capital asset pricing model

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

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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

Treynor, who was still deep in ruminations about the Capital Asset Pricing Model, found Black a stimulating intellectual companion. Black became so fascinated by CAPM that he gradually gave up his work with computers and information processing and shifted to finance. Black’s decision to switch career paths is another example of how the study of financial markets seems to pull scientists into its orbit. Bachelier was lured from mathematics, Sharpe from medicine, Osborne from astronomy, Working and Kendall from statistical theory, and Treynor from physics and math. Black decided that the Capital Asset Pricing Model was right up his alley—“the notion of equilibrium in the market for risky assets had great beauty for me. . . . I worked on the Capital Asset Pricing Model because I wanted to discover the truth.”2 He was especially attracted by what he calls the “cruel truth” of the model: “To get higher expected gain, you must take more risk.

When he visited there in early 1962, he went through the customary procedure of spending fifteen minutes with each faculty member and then presenting a paper to the group—once again, his ideas on the Capital Asset Pricing Model. Then it was time to get down to business: The final negotiation involved Allen Wallis, then Dean of the department, offering me a salary of $1500 more than whatever I was making, on the grounds that Lake Michigan required more than Lake Washington. I eventually responded that I thought Lake Washington was worth more than $1500 over Lake Michigan and we agreed to drop the negotiations. When I had talked to Harry Markowitz about it, he told me “Go with your gut.”30 Academic scuttlebutt being what it is, word of Sharpe’s paper on the Capital Asset Pricing Model reached Modigliani at MIT, who arranged for Sharpe and Treynor to exchange manuscripts some time in late 1962 or early 1963.

Little and Boston to work for Donald Regan at Merrill Lynch in New York, Black inherited some of the work that Treynor had been doing at ADL in analyzing and designing portfolio management systems. Inspired by what Treynor had done, Black set out to apply the Capital Asset Pricing Model to assets other than stocks. He experimented with it on bonds, corporate decisions on direct investments in plant and equipment, and the pricing of warrants. Black chose to work on warrants rather than options because the pricing of options in the over-the-counter market at that time—the market in which my firm was dabbling—was less efficient than the pricing of warrants, which traded on the active markets at the New York and American stock exchanges. When he applied the Capital Asset Pricing Model to the valuation of warrants, Black assumed that both the warrant and the associated stock would obey the model at every moment and at every possible price of the stock.

Monte Carlo Simulation and Finance by Don L. McLeish

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

Given only three bond prices with the same default characteristics, for example, and assuming constant interest rates so that Bs = (1 + r)s , we may solve for the values of the three unknown parameters (r, a, p) equations of the form MINIMUM VARIANCE PORTFOLIOS AND THE CAPITAL ASSET PRICING MODEL.35 P0 − pF (1 + r)−T = X (1 + a + r + ar)−s ds + (1 − p)F (1 + a + r + ar)−T . 0<s<T Market prices for a minimum of three different bonds would allow us to solve for the unknowns (r, a, p) and these are obtainable from three different bonds. Minimum Variance Portfolios and the Capital Asset Pricing Model. Let us begin by building a model for portfolios of securities that captures many of the features of market movements. We assume that by using the methods of the previous section and the prices of low-risk bonds, we are able to determine the value Bt of a risk-free investment at time t in the future.

MONTE CARLO SIMULATION AND FINANCE Don L. McLeish September, 2004 ii Contents 1 Introduction 1 2 Some Basic Theory of Finance 13 Introduction to Pricing: Single Period Models . . . . . . . . . . . . . . 13 Multiperiod Models. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Determining the Process Bt . . . . . . . . . . . . . . . . . . . . . . . . . 30 Minimum Variance Portfolios and the Capital Asset Pricing Model. . . 35 Entropy: choosing a Q measure . . . . . . . . . . . . . . . . . . . . . 56 Models in Continuous Time . . . . . . . . . . . . . . . . . . . . . . . . 67 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 3 Basic Monte Carlo Methods 97 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97 Uniform Random Number Generation . . . . . . . . . . . . . . . . . . 98 Apparent Randomness of Pseudo-Random Number Generators . . . . 109 Generating Random Numbers from Non-Uniform Continuous Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 Generating Random Numbers from Discrete Distributions . . . . . . . 166 Random Samples Associated with Markov Chains . . . . . . . . . . . 176 Simulating Stochastic Partial Differential Equations. . . . . . . . . . . 186 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 iii iv CONTENTS 4 Variance Reduction Techniques 203 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203 Variance reduction for one-dimensional Monte-Carlo Integration. . . . 207 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 252 5 Simulating the Value of Options 255 Asian Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 Pricing a Call option under stochastic interest rates. . . . . . . . . . . 266 Simulating Barrier and lookback options . . . . . . . . . . . . . . . . . 269 Survivorship Bias . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 298 6 Quasi- Monte Carlo Multiple Integration 301 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 301 Theory of Low discrepancy sequences . . . . . . . . . . . . . . . . . . 307 Examples of low discrepancy sequences . . . . . . . . . . . . . . . . . . 310 Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 324 CONTENTS Dedication: v to be added Acknowledgement 1 I am grateful to all of the past students of Statistics 906 and the Master’s of Finance program at the University of Waterloo for their patient reading and suggestions to improve this material, especially Keldon Drudge and Hristo Sendov.

