21 results back to index
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
The profit on the put offsets the decline in the value of the stocks the insurer holds. If the stocks in the index rise, the insurer loses what he paid for the put. 3. Index arbitrage Involves the correlation between the stock market and the futures and options markets. Financial products sold in the futures and options markets are derived from an underlying cash product. For reasons that are inexplicable, sometimes when good news occurs, the futures and options markets for an index such as the S&P 500 are not at equilibrium with the underlying stock prices and trade above in relation to the actual market. An example of an index arbitrage opportunity would be selling expensive futures and options that are trading exuberantly but will soon return to fair valuations, and buying underlying stocks currently undervalued.
The book begins with Chapter 1: Overview of Electronic and Algorithmic Trading, which defines important ideas and gives a historical perspective on the emergence of program and algorithmic trading. We learn how decimalization, which changed the way the New York Stock Exchange quoted security prices, impacted the market, and how Electronic Communication Networks (ECNs) and multilateral trading facilities (MTFs) emerged to compete with monopolistic central exchanges. The chapter covers different aspects of electronic trading, such as duration averaging, dynamic hedging, and index arbitrage, and touches on the connectivity protocol known as FIX (Financial Information Exchange), which is the technological basis for increased connectivity. Chapter 2: Automating Trade and Order Flow covers the trade life cycle from beginning to end. It highlights the major steps in the trade life cycle, such as trade confirmation, settlement, and reconciliation. It argues that changing back-office processes are, in fact, key enablers of financial innovation.
The securities and futures markets have circuit breakers that provide for brief, coordinated cross-market trading halts during a severe market decline as measured by a single-day decrease in the Dow Jones Industrial Average (DJIA). There are three circuit breaker thresholds— 10%, 20%, and 30%—set by the markets at point levels that are calculated at the beginning of each quarter. Under NYSE Rule 80A, if the DJIA moves up or down 2% from the previous closing value, program trading orders to buy or sell the Standard & Poor’s 500 stocks as part of index arbitrage strategies must be entered with directions to have the order executions effected in a manner that stabilizes share prices. The collar restrictions are lifted if the DJIA returns to or within 1% of its previous closing value. The futures exchanges set the price limits that aim to lessen sharp price swings in contracts, such as stock index futures. A price limit does not stop trading in the futures, but prohibits trading at prices below the preset limit during a price decline.
asset allocation, backtesting, Black-Scholes formula, Bretton Woods, buy low sell high, California gold rush, capital asset pricing model, cognitive dissonance, compound rate of return, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fixed income, German hyperinflation, implied volatility, index arbitrage, index fund, Isaac Newton, joint-stock company, Long Term Capital Management, loss aversion, market bubble, mental accounting, Myron Scholes, new economy, oil shock, passive investing, Paul Samuelson, popular capitalism, prediction markets, price anchoring, price stability, purchasing power parity, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, survivorship bias, technology bubble, The Great Moderation, The Wisdom of Crowds, transaction costs, tulip mania, Vanguard fund
223 Uncertainty and the Market 226 Democrats and Republicans 227 Stocks and War 231 The World Wars 231 Post-1945 Conflicts 233 Conclusion 235 Chapter 14 Stocks, Bonds, and the Flow of Economic Data 237 Economic Data and the Market 238 Principles of Market Reaction 238 Information Content of Data Releases 239 Economic Growth and Stock Prices 240 The Employment Report 241 The Cycle of Announcements 243 Inflation Reports 244 Core Inflation 245 Employment Costs 246 Impact on Financial Markets 246 Central Bank Policy 247 Conclusion 247 PART 4 STOCK FLUCTUATIONS IN THE SHORT RUN Chapter 15 The Rise of Exchange-Traded Funds, Stock Index Futures, and Options 251 Exchange-Traded Funds 252 Stock Index Futures 253 Basics of the Futures Markets 255 xii Index Arbitrage 257 Predicting the New York Open with Globex Trading 258 Double and Triple Witching 260 Margin and Leverage 261 Using ETFs or Futures 261 Where to Put Your Indexed Investments: ETFs, Futures, or Index Mutual Funds? 262 Index Options 264 Buying Index Options 266 Selling Index Options 267 The Importance of Indexed Products 267 Chapter 16 Market Volatility 269 The Stock Market Crash of October 1987 271 The Causes of the October 1987 Crash 273 Exchange-Rate Policies 274 The Futures Market 275 Circuit Breakers 276 The Nature of Market Volatility 277 Historical Trends of Stock Volatility 278 The Volatility Index (VIX) 281 Recent Low Volatility 283 The Distribution of Large Daily Changes 283 The Economics of Market Volatility 285 The Significance of Market Volatility 286 Chapter 17 Technical Analysis and Investing with the Trend 289 The Nature of Technical Analysis 289 Charles Dow, Technical Analyst 290 The Randomness of Stock Prices 291 Simulations of Random Stock Prices 292 Trending Markets and Price Reversals 294 Moving Averages 295 Testing the Dow Jones Moving-Average Strategy 296 Back-Testing the 200-Day Moving Average 297 The Nasdaq Moving-Average Strategy 300 CONTENTS CONTENTS xiii Distribution of Gains and Losses 301 Momentum Investing 302 Conclusion 303 Chapter 18 Calendar Anomalies 305 Seasonal Anomalies 306 The January Effect 306 Causes of the January Effect 309 The January Effect Weakened in Recent Years 310 Large Monthly Returns 311 The September Effect 311 Other Seasonal Returns 315 Day-of-the-Week Effects 316 What’s an Investor to Do?
