develop a small edge on each of many small diversified trades using sophisticated processing of ideas that cannot be easily processed using non-quantitative methods.

In other words, trading is done by feeding data into computers that run various programs with human oversight. Discretionary trading has the advantages of a tailored analysis of each trade and the use of a lot of soft information such as private conversations, but its labor-intensive method implies that only a limited number of securities can be analyzed in depth, and the discretion exposes the trader to psychological biases. Quantitative trading has the advantage of discipline, an ability to apply a trading idea to a wide universe of securities with the benefits of diversification, and efficient portfolio construction, but it must rely only on hard data and the computer program’s limited ability to incorporate real-time judgment. While the three forms of equity investment have several differences, each relies on an understanding of equity valuation.


pages: 293 words: 81,183

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

barriers to entry, basic income, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, Lean Startup, M-Pesa, mass immigration, meta analysis, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, universal basic income, women in the workforce

Both of these careers also come with a high chance of dropping out, since at each stage, if you fail to be promoted, you’ll probably have to switch into a different job with lower pay. Even taking this into account, however, they’re still among the career paths with the highest expected earnings. Tech entrepreneurship and quantitative trading in hedge funds offer even higher expected earnings, though tech entrepreneurship comes with even higher risks (entrepreneurs have less than a 10 percent chance of ever selling their shares in the company at profit) and quantitative trading requires exceptionally strong mathematical skills. Among less risky careers, medicine is probably the highest-earning option, especially in the United States, though earnings are probably less than in finance. Law is less appealing than one might think, because unless you can get into one of the very top law schools such as Harvard, you won’t likely earn as much as you would in consulting or finance.


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

Albert Einstein, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative finance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game

Longstreet, Roy. Viewpoints of a Commodity Trader. Greenville: Traders Press, 1967. Mallaby, Sebastian. More Money than God: Hedge Funds and the Making of a New Elite. New York: The Penguin Press, 2010. Mauboussin, Michael. More Than You Know: Finding Financial Wisdom in Unconventional Places. New York: Columbia Business School, 2006. Narang, Rishi. Inside the Black Box: The Simple Truth About Quantitative Trading. Hoboken: John Wiley and Sons, Inc., 2009. Neill, Humphrey. Tape Reading: Market and Tactics. LaVergne: BN Publishing, 2008. O’Shaughnessy, James. What Works on Wall Street: A Guide to the BestPerforming Investment Strategies of all Time. New York: McGraw Hill, 1997. Patel, Charles. Technical Trading Systems for Commodities and Stocks. Greenville: Traders Press, Inc., 1998. Paulos, John Allen.


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, 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, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, 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 tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, source of truth, statistical model, stocks for the long run, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

An asset-class benchmark measures the returns earned and risks incurred by investing in a specific asset class, with no management skill. Lars Kestner, Quantitative Trading Strategies: Harnessing the Power of Quantitative Techniques to Create a Winning Trading Program (New York: McGraw-Hill, 2003), 129–180. The eight market sectors tested were foreign exchange, interest rates, stock index, metals, energy, grains, meats, and softs. The nine industry sectors were energy, basic materials, consumer discretionary, consumer staples, health care, financials and information technology, telecom. The three stock indexes were S&P 500, NASDAQ 100, and Russell 2000. The five trend-following systems were channel breakout, dual moving-average crossover, two version of momentum, and MACD versus its signal line. For more description see Kestner’s Quantitative Trading Strategies. M. Cooper, “Filter Rules Based on Price and Volume in Individual Security Overreaction,” Review of Financial Studies 12, no. 4 (Special 1999), 901–935.


pages: 265 words: 93,231

The Big Short: Inside the Doomsday Machine by Michael Lewis

Asperger Syndrome, asset-backed security, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversified portfolio, facts on the ground, financial innovation, fixed income, forensic accounting, Gordon Gekko, high net worth, housing crisis, illegal immigration, income inequality, index fund, interest rate swap, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, medical residency, money market fund, moral hazard, mortgage debt, pets.com, Ponzi scheme, Potemkin village, quantitative trading / quantitative finance, Robert Bork, short selling, Silicon Valley, the new new thing, too big to fail, value at risk, Vanguard fund, zero-sum game