i=1 If we now subtract the value invested at the beginning of the period and divide by the value at the beginning, we obtain P P n I(t) ni=1 wi (t)Si (t + 1) − I(t) ni=1 wi (t)Si (t) X Pn wi (t)Ri (t + 1) = I(t) i=1 wi (t)Si (t) i=1 which is just a weighted average of the individual stock returns. Note that it does not depend on the initial price of the stocks or the total amount that we MINIMUM VARIANCE PORTFOLIOS AND THE CAPITAL ASSET PRICING MODEL.37 invested at time t. The advantage in using returns instead of stock prices to assess investments is that the return of a portfolio over a period is a valueweighted average of the returns of the individual investments. When time is measured continuously, we might consider defining returns by using the definition above for a period of length h and then reducing h. In other words we could define the instantaneous returns process as Si (t + h) − Si (t) . h→0 Si (t) lim In most cases, the returns over shorter and shorter periods are smaller and smaller, and approach the limit zero so some renormalization is required above.

Investment: A History by Norton Reamer, Jesse Downing

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

Nevertheless, despite its drawbacks, Markowitz’s idea was a radical rethinking of portfolio design and allocation and paved the way for the next revolution in the intellectual theory of investing: the capital asset pricing model. Capital Asset Pricing Model The capital asset pricing model (CAPM), proposed by William Sharpe in 1964 and John Lintner in 1965, is an extension of the Markowitz model.32 It assumes that investors are in agreement about the expected returns and variances of the assets in the opportunity set and, further, that capital for investment can be borrowed and lent at the risk-free interest rate. This generates the condition that all investors hold the same combination of assets in the same proportions, creating the market portfolio.33 The notion of beta is central in the capital asset pricing model. Beta is a measure of how responsive an asset is to a change in the value of a benchmark.

It is here that Tobin’s famous separation theorem arises: the agent should hold some linear combination of the risk-free rate and the assets on the point of the efficient frontier that intersects with the capital allocation line (see figure 7.2).31 There is a remarkable implication of Tobin’s separation theorem: the only difference in the assets every agent in the market should hold is just in the combination of risk-free assets and the tangency portfolio (assuming, of course, that everyone agrees on the risk and return Higher expected return The efficient frontier Expected return The tangency portfolio Assets Capital allocation line Lower expected return Lower risk Higher risk Risk/volatility (standard deviation) Figure 7.2 Tobin’s Separation Theorem Source: “The Capital Asset Pricing Model—Fundamental Analysis,” EDinformatics, accessed 2013, http://edinformatics.com/investor _education/capital_asset_pricing_model.htm. The Emergence of Investment Theory 243 characteristics of all the assets in the opportunity set). Disagreement in these inputs, according to the model, is the only reason to have a portfolio whose components look any different from that of another rational investor doing mean-variance optimization. Which combination of risk-free asset and the tangency portfolio is selected is a function of how much risk one cares to accept.

CAPM instructs the practitioner that a portfolio analysis involves looking at more than just a collection of individually attractive assets; it involves, rather, a dissection of the blended whole. The capital asset pricing model is also useful in corporate finance and in determining whether or not firms should invest in particular projects. A firm has a certain cost of capital that can be measured rather simply by beta. If a project has a rate of return on a given investment of capital that is less than the minimum return as prescribed by the CAPM, the firm should steer clear of the project, which would not entail an efficient deployment of funds. Given this, one of the most difficult aspects of the capital asset pricing model is actually computing the forward beta, not unlike the problems of forecasting forward expected returns and volatilities discussed in the Markowitz model.


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The Investopedia Guide to Wall Speak: The Terms You Need to Know to Talk Like Cramer, Think Like Soros, and Buy Like Buffett by Jack (edited By) Guinan

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Albert Einstein, asset allocation, asset-backed security, Brownian motion, business process, capital asset pricing model, clean water, collateralized debt obligation, computerized markets, correlation coefficient, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, equity premium, fixed income, implied volatility, index fund, intangible asset, interest rate swap, inventory management, London Interbank Offered Rate, margin call, market fundamentalism, money market fund, mortgage debt, Myron Scholes, passive investing, performance metric, risk tolerance, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, short selling, statistical model, time value of money, transaction costs, yield curve, zero-coupon bond

(1) Financial assets or the financial value of assets, such as cash. (2) The factories, machinery, and equipment owned by a business and used for operations and production. Investopedia explains Capital Capital is an extremely vague term, and its specific definition depends on the context in which it is used. In general, it refers to financial resources available for use: working capital. Related Terms: • Capital Asset Pricing Model—CAPM • Capital Structure • Venture Capital • Capital Gain • Depreciation Capital Asset Pricing Model (CAPM) What Does Capital Asset Pricing Model (CAPM) Mean? A model that describes the relationship between risk and expected return; it is used to price securities. The general idea behind CAPM is that investors need to be compensated for investing their cash in two ways: (1) time value of money and (2) risk. (1) The time value of money is represented by the risk-free (rf) rate in the formula and compensates investors for placing money in any investment over 36 The Investopedia Guide to Wall Speak a period of time. (2) Risk calculates the amount of compensation the investor needs for taking on additional risk.