In most states, particularly Illinois where the large futures exchanges are located, settling a futures contract in cash was considered a wager—and wagering, except in some special circumstances, was illegal. In 1974, however, the Commodity Futures Trading Commission, a federal agency, was established by Congress to regulate all futures trading. Since futures trading was now governed by this new federal agency and since there was no federal prohibition against wagering, the prohibitory state laws were superseded. INDEX ARBITRAGE The prices of commodities (or financial assets) in the futures market do not stand apart from the prices of the underlying commodity. If the value of a futures contract rises sufficiently above the price of the commodity that can be purchased for immediate delivery in the open market, often called the cash or spot market, traders can buy the commodity, store it, and then deliver it at a profit against the higher-priced futures contract on the settlement date.
An arbitrageur in the ETF makes a profit when the prices of the stocks that she buys to create the ETF are less than the funds that she receives by selling, or creating, an ETF. Alternatively if the prices she receives from selling the stocks in the index exceed the cost of buying the ETF, the arbitrageur will buy the ETF, exchange it into its component stocks, and sell them in the open market. Index arbitrage has become a finely tuned art. The prices of stock index futures and ETFs usually stay within very narrow bands of the index value based on the price of the underlying shares. When the buying or selling of stock index futures or ETFs drives the price outside this band, arbitrageurs step in, and a flood of orders to buy or sell are immediately transmitted to the exchanges that trade the underlying stocks in the index.
Commodity Trading Advisors: Risk, Performance Analysis, and Selection by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah
Asian financial crisis, asset allocation, backtesting, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, fixed income, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Ponzi scheme, quantitative trading / quantitative ﬁnance, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, technology bubble, transaction costs, value at risk, zero-sum game
Diz (1996) and Fung and Hsieh (1997b) had 925 and 901 managed future programs from 1975 to 52 PERFORMANCE TABLE 4.1 Grouping of Barclay Trading Group Strategies Grouped CTA Strategies Technical Diversified Technical Financial/Metals Technical Currency Other Technical Fundamental Discretionary Systematic Stock Index Arbitrage Option Strategies No Category Barclay Trading Group Strategy Technical Diversified Technical Financial/Metals Technical Currency Technical Interest Rate Technical Energy Technical Agricultural Fundamental Diversified Fundamental Interest Rate Fundamental Financial/Metals Fundamental Energy Fundamental Currency Fundamental Agricultural Discretionary Systematic Stock Index Arbitrage Option Strategies No Category Note: The left-hand side of the table reports the strategy classification used throughout the study; the right-hand side contains the original classification of the Barclay Trading Group. 1995, and from 1986 to 1996 respectively.
. = standard deviation; Min = minimum; Max = maximum. The Sharpe ratio is calculated with a 5 percent risk-free rate. Note: The other technical strategy funds exist only for the August 1985–May 1995 period and for the October 1998–April 2001 period. Option strategy funds exist since September 1990. Technical Diversified Technical Financial/ Metals Technical Currency Other technical Total technical Fundamental Discretionary Systematic Stock Index Arbitrage Option strategy No Category Total No. of % of Living Funds the Total Funds CTA Strategies TABLE 4.2 Descriptive Statistics 54 PERFORMANCE Table 4.2 indicates that the systematic strategy is the most represented strategy (with 897 funds) followed by total technical funds (416 funds) and discretionary funds (299 funds). Other technical funds, option strategy funds, and fundamental funds count only 8, 9, and 19 funds respectively.
Second, the first column of the table reports the alpha of the different strategies once the performance of the CTA database considered as a whole is taken into account through the CTA Global Index. This is the performance not explained by the global CTA index. Seven out of the 11 strategies are significantly positive at the 5 or 1 percent significance level (technically financial/metals, technically currency, technically other, discretionary, stock index, arbitrage, and option strategies); two are not significantly different from zero (fundamental and no category); and two are significantly negative (technically diversified and systematic). These results indicate that all but two strategies produce returns significantly different from zero, which means that the individual strategies produce returns significantly different from their aggregation.6 6The CTA Global Index is composed of all the individual funds classified in the various strategies.
algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, 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
The bulls ignored subsequent interest rate increases in August and September as well.4 There had been at least one disturbing augury: In 1986, John Phelan, chairman of the New York Stock Exchange (NYSE), had warned that an explosion in “program trading” could cause a market “meltdown.”5 “What Phelan foresaw was that the combination of portfolio insurance and index arbitrage could create a chain reaction. By selling heavily in the futures pits, the computer guided, portfolio insurance firms would create a gap between the cache and futures markets that in turn would trigger index arbitrage in the form of purchases in the pits and sales on the floor. The arbitrage sales on the floor would drive down the underlying price to the point where the computers would call for the next round of portfolio insurance sales in the pits, and the process would repeat itself until neither the futures contracts nor the stocks had any market value at all,” wrote author Martin Mayer in December 1987.6 Phelan’s dire prediction had little effect on investors, who likely thought him a proper Luddite.
., the number of sells had mushroomed to $975 million. Exacerbating the selloff, was a relaxation of SEC Rule 10-A the previous December.15 The rule prohibited selling by a brokerage house except on an uptick. The SEC, in an effort to appease the brokerage community, had decided to allow short sales into a declining market as long as one of the firm’s proprietary accounts was long the stocks and the sale was pursuant to the unwinding of an index arbitrage. Buying and selling on the floor of the NYSE largely was manual in 1987—handled by specialists required to purchase shares in their own accounts when there were no other buyers. The specialists kept an order book listing buy orders on one side and sell orders on the other. They received a commission for matching buyer and seller, which was anywhere from an eighth to a sixteenth of a dollar, depending on the stock price.