The longest options available to individual investors on public exchanges were LEAPs, which were two-and-a-half-year options on common stocks. You know, Ben said to Charlie and Jamie, if you established yourself as a serious institutional investor, you could phone up Lehman Brothers or Morgan Stanley and buy eight-year options on whatever you wanted. Would you like that? They would! They wanted badly to be able to deal directly with the source of what they viewed as the most underpriced options: the most sophisticated, quantitative trading desks at Goldman Sachs, Deutsche Bank, Bear Stearns, and the rest. The hunting license, they called it. The hunting license had a name: an ISDA. They were the same agreements, dreamed up by the International Swaps and Derivatives Association, that Mike Burry secured before he bought his first credit default swaps. If you got your ISDA, you could in theory trade with the big Wall Street firms, if not as an equal then at least as a grown-up.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bernie Madoff, 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, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

I would like to thank Daniel Stark for providing me access to Stark & Company’s very comprehensive commodity trading advisor (CTA) database that, critically, also contains returns for defunct funds—data that is essential in conducting statistically unbiased analysis of the relationship between past and future returns. About the Author Jack D. Schwager is a recognized industry expert in futures and hedge funds and the author of a number of widely acclaimed financial books. He is currently the co–portfolio manager for the ADM Investor Services Diversified Strategies Fund, a portfolio of futures and foreign exchange (FX) managed accounts. He is also an adviser to Marketopper, an India-based quantitative trading firm, supervising a major project that will adapt that firm’s trading technology to trade a global futures portfolio. Previously, Mr. Schwager was a partner in the Fortune Group, a London-based hedge fund advisory firm acquired by the Close Brothers Group. His previous experience also includes 22 years as director of futures research for some of Wall Street’s leading firms and 10 years as the co-principal of a CTA.


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

The process we’re describing, of machines taking the high-end cognitive parts of work and turning people into a sort of human user interface, is occurring across many professional realms. Actual decision-making roles have been ceded to computers—and they are doing pretty well in those roles, despite some occasional hiccups. “Program trading” (also known as high-frequency, algorithmic, or quantitative trading) of equities and fixed-income investments, for example, is widespread on Wall Street and around the financial world. It’s one of the reasons why the New York Stock Exchange is so quiet today. Decisions about which stocks and bonds to buy for what price used to be made by human traders but are now largely made by computer. Likewise, decisions that used to be made by human pricing analysts are now arrived at automatically.


pages: 306 words: 97,211

Value Investing: From Graham to Buffett and Beyond by Bruce C. N. Greenwald, Judd Kahn, Paul D. Sonkin, Michael van Biema

Andrei Shleifer, barriers to entry, Berlin Wall, business cycle, capital asset pricing model, corporate raider, creative destruction, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, Eugene Fama: efficient market hypothesis, fixed income, index fund, intangible asset, Long Term Capital Management, naked short selling, new economy, place-making, price mechanism, quantitative trading / quantitative finance, Richard Thaler, shareholder value, short selling, Silicon Valley, stocks for the long run, Telecommunications Act of 1996, time value of money, tulip mania, Y2K, zero-sum game

And he followed Graham's practice of comparing two companies in the same industry, like Bethlehem and Crucible Steel, to see which was cheaper on an intrinsic value basis. Their focus was the balance sheet, not the income statement. They were able to discuss these ideas and pick up other suggestions from a community of value investors that had formed around Graham. One lasting interest of Graham and this circle was the search for quantitative trading formulas that could be used to direct market investment strategies in a disciplined way. Heilbrunn contributed to the development of these kinds of rules in an article published in 1958. The method prefigured many of the formulas used by quantitatively oriented value investors today. Heilbrunn examined the price, earnings, and dividend histories of specific companies to establish the ranges of the price to earnings (P/E) multiple and the dividend yield within which the securities had traded.


Fortunes of Change: The Rise of the Liberal Rich and the Remaking of America by David Callahan

affirmative action, Albert Einstein, American Legislative Exchange Council, automated trading system, Bernie Sanders, Bonfire of the Vanities, carbon footprint, carried interest, clean water, corporate social responsibility, David Brooks, demographic transition, desegregation, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Thorp, financial deregulation, financial independence, global village, Gordon Gekko, greed is good, high net worth, income inequality, Irwin Jacobs: Qualcomm, Jeff Bezos, John Markoff, Kickstarter, knowledge economy, knowledge worker, Marc Andreessen, Mark Zuckerberg, market fundamentalism, medical malpractice, mega-rich, Mitch Kapor, Naomi Klein, NetJets, new economy, offshore financial centre, Peter Thiel, plutocrats, Plutocrats, profit maximization, quantitative trading / quantitative finance, Ralph Nader, Renaissance Technologies, Richard Florida, Robert Bork, rolodex, Ronald Reagan, school vouchers, short selling, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, stem cell, Steve Ballmer, Steve Jobs, unpaid internship, Upton Sinclair, Vanguard fund, War on Poverty, working poor, World Values Survey