A line used in the capital asset pricing model that plots the rates of return for efficient portfolios, depending on the risk-free rate of return and the level of risk (standard deviation) for a particular portfolio. Investopedia explains Capital Market Line (CML) The CML is derived by drawing a tangent line from the intercept point on the efficient frontier to the point where the expected return equals the risk-free rate of return. The CML is considered superior to the efficient frontier because it takes into account the inclusion of a risk-free asset in the portfolio. The capital asset pricing model (CAPM) demonstrates that the market portfolio is essentially the efficient frontier. This is represented visually by the security market line (SML). Related Terms: • Capital Asset Pricing Model—CAPM • Efficient Market Hypothesis—EMH • Modern Portfolio Theory—MPT • Standard Deviation • Volume 38 The Investopedia Guide to Wall Speak Capital Structure What Does Capital Structure Mean?

See under Stop Buy-write. See Covered call CAGR. See Compound annual growth rate (CAGR) Call, 26, 33, 51-52, 56-57, 240, 282 Call option, 34, 117, 134, 214-215, 285-286 Call period, 33 Callable bond, 34 Candlestick, 34-35 Capital, 35. See also Shareholders’ equity; specific types of capital Capital Asset Pricing Model (CAPM), 9, 35-36, 37, 266-267 Capital gain, 36-37 Capital market line (CML), 37 Capital stock. See Outstanding shares Capital structure, 38 Capitalization issue. See Stock split CAPM. See Capital Asset Pricing Model (CAPM) Cash, 14, 176-177 Cash and cash equivalents (CCE), 38 Cash asset ratio. See Current ratio Cash conversion cycle (CCC), 39 Cash flow, 39-40, 109-111, 182-183, 198-199, 208 Cash flow statement, 40-41, 208 Cash ratio. See Current ratio C-CPI-U. See Chained urban consumers, CPI (C-CPI-U) CDO.


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

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3Com Palm IPO, accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, backtesting, beat the dealer, Bernie Madoff, BRICs, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, money market fund, mortgage tax deduction, new economy, Own Your Own Home, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, survivorship bias, The Myth of the Rational Market, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

The only part of total risk that investors will get paid for bearing is systematic risk, the risk that diversification cannot help. Thus, the capital-asset pricing model says that returns (and, therefore, risk premiums) for any stock (or portfolio) will be related to beta, the systematic risk that cannot be diversified away. THE CAPITAL-ASSET PRICING MODEL (CAPM) The proposition that risk and reward are related is not new. Finance specialists have agreed for years that investors do need to be compensated for taking on more risk. What is different about the new investment technology is the definition and measurement of risk. Before the advent of the capital-asset pricing model, it was believed that the return on each security was related to the total risk inherent in that security. It was believed that the return from a security varied with the variability or standard deviation of the returns it produced.

Thus, diversification in practice reduces some but not all risk. Three academics—the former Stanford professor William Sharpe and the late finance specialists John Lintner and Fischer Black—focused their intellectual energies on determining what part of a security’s risk can be eliminated by diversification and what part cannot. The result is known as the capital-asset pricing model. Sharpe received a Nobel Prize for his contribution to this work at the same time Markowitz was honored in 1990. The basic logic behind the capital-asset pricing model is that there is no premium for bearing risks that can be diversified away. Thus, to get a higher average long-run rate of return, you need to increase the risk level of the portfolio that cannot be diversified away. According to this theory, savvy investors can outperform the overall market by adjusting their portfolios with a risk measure known as beta.

Schematically, this situation appears as follows: Group I (60 Securities) Group II (60 Securities) Systematic risk (beta) = 1 for each security Systematic risk (beta) = 1 for each security Specific risk is high for each security Specific risk is low for each security Total risk is high for each security Total risk is low for each security Now, according to the old theory, commonly accepted before the advent of the capital-asset pricing model, returns should be higher for a portfolio made up of Group I securities, because each security in Group I has a higher total risk than each security in Group II, and risk, as we know, has its reward. With a wave of their intellectual wands, the academics changed that sort of thinking. Under the capital-asset pricing model, returns from the two portfolios should be equal. Why? First, remember the preceding chart How Diversification Reduces Risk: Risk of Portfolio (Standard Deviation of Return). (The forgetful can take another look.) There we saw that as the number of securities in the portfolio approached sixty, the total risk of the portfolio was reduced to its systematic level.


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

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

Richard Roll, “A Critique of the Asset Pricing Theory’s Tests Part I: On Past and Potential Testability of the Theory,” Journal of Financial Economics, 4(2) (1977), 129–76. 6. Fischer Black, Michael C. Jensen, and Myron Scholes, “The Capital Asset Pricing Model: Some Empirical Tests,” in Michael C. Jensen (ed.), Studies in the Theory of Capital Markets. New York: Praeger, 1972, pp. 79–121. 7. James Tobin, “Liquidity Preference, Separation and Asset Pricing,” Zeitschrift für Betriebswirtschaft, 3 (1983), 53–7. 12 Life and Legacy 1. Jonathan Burton, “Revisiting the Capital Asset Pricing Model,” Dow Jones Asset Manager (1998), pp. 20–8. 2. William F. Sharpe, “Capital Asset Prices – A Theory of Market Equilibrium Under Conditions of Risk,” Journal of Finance, XIX(3) (1964), 425–42. 13 The Early Years 1. www.nytimes.com/1998/11/14/business/when-theory-met-reality-specialreport-teachings-two-nobelists-also-proved-their.html?