3Com Palm IPO, Andrei Shleifer, asset allocation, capital asset pricing model, correlation coefficient, cross-subsidies, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, fixed income, index arbitrage, index fund, information asymmetry, liberal capitalism, locking in a profit, Long Term Capital Management, loss aversion, margin call, market friction, market microstructure, mental accounting, merger arbitrage, Myron Scholes, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, survivorship bias, transaction costs, Vanguard fund
For an average stock, however, options are either unavailable or too expensive to trade, causing the weekend effect for the equally weighted index to be relatively unchanged.) 43 44 Beyond the Random Walk Short Selling as the Primary Explanation Given evidence of the weekend effect, what is the cause? The primary explanation for the weekend effect relies on the behavior of short sellers with regard to unhedged short sales, as distinct from hedged short sales.3 Hedged short sales include merger arbitrage where an investor short-sells the bidder and buys the target (see Chapter 9), index arbitrage between futures and cash markets, short selling by put option writers to hedge their positions, shorting against the box (short-selling a stock that is held long in another account) to postpone realization of capital gains, and other similar activities where the short position is hedged by an offsetting similar position. On the other hand, unhedged or purely speculative short sales are naked positions based on the expectation (or hope) that the price of the shorted security will fall.
However, the test discussed in the previous paragraph does not distinguish between speculative short interest and hedged short interest. Initial public offerings (IPOs) are ideal for testing the effect of speculative short sales on the weekend returns, because they are likely to have only speculative short positions. IPOs are not good candidates for hedged short sale activity because (1) they are usually not part of an index (no index arbitrage), (2) they are not likely to be takeover candidates (no merger arbitrage); and (3) the high volatility of IPOs inhibits other types of nonspeculative short sellers from trading them. Results with IPOs show that the weekend effect increases from 0.12 percent for the low RSI quartile to 0.59 percent for the high RSI quartile. The weekend effect is nearly four times greater for IPOs in the top 25 percent by RSI than for IPOs in the bottom 25 percent by RSI.
There are several reasons why traders may want to short-sell a stock. Most short sales are hedged to reduce the risk of a short position. However, traders do take naked or speculative short positions when they believe that the stock is overvalued. The reasons for short selling are discussed below. Hedged Short Sales Most of the transactions in this category occur from perceived mispricings, some of which are discussed in Chapters 2 through 11 of the book. Index arbitrage occurs when an index futures contract trades at a price different from that implied by the underlying cash index. For example, if the S&P 500 index futures contract is trading at a price below that implied by the stock market, then the arbitrageur will buy the futures contract in the futures market and hedge that by shortselling all five hundred stocks on the stock market. Differences between prices on different exchanges for the same security can be arbitraged by taking a long position on one exchange and a short position on the other.
A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber
affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, 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
Still, the visit warmed me to the idea of working outside of academia, so when I got calls from investment banks the next year, I was ready to listen. 188 ccc_demon_165-206_ch09.qxd 7/13/07 2:44 PM Page 189 T H E B R AV E N E W W O R L D OF HEDGE FUNDS ARRIVEDERCI TARTAGLIA Tartaglia matched Bamberger’s revenue of $6 million the year after he took over the strategy. He started a new department at Morgan Stanley christened Analytical Proprietary Trading (APT). He automated Bamberger’s techniques, linked them to the SuperDOT network that had been developed for program trading and index arbitrage, and applied them to an array of thousands of stocks, often holding a portfolio containing more than 600 names at a time. In 1986, with his new scale of operation, he brought in $40 million. As the money rolled in, the department’s size and accoutrements swelled. APT grew to 40 professionals and an endless array of high-tech toys. They bought Silicon Graphics machines that used 3-D glasses to try to discern patterns in stock data.
The most successful strategies fade away and new ones emerge two or three years down the road, often based on securities that are new to the market. To see this point, consider the history of opportunistic strategies. Although they were not executed within the traditional hedge fund structure, some of the early opportunistic strategies included basis trading on the cheapest-to-deliver bond shortly after the introduction of the Treasury bond futures, and cash-futures index arbitrage in the years following the introduction of the S&P and the Value Line futures. Both strategies peaked within a few years, and a decade later amounted to little more than background radiation in the trading firmament. O’Connor’s Partnership was making hundreds of millions of dollars by applying the Black-Scholes formula to options in the nascent Chicago Board Options Exchange in the late 1970s and early 1980s, with a cadre of young traders grabbing their pricing sheets at the start of the day and taking their posts along the CBOE trading floor to apply delta hedges to mispriced options.
O’Connor’s Partnership was making hundreds of millions of dollars by applying the Black-Scholes formula to options in the nascent Chicago Board Options Exchange in the late 1970s and early 1980s, with a cadre of young traders grabbing their pricing sheets at the start of the day and taking their posts along the CBOE trading floor to apply delta hedges to mispriced options. By the mid-1980s, the writing was on the wall for margin contractions in the floor marketmaking business, and O’Connor’s sold itself to Swiss Bank. On the heels of the cash-futures and index arbitrage opportunities came statistical arbitrage, which was the first to emerge in a hedge fund structure. In 1985, the first statistical arbitrage strategy was developed at Morgan Stanley, by Gerry Bamberger, a young information technology (IT) person who had been assigned to work on some hedging issues on the equity trading floor. As we discussed earlier, Bamberger developed a pairs trading strategy that resulted in a burgeoning business for Morgan Stanley and spawned D.E.
algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business process, 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
As a result, faster informationarbitraging strategies have been perfected for the futures market, while systematic equity strategies remain underdeveloped to this day. By the time this book was written, the lead-lag effect between futures and spot markets had decreased from the 5- to 10-minute period documented by Stoll and Whaley (1990) to a 1- to 2-second advantage. However, profit-taking opportunities still exist for powerful high-frequency trading systems with low transaction costs. Indexes and ETFs Index arbitrage is driven by the relative mispricings of indexes and their underlying components. Under the Law of One Price, index price should be equal to the price of a portfolio of individual securities composing the index, weighted according to their weights within the index. Occasionally, relative prices of the index and the underlying securities deviate from the Law of One Price and present the following arbitrage opportunities.