Shaw Group, a large hedge fund that had $39 billion in assets before the crash and offices on three continents. Shaw started out as an academic, getting his PhD from Stanford University in 1980 and then joining the faculty of Columbia University’s computer science department, where he led work on supercomputers. Shaw later moved to the investment world, where he made a killing using advanced quantitative trading methodologies—an approach that led Fortune to call him “King Quant.” Eventually, Shaw drifted back to computer science, founded a research firm, and affiliated again with Columbia. But he remains involved with his hedge fund—involved enough to make $275 million for himself in 2008—and avidly pursues his passion of liberal politics. Shaw has been one of the largest hedge fund contributors to the Democratic Party in recent years, giving the party hundreds of thousands of dollars.


pages: 289 words: 113,211

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

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

ZBI traded only with internal funds—those of the Ziff family, of publishing fame—in what is termed in the investment world a family office; it was never chasing after other hedge funds’ investor dollars. Not long after I joined ZBI I moved from risk management to portfolio management. I redirected the efforts of a small group of PhDs who had been providing quantitative analysis for the traditional portfolio managers toward running an internal portfolio based on quantitative trading models. While the trading center for Moore was macro strategies, at ZBI the center was equities, and the portfolio I managed was an equity portfolio. So between this and Scribe Reports, I had moved solidly into the world of equity hedge funds, and I found equities to be a very attractive market. There are many state variables that underlie the price of a stock, and with years of data on thousands of stocks there is a statistical soup of observations where relationships can be coaxed out in many dimensions (although this bounty is not necessarily an advantage—for those without sufficient discipline or statistical knowledge, there is enough data to find just about any relationship you want).


pages: 354 words: 118,970

Transaction Man: The Rise of the Deal and the Decline of the American Dream by Nicholas Lemann

Affordable Care Act / Obamacare, Airbnb, airline deregulation, Albert Einstein, augmented reality, basic income, Bernie Sanders, Black-Scholes formula, buy and hold, capital controls, computerized trading, corporate governance, cryptocurrency, Daniel Kahneman / Amos Tversky, dematerialisation, diversified portfolio, Donald Trump, Elon Musk, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, fixed income, future of work, George Akerlof, gig economy, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, index fund, information asymmetry, invisible hand, Irwin Jacobs, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kickstarter, life extension, Long Term Capital Management, Mark Zuckerberg, mass immigration, means of production, Metcalfe’s law, money market fund, Mont Pelerin Society, moral hazard, Myron Scholes, new economy, Norman Mailer, obamacare, Paul Samuelson, Peter Thiel, price mechanism, principal–agent problem, profit maximization, quantitative trading / quantitative finance, Ralph Nader, Richard Thaler, road to serfdom, Robert Bork, Robert Metcalfe, rolodex, Ronald Coase, Ronald Reagan, Sand Hill Road, shareholder value, short selling, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, TaskRabbit, The Nature of the Firm, the payments system, Thomas Kuhn: the structure of scientific revolutions, Thorstein Veblen, too big to fail, transaction costs, universal basic income, War on Poverty, white flight, working poor

More and more lines of business involved technical, quantitative computer-driven financial transactions, running twenty-four hours a day, designed by people with advanced academic training whom the firm had hired. Dick Fisher, and John Mack after him, liked to say that three forces were driving the new financial world: deregulation, globalization, and technology. Morgan Stanley pursued enthusiastically anything that seemed attuned to those forces. The firm started a quantitative trading division run by a team of mathematicians. It invented new kinds of derivatives. It became a force in Silicon Valley, where its star analyst, Mary Meeker, wrote optimistic reports about the future of technology and also made Morgan Stanley the leading manager of the initial public offerings of companies like Netscape and Google. In 1993 Morgan Stanley’s investment bankers advised Dean Witter, a big retail stockbroker that was especially strong in the Midwest, when it spun itself off from Sears, the venerable chain of stores, and became a separate company.