The discipline of finance used the same technique in what we now know as the mean and variance approach. With measures of the mean and variance at hand, scholars then described how these measures were used to price an individual security. While we see that four scholars worked independently to develop the link between the mean return and the variance of a security and its market price, we will forever associate this new methodology of the Capital Asset Pricing Model (CAPM) with the great mind William Sharpe. 4 A Roadmap to Resolve the Big Questions 5 However, while Sharpe’s insights helped us better understand how an individual security is priced, the greatest need for the rapid pricing of securities was in the derivatives market. This new financial market, once the sleepy domain of farmers and food processors concerned about price stability for the future delivery of agricultural commodities, now represents an annual market value that rivals the combined size of the world’s economies.

This page intentionally left blank Part II William Forsyth Sharpe, John Lintner, Jan Mossin, and Jack Treynor Practitioners recognized it, Sir John Hicks surmised it, Jacob Marschak proposed it, and Harry Markowitz incorporated it into Modern Portfolio Theory. But it was not until William Forsyth Sharpe, John Lintner, Jan Mossin, and Jack Treynor transformed it into the realm of application that the mean-variance approach allowed us to price individual securities. What we now know as the Capital Asset Pricing Model has since become the foundation of securities pricing theory. This page intentionally left blank 8 The Early Years Jacob Marschak and Sir John Hicks pioneered the concept of a meanvariance approach, also known to mathematicians and physicists as the first and second moment approach, to the risk reward trade-off in the 1930s and 1940s. However, it was not until Harry Markowitz formally incorporated risk and uncertainty into financial decisionmaking in his description of the mean-variance approach to portfolio design in the 1950s that a more general theory of finance began to foment.


pages: 461 words: 128,421

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

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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

Modigliani read the paper, and invited Treynor to study economics and finance with him, which Treynor did for six months in 1962. While at MIT he further elaborated his theory of asset pricing, but he never submitted it for publication. For one thing, he thought there were other risk factors besides covariance that he needed to nail down. For another, he had to go back to work at Arthur D. Little. It was left to an assistant professor on the other side of the country to unveil what came to be known as the “capital asset pricing model.” He too arrived at it by way of Harry Markowitz. AFTER WRITING HIS BOOK ON portfolio selection, Markowitz had gone back to RAND, where he devised a computer simulation language, SIMSCRIPT, which is still in use today. And he forgot, for a while, about finance. Then, one day in 1960, William Sharpe presented himself at Markowitz’s office door. Sharpe was a RAND staffer and economics doctoral student at the University of California at Los Angeles.

“But at least I made the argument that if this really does tell us something about expected returns, it’s worth considering.” The journal had changed editors in the interim. This time Sharpe’s paper was accepted, and published in September 1964.26 In a footnote to the published article, Sharpe mentioned that after finishing it he had seen a draft of Treynor’s similar but unpublished work. In 1965, Harvard Business School’s John Lintner unveiled his own version of the capital asset pricing model. Lintner had a Ph.D. from Harvard’s Economics Department, and was hired at HBS in the 1940s over the objections of some of the school’s anti-theorist old guard. He did his best to fit in, becoming an outspoken critic of economics-based theories of corporate behavior like those of Modigliani and Miller. After talking to Jack Treynor about his asset pricing ideas, Lintner set out to show why Treynor was wrong.

Upon joining the Chicago faculty, Fama had been dispatched to teach portfolio theory and asset pricing. Those were subjects he had never gotten around to looking into as a graduate student, so he introduced himself to the work of Harry Markowitz, and read the landmark papers of Bill Sharpe and John Lintner as they appeared. It was Fama who was first to demonstrate that these two seemingly different versions of the capital asset pricing model (CAPM) were actually saying the same thing. And it was Fama who determined that if his efficient market was to have any real meaning, it needed to be joined at the hip to CAPM. Fama did the joining at the 1969 annual meeting of the American Finance Association. As published in the Journal of Finance the next year under the title “Efficient Markets: Theory and Evidence,” his paper became—along with Harry Markowitz’s portfolio theory, the M&M propositions, and the several CAPM papers—a core document of the new quantitative finance.


pages: 337 words: 89,075

Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio by Victor A. Canto

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accounting loophole / creative accounting, airline deregulation, Andrei Shleifer, asset allocation, Bretton Woods, buy low sell high, capital asset pricing model, commodity trading advisor, corporate governance, discounted cash flows, diversification, diversified portfolio, fixed income, frictionless, high net worth, index fund, inflation targeting, invisible hand, John Meriwether, law of one price, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, market bubble, merger arbitrage, money market fund, new economy, passive investing, Paul Samuelson, price mechanism, purchasing power parity, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, survivorship bias, the market place, transaction costs, Y2K, yield curve, zero-sum game