If the price of the index-mimicking portfolio net of transaction costs exceeds the price of the index itself, also net of transaction costs, sell the index-mimicking portfolio, buy index, hold until the market corrects its index pricing, then realize gain. Similarly, if the price of the index-mimicking portfolio is lower than that of the index itself, sell index, buy portfolio, and close the position when the gains have been realized. Alexander (1999) shows that cointegration-based index arbitrage strategies deliver consistent positive returns and sets forth a cointegrationbased portfolio management technique step by step: 1. A portfolio manager selects or is assigned a benchmark. For a portfo- lio manager investing in international equities, for example, the benchmark can be a European, Asian, or Far East (EAFE) Morgan Stanley index and its constituent indexes. Outperforming the EAFE becomes the objective of the portfolio manager. 2.
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, butterfly effect, buttonwood tree, 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, Khan Academy, 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, Richard Stallman, 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
They were perhaps the first to use computers for actual trading, as well as for identifying trades. The early alpha seekers were the first combatants in the algo wars. Pairs trading, popular at the time, relied on statistical models. Finding stronger short-term correlations than the next guy had big rewards. Escalation beyond pairs to groups of related securities was inevitable. Parallel developments in futures markets opened the door to electronic index arbitrage trading. Automated market making was a valuable early algorithm. In quiet, normal markets buying low and selling high across the spread was easy 68 Nerds on Wall Str eet money. Real market makers have obligations to maintain a two-sided quote for their stocks, even in turbulent markets, which is often expensive. Electronic systems, without the obligations of market makers, not only were much faster at moving quotes, they could choose when not to make markets in a stock.
Quantitative information expressed as numbers will be combined with qualitative information expressed as text. Latencies will go to zero, and information will go to the sky. Get used to it. Notes 1. Rosenblatt Securities, at www.rblt.com, maintains one of the most complete public sites for information on the fast-changing world of dark liquidity. 2. White slips were used for buy orders, pink for sells. Index arbitrage, a strategy that would buy or sell a basket of index (e.g., S&P 500) stocks and a simultaneous opposite position in the index futures, was just getting started at this time. Index arbitrageurs would bring their hundreds of order slips to the floor in wheelbarrows. So as not to signal whether they were buyers or sellers, they would have pairs of wheelbarrows, one with white slips and one with pink, ready at the edge of the trading floor.
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, 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, 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
The simplest type of reversal strategy is to buy stocks that have experienced the lowest return over the past days and short those that had the highest return. More sophisticated reversal strategies (also called residual reversal strategies) seek to estimate each stock’s expected return in light of its characteristics and the returns of other stocks with similar characteristics, and then bet that the residual between the stock’s actual return and its expected return will revert. Index Arbitrage and Closed-End Fund Arbitrage Finally, stat arb traders pursue strategies that seek to arbitrage the difference between a “basket security” and its components. For example, they try to arbitrage the difference between stock index futures and the prices of the underlying stocks, the discrepancies between futures and an ETF, the difference between the ETF and its constituents, and the difference between a closed-end mutual fund and its underlying stock holdings.
See also backtests of strategies; investment styles; specific strategies hedge ratio (delta, Δ): in binomial option pricing model, 237; in convertible bond arbitrage, 275, 275f, 283; to make a strategy market neutral, 28; in slope trade, 251 hedging: as benefit of short-selling, 123; of convertible bonds versus straight bonds, 270; defined, 19; dynamic, 234, 235, 237–38, 240; in fixed-income arbitrage, 241; Scholes on broker-dealers and, 267; tail hedging, 59, 228 Heisenberg uncertainty principle of finance, 135 herding, 209, 210, 211–12 high-conviction trades: going for the jugular with, 12, 321; portfolio construction and, 55, 57 high-frequency trading (HFT), 10, 134, 134t, 135, 153–57; flash crash of 2010 and, 156–57; as market making, 44–45, 153–55 high-minus-low (HML) factor, 29, 137, 137n high-moneyness convertible bonds, 282, 282f, 284, 284f high water mark (HWM), 21–22, 35, 36f holding periods, 105–6; at Maverick Capital, 111–12 hurdle rate, 21 Huygens, Christiaan, 81 hybrid convertible bonds, 282, 282f idiosyncratic risk, 27–28; in information ratio, 30; washed out in quant investing, 144 illiquid assets, in asset allocation, 168, 170 illiquidity premium, 43 illiquid securities, defined, 63 IMA (investment management agreement), 25 immunization, 246, 251 implementation costs, 63–64. See also funding costs; transaction costs implementation shortfall (IS), 70–72, 73f implied cost of capital, 93 implied expected returns, 93 implied volatility, 239, 262 index arbitrage, 153 index funds, 28 index options: demand pressure for, 46; implied volatilities of, 239 index weightings, Maverick’s indifference to, 111 industry-neutral portfolio construction, 144; quant event of 2007 and, 146 industry rotation, 98 inefficient markets: Asness on successful strategies and, 164; defined, vii. See also efficiently inefficient markets inflation: aggregate demand and, 193f, 194; aggregate supply and, 193, 193f; bond returns and, 180; central bank policies and, 189–90; currency returns and, 182–83; economic environment and, 191–92, 191t; employment and, 193; equity returns and, 178–79; Federal Reserve policy and, 189–90; interest rates and, 189–90, 194, 250; supply or demand shocks and, 195t inflation risk premium, 196 information: efficient market hypothesis and, 201, 227; short sellers as providers of, 132; as source of profits, 39, 40–42, 40f information ratio (IR), 30, 31; adjusted for stale prices, 37 in-sample backtests, 50, 53 insider selling, 125, 128 insider trading, 9, 40–41, 294, 318 Integrated Resources, 129 interest rate futures, 190 interest-rate risk: in convertible bond arbitrage, 283; in event-driven investment, 292 interest rates: aggregate demand and, 194; in efficiently inefficient markets, 7t; inflation and, 189–90, 194, 250; in neoclassical finance, 7t; option instruments related to, 262; overnight, central banks and, 248–49.