pages: 492 words: 118,882

The Blockchain Alternative: Rethinking Macroeconomic Policy and Economic Theory by Kariappa Bheemaiah

accounting loophole / creative accounting, Ada Lovelace, Airbnb, algorithmic trading, asset allocation, autonomous vehicles, balance sheet recession, bank run, banks create money, Basel III, basic income, Ben Bernanke: helicopter money, bitcoin, blockchain, Bretton Woods, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, cashless society, cellular automata, central bank independence, Claude Shannon: information theory, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, complexity theory, constrained optimization, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, David Graeber, deskilling, Diane Coyle, discrete time, disruptive innovation, distributed ledger, diversification, double entry bookkeeping, Ethereum, ethereum blockchain, fiat currency, financial innovation, financial intermediation, Flash crash, floating exchange rates, Fractional reserve banking, full employment, George Akerlof, illegal immigration, income inequality, income per capita, inflation targeting, information asymmetry, interest rate derivative, inventory management, invisible hand, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, knowledge economy, large denomination, liquidity trap, London Whale, low skilled workers, M-Pesa, Marc Andreessen, market bubble, market fundamentalism, Mexican peso crisis / tequila crisis, MITM: man-in-the-middle, money market fund, money: store of value / unit of account / medium of exchange, mortgage debt, natural language processing, Network effects, new economy, Nikolai Kondratiev, offshore financial centre, packet switching, Pareto efficiency, pattern recognition, peer-to-peer lending, Ponzi scheme, precariat, pre–internet, price mechanism, price stability, private sector deleveraging, profit maximization, QR code, quantitative easing, quantitative trading / quantitative finance, Ray Kurzweil, Real Time Gross Settlement, rent control, rent-seeking, Satoshi Nakamoto, Satyajit Das, savings glut, seigniorage, Silicon Valley, Skype, smart contracts, software as a service, software is eating the world, speech recognition, statistical model, Stephen Hawking, supply-chain management, technology bubble, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Nature of the Firm, the payments system, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, too big to fail, trade liberalization, transaction costs, Turing machine, Turing test, universal basic income, Von Neumann architecture, Washington Consensus

His current research is on complexity economics, focusing on systemic risk in financial markets and technological progress. During his career, he has made important contributions to complex systems (See Appendix 1), chaos theory, artificial life, theoretical biology, time series forecasting and Econophysics. He is also an entrepreneur and co-founded the Prediction Company, one of the first companies to do fully automated quantitative trading. 30 Jacky Mallett has a PhD in computer science from MIT. She is a research scientist at Reykjavik Universit, who works on the design and analysis of high performance, distributed computing systems and simulations of economic systems with a focus on Basel regulatory framework for banks, and its macro-economic implications. She is also the creator of ‘Threadneedle’, an experimental tool for simulating fractional reserve banking systems. 29 203 Chapter 4 ■ Complexity Economics: A New Way to Witness Capitalism The question about [modelling] individual households raises a very significant issue: are there distinctions at that level that could affect the macro-economy?


pages: 436 words: 141,321

Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness by Frederic Laloux, Ken Wilber

Albert Einstein, augmented reality, blue-collar work, Buckminster Fuller, call centre, carbon footprint, conceptual framework, corporate social responsibility, crowdsourcing, different worldview, failed state, future of work, hiring and firing, index card, interchangeable parts, invisible hand, job satisfaction, Johann Wolfgang von Goethe, Kenneth Rogoff, meta analysis, meta-analysis, pattern recognition, post-industrial society, quantitative trading / quantitative finance, randomized controlled trial, selection bias, shareholder value, Silicon Valley, the market place, the scientific method, Tony Hsieh, zero-sum game

There is a school of thought that suggests we need accounting systems that track not just profit but also a firm’s impact on people and the planet; how else could managers make trade-offs between these elements? The argument sounds reasonable, so how come none of the pioneer Teal Organizations use multiple-bottom-line accounting systems? I think the following is at play: multiple bottom lines may help to overcome the fixation on profits alone, but the concept is still rooted in Orange thinking, where decisions are informed only by quantitative trade-offs, by weighing costs and benefits. From an Evolutionary-Teal perspective, not everything needs to be quantified to discern a right course of action. Of course, there are valuable insights to be gained from measuring how a company’s actions impact the environment and society (and for that reason, multiple bottom lines may well become a standard way of reporting in the future). But these pioneers seem to believe that, more than advanced accounting systems, we need integrity and wholeness to transcend the primacy of profits and heal our relationship with the world.