See market breadth Bush, George H.W., 55, 73, 76, 83, 238 Bush, George W., 55, 83-84, 101 buy-and-hold. See passive management C CAA. See cyclical asset allocation California energy crisis example (location effect), 194-198, 273 cap-weighted indexes versus equal-weighted indexes, 175-180, 242-245 capital asset pricing model (CAPM), 2-3, 19, 113, 253 capital gains, 72-73, 76-79, 83-84. See also return-delivery vehicles capital tax sensitivity (CATS), 213-214 capitalized earnings model (CEM), 90-100 CAPM (capital asset pricing model), 2-3, 19, 113, 253 Carhart, Mark, 165 case studies financial-management firm for high-net-worth, 145-146, 149 global financial management plan, 149-152 hedge funds, 157-161 lifecycle funds, 152-157 309 CATS (capital tax sensitivity), 213-214 CEM. See capitalized earnings model Clinton, William J., 55, 73, 76, 83, 90, 95, 238 commodities prices, location effect and, 201 consol, 91 consolidation, incentive for, 185 corporate behavior, tax-rate changes and, 68-70 capital gains, 76-79 debt financing, 73-76 incentive structure effects, 70-73, 80-84 corporate debt, 72-76.

Similarly, the higher the Sharpe ratio, the lower the risk in relation to the reward. The Sharpe ratio is calculated using the mean and standard deviation of an excess return. That is the net of the asset class return and the risk free rate (that is, three months’ T-bill yields). A related measure is obtained when the ratio is calculated based on the mean and return of a single investment. This ratio is also known as the information ratio. Then, there’s the capital asset pricing model (CAPM), which similarly looks at the relationship between an investment’s risk and its expected market return— or, more specifically, the ways investment risk should affect its expected return.3 2 One major insight of the CAPM is that not all risks should affect asset prices. As would be the case if two assets moved in the same direction, the volatility of the portfolio consisting of the two assets would remain the same as the individual assets.

But small-cap stocks offered the most intriguing choice: They delivered a much higher annual rate of return during the sample period—but with greater volatility. So, the question is straightforward: Do higher rates of return compensate an investor for the added risk? Arguably, systematic risk is the most important risk measure for investors who are considering the addition of an asset class to a diversified portfolio.3 According to the capital asset pricing model (CAPM), the only risk priced (that is, a risk that requires a higher rate of return) is risk correlated with the market. This is otherwise known as systematic risk, or market risk. Risk not correlated with the market is not priced because it can be diversified away. The CAPM offers a way to estimate systematic risk for different asset classes—what is known as beta. It also offers a precise measure of the additional return provided by the asset class over that required to compensate for the systematic risk—or what is known as alpha (or Jensen’s alpha).


pages: 320 words: 33,385

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

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

Markowitz’s work laid the foundation for the development of the theory of asset pricing, and we provide an overview of this in Section I.6.4. The theory is based on the principles of single-agent optimization and market equilibrium. We start by introducing the concept of the market portfolio which all rational investors in risky assets prefer to hold, and the capital market line. This leads on to the capital asset pricing model that was independently developed by Treynor (1965), Sharpe (1964) and Lintner (1965). Attempts to test the capital asset pricing model are surveyed and we outline its recent extensions. Section I.6.5 introduces the risk adjusted performance measures that are commonly used to rank investments. It is important to realize that we can always use a risk adjusted performance measure to order investments, but this ordering is not necessarily a preference ordering.

Solution The intercept of the CML is equal to the risk free rate of return, i.e. 0.05 in this example, and the slope of the CML is the Sharpe ratio (I.6.43) for the market portfolio, i.e. 01 − 005 = 025 = 02 Hence the equation of the CML is = 005 + 025 where and are the expected return and the standard deviation of a portfolio. I.6.4.2 Capital Asset Pricing Model Markowitz’s mean–variance analysis in the 1950s laid the foundations of the capital asset pricing model (CAPM) which was independently developed during the 1960s by Treynor (1965), Sharpe (1964) and Linter (1965). The CAPM assumes the existence of a market portfolio and the capital market line, but in themselves these do not tell us how to price risky assets. The purpose of the CAPM is to deduce how to price risky assets when the market is in equilibrium.

Finally, we provided an overview of the classical theories of asset pricing and portfolio selection. The capital asset pricing model that was introduced in the 1960s provided the first theory of the formation of asset prices in market equilibrium. Under this theory, assuming we know the returns on the market portfolio and the risk free asset, we can deduce the equilibrium expected return on the asset by using an estimate of its systematic risk. Most fund managers admit there is a possibility for deviations from the market equilibrium. That is, they expect that some assets have abnormal returns. The skill of a fund manager relies on accurate forecasts of the expected returns and systematic risk on all risky assets, Introduction to Portfolio Theory 267 and on the market portfolio. These forecasts are made in the context of the capital asset pricing model (or its extension to a multi-factor, time varying or higher moment asset pricing model) and they provide the fund manager with a solution to the portfolio selection problem.


pages: 239 words: 69,496

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

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

Wall Street Journal, May 17, 2016; and Moggridge, Donald. Maynard Keynes: An Economist’s Biography. London: Routledge, 1992. Aristotle’s discussion of friendships is from Aristotle. Nicomachean Ethics. Translated by W. D. Ross. http://classics.mit.edu/Aristotle/nicomachaen.8.viii.html. A rich intellectual history, and explanation, of the original capital asset pricing model is provided in Bernstein, Peter L. Capital Ideas: The Improbable Origins of Modern Wall Street. 1st ed. New York: Free Press, 1992; Perold, André F. “The Capital Asset Pricing Model.” Journal of Economic Perspectives 18, no. 3 (2004): 3–24; Sharpe, William F. “Capital Asset Prices with and Without Negative Holdings.” Nobel Lecture, Stanford University Graduate School of Business, Stanford, CA, December 7, 1990; and Black, Fischer. “Beta and Return.” Journal of Portfolio Management 20, no. 1 (1993): 8–18.