Capital Ideas: The Improbable Origins of Modern Wall Street by Peter L. Bernstein
Albert Einstein, asset allocation, backtesting, Benoit Mandelbrot, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buy low sell high, capital asset pricing model, corporate raider, debt deflation, diversified portfolio, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, full employment, implied volatility, index arbitrage, index fund, interest rate swap, invisible hand, John von Neumann, Joseph Schumpeter, Kenneth Arrow, law of one price, linear programming, Louis Bachelier, mandelbrot fractal, martingale, means of production, money market fund, Myron Scholes, new economy, New Journalism, Paul Samuelson, profit maximization, Ralph Nader, RAND corporation, random walk, Richard Thaler, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, stochastic process, the market place, The Predators' Ball, the scientific method, The Wealth of Nations by Adam Smith, Thorstein Veblen, transaction costs, transfer pricing, zero-coupon bond, zero-sum game
There are hundreds of mutual funds specializing in big stocks, small stocks, emerging growth stocks, Treasury bonds, junk bonds, index funds, government-guaranteed mortgages, and international stocks and bonds from all around the world. There is ERISA to regulate corporate pension funds, and there are employee savings plans that enable employees to manage their own pension funds. There are markets for options (puts and calls) and markets for futures, and markets for options on futures. There is program trading, index arbitrage, and risk arbitrage. There are managers who provide portfolio insurance and managers who offer something called tactical asset allocation. There are butterfly swaps and synthetic equity. Corporations finance themselves with convertible bonds, zero-coupon bonds, bonds that pay interest by promising to pay more interest later on, and bonds that give their owners the unconditional right to receive their money back before the bonds come due.
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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, 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, South Sea Bubble, statistical model, 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
Treasury Secretary James Baker commented on Thursday that he might favor a further fall in the dollar against the German mark, and a bill was introduced in Congress on Friday to restrict hostile takeovers.3 Whatever the reasons for this decline, it meant that on the morning of Monday, October 19, the portfolio insurers lined up to sell S&P 500 futures on the Chicago Merc to rebalance their clients’ portfolios. There was no way for them to signal that their selling was the result not of reasoned evaluation but of pure reflex. The futures traders in Chicago surely had an inkling, but that message seems to have been lost on the way to New York. The index arbitrage that Ed Thorp pioneered five years before was by this time an everyday affair. Whenever the price of S&P 500 futures in Chicago got out of whack with that of the actual stocks trading in New York, one of several big brokerages and money managers bought one and sold the other for a quick and easy profit—in the process bringing prices back in line. That Monday, the mass selling by the portfolio insurers in Chicago drove the futures price well below where stock prices dictated it should be.
Richard Roll, “The International Crash of October 1987,” Financial Analysts Journal (Sept.–Oct. 1988): 19. 9. The main reports were the above-mentioned Brady report and the SEC’s The October 1987 Market Break: A Report by the Division of Market Regulation (Feb. 1988), which were both perceived by the Chicago exchanges as blaming them for the crash. Merton H. Miller summarizes the results of a Chicago Merc study that saw things differently in “The Economics and Politics of Index Arbitrage,” keynote address, fourth annual Pacific Basin Research Conference, Hong Kong, July 6–8, 1992, in Merton Miller on Derivatives (New York: John Wiley & Sons, 1997), 26–39. 10. Donnelly, “Efficient Market Theorists Are Puzzled.” 11. Robert J. Shiller, “Speculative Prices and Popular Models,” Journal of Economic Perspectives (Spring 1990): 58. 12. Hector, “What Makes Stock Prices Move?” 72. 13.
Alvin Roth, barriers to entry, conceptual framework, correlation coefficient, discrete time, disintermediation, distributed generation, experimental economics, financial intermediation, index arbitrage, information asymmetry, interest rate swap, inventory management, market clearing, market design, market friction, market microstructure, martingale, price discovery process, price discrimination, quantitative trading / quantitative ﬁnance, random walk, Richard Thaler, second-price auction, selection bias, short selling, statistical model, stochastic process, stochastic volatility, transaction costs, two-sided market, ultimatum game, zero-sum game
MULTIPLE SECURITIES AND MULTIPLE PRICES The discussion has proceeded on the assumption that we know the cointegrating vectors, such as the price difference in the simple structural model (section 10.2) or the basis for the cointegrating vectors (A in equation (10.14)). In practice, this is almost always the case. The bids, asks, trade prices, and so on, even from multiple trading venues, for a single security cannot reasonably diverge without bound. In applications involving index arbitrage, the weights of the component prices are set by the definition of the index, and are known to practitioner and econometrician alike. (Of course, it might be easier, particularly in an exploratory analysis, to estimate the weights, rather than look them up.) 10.3.4 Pairs Trading The previous remarks notwithstanding, there is one practical situation, in which the testing and estimation of cointegration vectors is important.