Making Globalization Work by Joseph E. Stiglitz

affirmative action, Andrei Shleifer, Asian financial crisis, banking crisis, barriers to entry, Berlin Wall, business process, capital controls, central bank independence, corporate governance, corporate social responsibility, currency manipulation / currency intervention, Doha Development Round, Exxon Valdez, Fall of the Berlin Wall, Firefox, full employment, Gini coefficient, global reserve currency, Gunnar Myrdal, happiness index / gross national happiness, illegal immigration, income inequality, income per capita, incomplete markets, Indoor air pollution, informal economy, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), inventory management, invisible hand, John Markoff, Jones Act, Kenneth Arrow, Kenneth Rogoff, low skilled workers, manufacturing employment, market fundamentalism, Martin Wolf, microcredit, moral hazard, new economy, North Sea oil, offshore financial centre, oil rush, open borders, open economy, price stability, profit maximization, purchasing power parity, quantitative trading / quantitative finance, race to the bottom, reserve currency, rising living standards, risk tolerance, Silicon Valley, special drawing rights, statistical model, the market place, The Wealth of Nations by Adam Smith, Thomas L Friedman, trade liberalization, trickle-down economics, union organizing, Washington Consensus, zero-sum game

Even putting this statistical debate aside, it is striking that even NAFTA advocates suggest that it has had at most a small effect on growth, even in a period in which, because of the Mexican crisis, trade was vital.Mexico’s joining the WTO in January 1995 may have made more of a difference in some respects than NAFTA, because it limited what the government could do in the aftermath of the 1994–95 crisis. (In earlier crises, the government had imposed numerous quantitative trade restrictions, which critics say had long-lasting adverse effects.).NAFTA proponents sometimes argue that NAFTA’s real contribution was opening up investment, not trade. But, critics say, while the effect on overall foreign investment is uncertain, some aspects of foreign investment may have contributed to Mexico’s slow growth. As international banks took over all but one of Mexico’s banks—acquisitions that NAFTA effectively encouraged—the supply of credit to small-and medium-sized domestic enterprises became constrained, and growth (outside firms linked with international exports) diminished.


pages: 506 words: 152,049

The Extended Phenotype: The Long Reach of the Gene by Richard Dawkins

Alfred Russel Wallace, assortative mating, Douglas Hofstadter, Drosophila, epigenetics, Gödel, Escher, Bach, impulse control, Menlo Park, Necker cube, p-value, phenotype, quantitative trading / quantitative finance, selection bias, stem cell

If such a gene happened to cause, say, malfunction of the liver, that would be just too bad; the gene would increase anyway, since selection for good health is much less effective than selection by competition among sperm cells’ (Crow 1979). There is, of course, no particular reason why a sperm competition gene should happen to cause malfunction of the liver but, as already pointed out, most mutations are deleterious, so some undesirable side effect is pretty likely. Why does Crow assert that selection for good health is much less effective than selection by competition among sperm cells? There must inevitably be a quantitative trade-off involving the magnitude of the effect on health. But, that aside, and even allowing for the controversial possibility that only a minority of sperms are viable (Cohen 1977), the argument appears to have force because the competition between sperm cells in an ejaculate would seem to be so fierce. A million million spermatozoa, All of them alive: Out of their cataclysm but one poor Noah Dare hope to survive.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

asset-backed security, backtesting, banking crisis, barriers to entry, beat the dealer, Bernie Madoff, Black-Scholes formula, British Empire, business cycle, buy and hold, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, Edward Thorp, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative finance, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

*This appendix was originally published in Market Wizards (1989). About the Author Mr. Schwager is a recognized industry expert in futures and hedge funds and the author of a number of widely acclaimed financial books. He is currently the co-portfolio manager for the ADM Investor Services Diversified Strategies Fund, a portfolio of futures and FX managed accounts. He is also an advisor to Marketopper, an India-based quantitative trading firm, supervising a major project that will adapt their trading technology to trade a global futures portfolio. Previously, Mr. Schwager was a partner in the Fortune Group, a London-based hedge fund advisory firm, acquired by the Close Brothers Group. His previous experience also includes 22 years as director of futures research for some of Wall Street’s leading firms and 10 years as the coprincipal of a CTA.


pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

These would no longer be allowed under the new rule. Goldman Sachs also had a large proprietary trading desk generating almost 50% of the firm’s profits. For example, in 2007, the trading and principal investments group made 64% of Goldman Sach’s revenues.8 This proprietary trading desk would have to be shut down. In fact, Morgan Stanley has already begun preparing for these new rules and the head of their quantitative trading desk, Peter Muller, and the rest of his team have left Morgan to start their own hedge fund. Some Thoughts The purpose of the Volker rule is to prevent banks that are protected by the public sector safety net from having risks due to investments in hedge funds and/or proprietary trading desks which can increase their risk substantially. For example, take MF Global, which was a successful broker-dealer.