B Bachelier, Louis, 39–40 Bancroft, Anne, 95–96 bankruptcy, 8–9 automatic stay, 148 “discharge” of debt, 146 history of American law on, 145–46 moral questions, 150–52, 154, 160 opportunity for rebirth, 147 orderly bankruptcy, 148–49 “Bartleby, the Scrivener” (Melville), 46–48 Bellow, Saul, 48–49, 128, 137 Bentham, Jeremy, 121–23, 140 Berger, Harry, Jr., 123 Berryman, John, 33 Bessemer Venture Partners, 147 beta. See capital asset pricing model Bewkes, Jeff, 108 Bhagavad Gita, 155 Big Short, The (film), 97 Book of Matthew, 60 Brooks, Mel, 8, 75, 91–96 Critic, The (film), 93 relationship with Anne Bancroft, 95–96 relationship with Gene Wilder, 94 relationship with mother, 93 relationship with Sid Caesar, 94 Brown, Robert, Brownian motion, 38–40 Buck v. Bell (1927), 21 Buffett, Warren, 22 Burroughs, John, 117 Byrne, David, 13–14 C capital asset pricing model, xi, 53–56 advertising company betas, 53–54 gold, 54–55 high-beta and low-beta assets, 53–56 insurance, 54–55 LinkedIn relationships vs. friendship vs. unconditional love, 55–56 negative betas, 54–55 Case, Steve, 108, 109, 112 Cather, Willa, 9, 170, 172, 174 Charles, Ray, 99 Chaucer, Geoffrey, 18, 74 choix du roi, 104 Coase, Ronald, 115 Code of Justinian, Lex Rhodia, 23 commitments, 6, 154–56, 159–60 Confusion de Confusiones (de la Vega), 5, 43–44 Consilience (Wilson), 177 Cook, Tim, 77, 79, 80, 83 corporate finance, 7–9 corporate governance misaligned incentives, 80, 81 stock-based compensation, 81 Corrêa da Serra, José, 140 Cosmopolis (DeLillo), 165 Costa, Dora, 30 Crews, Frederick, 94 Crossroads of Should and Must, The (Luna), 90–92 Curry, Stephen, 51 D Davies, Owen, 25 Dead Poets Society (film), 17 debt overhang, 132–35 “Defence of Usury” (Bentham), 121 de la Vega, Joseph.

Stevens of Remains of the Day for insight on the power and peril of leverage. In short, this book is about how the humanities can illuminate the central ideas of finance. But this book is also about how the ideas of finance provide surprising insight on common aspects of our humanity. So, this book is also about how understanding insurance can help us make sense of and confront the disorder of the world; how understanding the capital asset pricing model can allow us to realize the value of relationships and the nature of unconditional love; how understanding value creation can help us live a meaningful life; how understanding bankruptcy can help us react to failure; and how appreciating theories of leverage can teach us about the value of commitments. For readers unfamiliar with, but curious about, finance, this book attempts to outline the main ideas of finance without a single equation or graph—and only with stories.


pages: 408 words: 85,118

Python for Finance by Yuxing Yan

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asset-backed security, business intelligence, capital asset pricing model, constrained optimization, correlation coefficient, distributed generation, diversified portfolio, implied volatility, market microstructure, P = NP, p-value, quantitative trading / quantitative finance, Sharpe ratio, time value of money, value at risk, volatility smile, zero-sum game

80 Importing a module 80 Adopting a short name for an imported module 81 Showing all functions in an imported module 82 Comparing "import math" and "from math import *" 82 Deleting an imported module 83 Importing only a few needed functions 84 Finding out all built-in modules 85 Finding out all the available modules 86 Finding the location of an imported module 87 More information about modules 88 Finding a specific uninstalled module 90 Module dependency 90 Summary 92 Exercises 93 Installation of NumPy and SciPy Launching Python from Anaconda Examples of using NumPy Examples of using SciPy Showing all functions in NumPy and SciPy More information about a specific function Understanding the list data type Working with arrays of ones, zeros, and the identity matrix Performing array manipulations Performing array operations with +, -, *, / Performing plus and minus operations [ iii ] 96 96 97 98 102 103 103 104 105 105 105 Table of Contents Performing a matrix multiplication operation 105 Performing an item-by-item multiplication operation 107 The x.sum() dot function 107 Looping through an array 108 Using the help function related to modules 108 A list of subpackages for SciPy 109 Cumulative standard normal distribution 109 Logic relationships related to an array 110 Statistic submodule (stats) from SciPy 111 Interpolation in SciPy 112 Solving linear equations using SciPy 113 Generating random numbers with a seed 114 Finding a function from an imported module 116 Understanding optimization 116 Linear regression and Capital Assets Pricing Model (CAPM) 117 Retrieving data from an external text file 118 The loadtxt() and getfromtxt() functions 118 Installing NumPy independently 119 Understanding the data types 119 Summary 120 Exercises 120 Chapter 7: Visual Finance via Matplotlib Installing matplotlib via ActivePython Alternative installation via Anaconda Understanding how to use matplotlib Understanding simple and compounded interest rates Adding texts to our graph Working with DuPont identity Understanding the Net Present Value (NPV) profile Using colors effectively Using different shapes Graphical representation of the portfolio diversification effect Number of stocks and portfolio risk Retrieving historical price data from Yahoo!