asset allocation, backtesting, capital asset pricing model, commoditize, computer age, correlation coefficient, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, fixed income, index arbitrage, index fund, intangible asset, Long Term Capital Management, p-value, passive investing, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, South Sea Bubble, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Yogi Berra, zero-coupon bond
The ADR price is kept at a level nearly identical to the currency-adjusted foreign market price by arbitrage. Annualized return: The constant return necessary to produce a given return or loss. For example, if a stock returns 0%, 0%, and 33.1% in three successive years, then the annualized return is 10% (1.1 ⫻ 1.1 ⫻ 1.1 ⫽ 1.331). Arbitrage: The simultaneous buying and selling of a given security in different markets at different prices, yielding a riskless profit. (The most prevalent variety is index arbitrage, which typically exploits small differences in prices between futures contracts and the underlying stocks.) Ask price: A broker’s price to sell a stock or bond; also called the offer price. 187 188 The Intelligent Asset Allocator Asset allocation: The process of dividing up one’s securities among broad asset classes, i.e., foreign and domestic stocks and foreign and domestic bonds. Asset class: Categories of stocks, bonds, and other financial assets.
always be closing, bank run, banking crisis, Big bang: deregulation of the City of London, Bolshevik threat, Boycotts of Israel, Bretton Woods, British Empire, California gold rush, capital controls, collective bargaining, corporate raider, Etonian, financial deregulation, fixed income, German hyperinflation, index arbitrage, interest rate swap, margin call, money market fund, Monroe Doctrine, North Sea oil, oil shale / tar sands, old-boy network, paper trading, Plutocrats, plutocrats, Robert Gordon, Ronald Reagan, short selling, strikebreaker, the market place, the payments system, too big to fail, transcontinental railway, Yom Kippur War, young professional
Once again there was a tendency to trace the crash to the internal mechanics of the market itself. In January 1988, Merrill Lynch, Shearson Lehman Hutton, and Goldman, Sachs suspended index arbitrage trading for their own accounts. But Morgan Stanley didn’t need to worry about angry small investors and exhibited its new renegade stance, despite the fact that Parker Gilbert was a governor of the New York Stock Exchange. It suspended its own “proprietary” program trading only after pressure from Congressman Edward J. Markey’s Subcommittee on Telecommunications and Finance. It was also notified by Maurice R. Greenberg, chief executive of the American International Group, a New York insurer, that his firm would cease business with houses that persisted in stock-index arbitrage for their own accounts. On May 10, 1988, in a splashy coordinated effort, Morgan Stanley, Salomon Brothers, Bear Stearns, Paine Webber, and Kidder, Peabody announced they would stop the practice.
Depression, migraine headaches, even sexual impotence were later reported among investors, but no aerial artistry as in 1929. Aside from a somewhat longer visitors’ queue at the New York Stock Exchange, the Corner betrayed little sense of calamity on this Black Monday. The Morgan houses were actually less remote from the 1987 crash than they’d been from the one in 1929. All the banks and brokerage houses were now trading operations. And Morgan Stanley was a major practitioner of stock-index arbitrage—computer-driven trades that exploited small price discrepancies between stocks in New York and stock-index futures in Chicago. Such transactions were blamed for wild market gyrations and even, unfairly, for the crash itself. In a secret, restricted computer room known as the Black Box for its sophisticated software—some programs forced traders to don 3-D glasses—fifty Morgan Stanley traders and analysts pored over information and scanned arbitrage opportunities.
., 65 Steagall, Henry, 362 Stedeford, Sir Ivan, 524 Steele, Charles, 70, 103, 335, 386, 469 Steel trust, 81–86, 99–100 Steffens, Lincoln, 65, 141, 149 Steichen, Edward, 86 Stenbeck, Jan, 611–12 Stephens, Claude, 563, 564 Sterling Drug, 707–709 Stetson, Francis Lynde, 73, 74–75, 150 Stettinius, Edward R., Jr., 441, 511 Stettinius, Edward R., Sr., 259–60 Export Department and, 188–91, 197, 202 insider trading and, 306–307 Stevens, Sir John, 569, 589, 590, 592, 613, 672 Arab clients and, 613–14 death of, 614, 615, 687 Stewart, Bernard, 472 Stillman, James, 90–91, 124, 126, 153 Stimson, Henry L, 339, 347, 352 Stock-index arbitrage, 699–700, 702 Stock market crash of 1929, 302–20, 422–23 bankers’ rescue and, 315–18, 355–56 Black Thursday, 303, 313, 315–17, 355–56 bonds and, 303, 304–305 deal making and, 307–308 Glass-Steagall Act and, 375–76 holding companies and, 308–10 insider trading and, 305–307 liquidity boom and, 302–303, 307, 313 margin lending and, 303–304, 314–15 pre-crash euphoria, 302–303 reasons for, 275, 302–15 second stage of, 317–19 stock pools and, 307 stocks and, 303–304, 308–10 Tragic Tuesday, 317–19 Stock market crash of 1987, 664, 699, 700–702, 717 Storehouse PLC, 677–78 Straight, Dorothy Whitney, 134–38, 140, 187, 201, 273 Straight, Willard Dickerman, 133–38, 187, 201, 345 Strauss, Frederick, 372 Strauss, Richard, 114 Stringher, Bonaldo, 280 Strong, Benjamin, 123, 124, 127, 144, 208, 218, 228, 247, 323, 491, 690 British gold standard and, 274–77 described, 244 at the Fed, 182, 211, 223, 244–45, 302, 313, 383 Italian loan and, 277, 282 Strong, George Templeton, 36 Strong, Katherine Converse, 182 Stuart, Harold, 416, 417, 502–503 Sudameris, 459 Sudan, 610, 618, 673 Sudetenland, 437–38 Suez affair, 529 Suffragettes, 246, 272–73 Sugiyama, Satoshi, see Phillips, John Sulaiman, Abdullah, 605, 606 Sullivan and Cromwell, 502, 703 Sumitomo Bank, 551, 558, 619, 695 Sumner, William Graham, 288 Sun Yat-sen, Dr., 136, 232 Suvich, Fulvio, 402 Sweet, Robert W., 651–52 Swiss Bank Corporation, 547 Switzerland, 547–48 Sword, William, 549, 570, 572, 590, 595, 596, 599, 611–12 Syria, 613, 614 T Tabuchi, Setsuya, 552 Taft, Robert A., 447, 448, 498 Taft, William Howard, 130–32, 183, 203, 217 trustbusting and, 130–31, 148–49 Takahashi, Korekiyo, 341, 344 Takeover Panel and Code, 571–73, 576, 577, 578, 678, 687, 689 Guinness and, 680, 682, 683, 688 Takeovers, see Hostile takeovers Takeshita, Noboru, 655 Talleyrand, 21 Tams, J.