Most programming books offer just a few complete and complex programs. The number of programs is far too less than enough. There are two side effects for such an approach. First, finance students are drowned in programming details, get intimidated, and eventually lose interest in learning a computer language. Second, they don't learn how to apply what they just learned, such as running a capital asset pricing model (CAPM) to estimate IBM's beta from 1990 to 2013. This book offers about 300 complete Python programs around many finance topics. Using real-world data Another shortcoming of the majority of books for programming is that they use hypothetical data. In this book, we use real-world data for various financial topics. For example, instead of showing how to run CAPM to estimate the beta (market risk), I show you how to estimate IBM, Apple, or Walmart's betas.

In particular, we will cover the following topics: • Installation of NumPy and SciPy • Launching Python from Anaconda • Examples of using NumPy and SciPy • Showing all functions in NumPy and SciPy • Getting more information about a specific function • Understanding the list data type • Array in NumPy, logic relationship related to arrays • Working with arrays of ones, zeros, and identity matrix • Performing array operations: +, -, *, and / • The x.sum() dot function • Looping through an array • A list of subpackages for SciPy • Cumulative standard normal distribution • Generating random numbers • Statistic submodule (stats) from SciPy • Interpolation, linear equations, and optimization Introduction to NumPy and SciPy • Linear regression and Capital Assets Pricing Model (CAPM) • Retrieving data from an external text file • Installing NumPy independently • Understanding the data types Installation of NumPy and SciPy In the previous chapter, we discussed the module dependency and how it might be difficult to install a new module because it depends on many other modules. Fortunately, several super packages, such as Anaconda and Enthought Canoy, could be used to install several (or many) modules simultaneously.


pages: 504 words: 139,137

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

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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

PRINCIPLES OF NEOCLASSICAL FINANCE AND ECONOMICS VS. THOSE IN AN EFFICIENTLY INEFFICIENT MARKET Neoclassical Finance and Economics Efficiently Inefficient Markets Modigliani–Miller Irrelevance of capital structure Capital structure matters because of funding frictions Two-Fund Separation Everyone buys portfolios of market and cash Investors choose different portfolios depending on their individual funding constraints Capital Asset Pricing Model Expected return proportional to market risk Liquidity risk and funding constraints influence expected returns Law of One Price and Black–Scholes No arbitrage, implied derivative prices Arbitrage opportunities arise as demand pressure affects derivative prices Merton’s Rule Never exercise a call option and never convert a convertible, except at maturity/dividends Optimal early exercise and conversion free up cash, save on short sale costs, and limit transaction costs Real Business Cycles and Ricardian Equivalence Macroeconomic irrelevance of policy and finance Credit cycles and liquidity spirals driven by the interaction of macro, asset prices, and funding constraints Taylor Rule Monetary focus on interest rate policy Two monetary tools are interest rate (the cost of loans) and collateral policy (the size of loans) II.

If a hedge fund has a beta of zero (i.e., a market-neutral hedge fund) and an alpha of 6% per year, this means that the hedge fund is expected to make the risk-free return plus 6% per year. For instance, if the risk-free rate is 2% per year, the hedge fund is expected to make 8%, but the actual realization could be far above or below that, depending on the realized idiosyncratic shock. The classic capital asset pricing model (CAPM) states that the expected return on any security or any portfolio is determined solely by the systematic risk, beta. In other words, CAPM predicts that alpha is equal to zero for any investment. Therefore, a hedge fund’s search for alpha is a quest to defy the CAPM, to earn higher returns than simple compensation for systematic risk. A hedge fund’s true alpha and beta are estimated with significant error.