Analysis of Financial Time Series by Ruey S. Tsay
Asian financial crisis, asset allocation, Bayesian statistics, Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, frictionless, frictionless market, implied volatility, index arbitrage, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve
Second, ∇ f t depends negatively on ∇ f t−1 in all three regimes. This is in agreement with the bid-ask bounce discussed in Chapter 5. Third, past log returns of the index futures seem to be more informative than the past log returns of the cash prices because there are more significant t ratios in ∇ f t−i than in ∇st−i . This is reasonable because futures series are in general more liquid. For more information on index arbitrage, see Dwyer, Locke, and Yu (1996). 8.7 PRINCIPAL COMPONENT ANALYSIS We have focused on modeling the dynamic structure of a vector time series in the previous sections. Of equal importance in multivariate time series analysis is the covariance (or correlation) structure of the series. For example, the covariance structure of a vector return series plays an important role in portfolio selection.
C. (1977), “A canonical analysis of multiple time series,” Biometrika, 64, 355–366. Brenner, R. J., and Kroner, K. F. (1995), “Arbitrage, cointegration, and testing the unbiasedness hypothesis in financial markets,” Journal of Financial and Quantitative Analysis, 30, 23–42. Cochrane, J. H. (1988), “How big is the random walk in the GNP?” Journal of Political Economy, 96, 893–920. Dwyer, Jr., G. P., Locke, P., and Yu, W. (1996), “Index arbitrage and nonlinear dynamics between the S&P 500 futures and cash,” Review of Financial Studies, 9, 301–332. Engle, R. F., and Granger, C. W. J. (1987), “Co-integration and error correction representation, estimation and testing,” Econometrica, 55, 251–276. Forbes, C. S., Kalb, G. R. J., and Kofman, P. (1999), “Bayesian arbitrage threshold analysis,” Journal of Business & Economic Statistics, 17, 364–372. 356 VECTOR TIME SERIES Fuller, W.
Market Sense and Nonsense by Jack D. Schwager
3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, 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
Schwager is a frequent seminar speaker and has lectured on a range of analytical topics, including the characteristics of great traders, investment fallacies, hedge fund portfolios, managed accounts, technical analysis, and trading system evaluation. He holds a BA in economics from Brooklyn College (1970) and an MA in economics from Brown University (1971). Index Adjustable-rate mortgages (ARMs) Allocation bias Allocation decisions, future AMEX Internet Index Arbitrage Arbitrary investment rules ARM subprime mortgages Asness, Clifford Automatic selling Automatic trading Average maximum retracement (AMR) Average pair correlation Average return Back-adjusted return measures gain-to-pain ratio (GPR) MAR and Calmar ratios return retracement ratio (RRR) risk-adjusted return performance measures Sharpe ratio Sortino ratio strategy comparison symmetric downside-risk (SDR) Sharpe ratio tail ratio Backfilling bias Backwardation Bankrupt stocks Bear market of 2008 Bear market returns Bear markets vulnerability Behavioral biases Bernanke, Ben Best strategy risk for standard deviation Beta and correlation quantitative measures Black Monday (October 19, 1987) Black Tuesday (October 29, 1920) Bottoms-up allocation Brady commission Bubbles and crashes emotion-driven housing (mid-2000s) Internet market price tech timing and level Bubbles and crashes Bull market Bull market of 2009 Burn rate Calls Calmar ratio and MAR ratio Capital gains Capital losses Capital structure arbitrage Carve-out portfolio Catastrophe insurance Cause-and-effect relationship Church, George J.
Wall Street: How It Works And for Whom by Doug Henwood
accounting loophole / creative accounting, activist fund / activist shareholder / activist investor, affirmative action, Andrei Shleifer, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, borderless world, Bretton Woods, British Empire, capital asset pricing model, capital controls, central bank independence, computerized trading, corporate governance, corporate raider, correlation coefficient, correlation does not imply causation, credit crunch, currency manipulation / currency intervention, David Ricardo: comparative advantage, debt deflation, declining real wages, deindustrialization, dematerialisation, diversification, diversified portfolio, Donald Trump, equity premium, Eugene Fama: efficient market hypothesis, experimental subject, facts on the ground, financial deregulation, financial innovation, Financial Instability Hypothesis, floating exchange rates, full employment, George Akerlof, George Gilder, hiring and firing, Hyman Minsky, implied volatility, index arbitrage, index fund, information asymmetry, interest rate swap, Internet Archive, invisible hand, Irwin Jacobs, Isaac Newton, joint-stock company, Joseph Schumpeter, kremlinology, labor-force participation, late capitalism, law of one price, liberal capitalism, liquidationism / Banker’s doctrine / the Treasury view, London Interbank Offered Rate, Louis Bachelier, market bubble, Mexican peso crisis / tequila crisis, microcredit, minimum wage unemployment, money market fund, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, oil shock, Paul Samuelson, payday loans, pension reform, Plutocrats, plutocrats, price mechanism, price stability, prisoner's dilemma, profit maximization, publication bias, Ralph Nader, random walk, reserve currency, Richard Thaler, risk tolerance, Robert Gordon, Robert Shiller, Robert Shiller, selection bias, shareholder value, short selling, Slavoj Žižek, South Sea Bubble, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, The Market for Lemons, The Nature of the Firm, The Predators' Ball, The Wealth of Nations by Adam Smith, transaction costs, transcontinental railway, women in the workforce, yield curve, zero-coupon bond
Another perspective on the market's labile temperament is the "volatility paradox," the enormous variations in volatility in stock prices (Shiller 1988; Schwert 1989). This volatility bears no statistical relation to the volatility of real-world phenomena like inflation, money growth, industrial production, interest rates, or business failures.^^ Moreover, despite the advent of computerized trading techniques such as portfolio insurance and index arbitrage during the 1980s, day-to-day volatility during that decade was little different from that of the 1970s, though both decades were more volatile than the 1950s and 1960s (Davis and White 1987). Schwert's data report stock volatility to have been low during times of great economic distress, like wars or the extended depression of the late 19th century, suggesting that in times of real social stress, people have more important things to worry about than their portfolios.
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 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
It is worse when the top managers acquire a taste for the arts. Several managers that I worked for underwent this conversion, which generally consisted of a mid-life crisis, a red sports car, divorcing their wife (leaving her with young children) and marrying a much younger woman. As part of the transition they acquired a taste for life’s finer things. This would lead to bizarre conversations: Me The equity index arbitrage business sucks. The margins are gone. We are taking a lot of dividend risk. We need to shut it down. My boss We must not lose sight of the bigger picture. DAS_C03.QXP 8/7/06 4:25 PM Page 74 Tr a d e r s , G u n s & M o n e y 74 Me (puzzled) What picture? My boss Well, something like Picasso’s Three Musicians. Me I am not sure I catch your drift. My boss We must go beyond the primary colours in the palette.
algorithmic trading, asset-backed security, bank run, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, collateralized debt obligation, computer age, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency peg, diversification, Doha Development Round, energy security, financial deregulation, financial innovation, fixed income, Francis Fukuyama: the end of history, George Gilder, housing crisis, Hyman Minsky, imperial preference, income inequality, index arbitrage, index fund, interest rate derivative, interest rate swap, Joseph Schumpeter, Kenneth Rogoff, large denomination, Long Term Capital Management, market bubble, Martin Wolf, Menlo Park, mobile money, money market fund, Monroe Doctrine, moral hazard, mortgage debt, Myron Scholes, new economy, oil shale / tar sands, oil shock, old-boy network, peak oil, Plutocrats, plutocrats, Ponzi scheme, profit maximization, Renaissance Technologies, reserve currency, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, Satyajit Das, shareholder value, short selling, sovereign wealth fund, The Chicago School, Thomas Malthus, too big to fail, trade route
We’ve never had a correction with these types of institutions and these types of instruments.”3 Others distilled the doubts about hedge funds themselves—the exotic quantitative mathematics, the obscure language of fixed-leg features and two-step binomial trees, and the humongous bank loans needed for the fifteen- or twenty-to-one leverage that alchemized mere decimal points into financial Olympic gold medals. New products often turned out to have Achilles’ heels, like the misbehaving index arbitrage of so-called program insurance, the derivative innovation widely blamed for the 1987 crash, and the junk bonds derogated after their inventor went to jail. In 2007, the failures were multiple: besides the CDO and exotic mortage embarrassments, hedge funds’ mathematical vulnerabilities included too many copycats doing the same thing, as well as an inability to deal with anarchic, almost random, volatility. . . .
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 low sell high, carried interest, Chuck Templeton: OpenTable, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, compound rate of return, 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, Richard Feynman, risk-adjusted returns, Robert Shiller, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, statistical arbitrage, stem cell, 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
If instead you buy a “basket” of twenty or thirty stocks that are chosen to track the index, you may be able to harvest increased tax benefits. That such a small number of stocks can, together, act like an index is shown by the Dow Jones Industrial Index, a basket of just thirty stocks. It has historically moved in concert with the S&P 500, even though the two indexes are chosen by entirely different methods and the very similar price behavior of the two was not planned. To do index arbitrage, PNP developed techniques in the mid-1980s for finding baskets of stocks that did a particularly good job of tracking an index. We used this very profitably the day after “Black Monday,” October 19, 1987, to capture a spread of over 10 percent between the S&P 500 Index and the futures contracts on it. Quants have honed this to a fine art and, through their trading, generally keep the price discrepancy very small.
Den of Thieves by James B. Stewart
corporate raider, creative destruction, discounted cash flows, diversified portfolio, fixed income, fudge factor, George Gilder, index arbitrage, Internet Archive, Irwin Jacobs, margin call, money market fund, Ponzi scheme, rolodex, Ronald Reagan, shareholder value, South Sea Bubble, The Predators' Ball, walking around money, zero-coupon bond
He was prepared to cash in his large stock position for enormous profits. But he was blocked by DeNunzio, who over the years had shrewdly bestowed stock on his own allies. He had recognized early on that a man like Gordon would almost inevitably clash with his hand-picked successor. Others at the firm favored other solutions. Max Chapman, Jr., the head of fixed income and financial futures, had turned Kidder, Peabody into a major player in the field of index arbitrage and program trading (using options on broad market indices traded in Chicago and computer-driven trading strategies). He had become DeNunzio's heir apparent. DeNunzio had tried to set up a rivalry between Chapman and Siegel, but Siegel had told DeNunzio that he wasn't interested in administering the firm. "Don't tell Chapman that," DeNunzio insisted. Now Chapman, recognizing the need for more capital, wanted to sell a 20% minority stake in the firm, probably to the Japanese.