Also, when asked how much added return Harvard’s Endowment needs in private equity in return for tying up your money for five or 10 years, their CEO answered: We should be getting an incremental return for that illiquidity—and we call that our illiquidity premium—of at least 300 basis points annually on average over what we are expecting in publicly traded stocks. —Jane Mendillo, CEO of Harvard Management (Barron’s Feb. 8, 2014) Liquidity risk is an important reason why the standard capital asset pricing model (CAPM) does not work well in practice. Financial markets may be better approximated by the liquidity-adjusted CAPM.1 This model says that investors care about a security i’s return Ri net of its transaction cost TCi. As a result, the CAPM should apply for net returns Ri − TCi: where λ is the risk premium and βi measures the security’s covariance with the net return of the overall market M, The implication is that gross returns are determined by This equation says that investors required return E(Ri) is the risk-free rate plus the expected transaction cost E(TCi), plus compensation for four risks multiplied by the risk premium λ.


pages: 364 words: 101,286

The Misbehavior of Markets by Benoit Mandelbrot

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

price change departure from price swings following probability distributions as risk measured with three different Beta (β) analyzing investments with CAPM with definition of expected return in P/E effect with stock price with Bienaymé, Irénée Binomial time bending Black, Fischer background of eulogy of influence of modern finance influenced by options valued by Black Monday Black-Scholes formula calculation of origin of problem with results of risk evaluation with uses of volatility with Blindfolded archer’s score chance of Bond trading Paris exchange of Book-to-market Bouchaud, Jean-Philippe Bourbaki Boussinesq, Joseph Box-counting dimension fractals with Brahe, Tycho Bridge range in fractal geometry Bronchia fractals Brown, Robert Brownian motion charts with computer-simulated chart of dependence with Dow price movement compared to financial modeling with multifractal model with Nile river flooding with ordered appearance of pollen observed with price changes following risk values following Buffett, Warren E. Bull market Burton, Richard Francis Calculus Calvet, Laurent Canary Wharf Cantor, Georg Cantor dust fractals Capital Asset Pricing Model (CAPM) APT and discovery of finance with Merrill Lynch’s use of premise of results of study of time-scale with Capital budgeting Bachelier’s theories influencing Capital Fund Management CAPM. See Capital Asset Pricing Model Cartier-Bresson photograph Cartoon. See Fractal cartoon Cat brain activity Cauchy, Augustin-Louis distribution by exceptional chance seen by Center of gravity Cerf, Georges Certified Financial Adviser Chance blindfolded archer’s score with corporate finance based on determinism v.

The questionnaire was three pages long and took about seventeen minutes to fill out; and seventeen minutes is rather a lot of time to ask of the paymasters of the universe that most big-company CFOs imagine themselves to be. Still, 392 responded. The answer that came back: When it comes to estimating their cost of capital—an essential ingredient in any financial decision—the method used most widely was the Capital Asset Pricing Model, or CAPM. In all, 73.5 percent said they use it. Nor is this unique to U.S. Fortune 500 companies. A similar survey of CFOs in sixteen European countries in 2001 found the same acronym, CAPM, on the lips of 77 percent. It is also in the political phrase-books. When Central Hudson Gas & Electric Corp. wanted to raise its electricity prices in New York in 2001, CAPM was part of the rationale argued to the regulator.

And now a practical point, which helps explain why this formula became so popular in the world of finance. It takes all of Markowitz’s tedious portfolio calculations and reduces them to just a few. Work up a forecast for the market overall, and then estimate the βfor each stock you want to consider. From 495 calculations for a thirty-stock portfolio with Markowitz and portfolio theory, you simplify to thirty-one with Sharpe and the Capital Asset Pricing Model, as it came to be called. Looking at the entire New York Stock Exchange: From 3.9 million with Markowitz, you prune to 2,801 with Sharpe. This is no longer a job for a mainframe and a statistician; it is for a personal computer and a broker, or even an individual investor. The impact of Sharpe’s formula was not immediately apparent, even to him. After finishing his thesis, he wrote his ideas up in an article for publication in one of the leading academic journals—a process, as every researcher knows, fraught with uncertainty, politics, and, often, disappointment.


pages: 345 words: 86,394

Frequently Asked Questions in Quantitative Finance by Paul Wilmott

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

Figure 2-3: Reward versus risk, a selection of risky assets and the efficient frontier (bold). Harry Markowitz, together with Merton Miller and William Sharpe, was awarded the Nobel Prize for Economic Science in 1990. References and Further Reading Markowitz, HM 1952 Portfolio selection. Journal of Finance 7 (1) 77-91 Ingersoll, JE Jr 1987 Theory of Financial Decision Making. Rowman & Littlefield What is the Capital Asset Pricing Model? Short Answer The Capital Asset Pricing Model (CAPM) relates the returns on individual assets or entire portfolios to the return on the market as a whole. It introduces the concepts of specific risk and systematic risk. Specific risk is unique to an individual asset, systematic risk is that associated with the market. In CAPM investors are compensated for taking systematic risk but not for taking specific risk.

References and Further Reading What is the Central Limit Theorem and What are its Implications for Finance? References How is Risk Defined in Mathematical Terms? References What is Value at Risk and How is it Used? References and Further Reading What is CrashMetrics? References and Further Reading What is a Coherent Risk Measure and What are its Properties? References and Further Reading What is Modern Portfolio Theory? References and Further Reading What is the Capital Asset Pricing Model? References and Further Reading What is Arbitrage Pricing Theory? References and Further Reading What is Maximum Likelihood Estimation? References and Further Reading What is Cointegration? References and Further Reading What is the Kelly criterion? References and Further Reading Why Hedge? References and Further Reading What is Marking to Market and How Does it Affect Risk Management in Derivatives Trading?

What is arbitrage? 25 3. What is put-call parity? 28 4. What is the central limit theorem and what are its implications for finance? 31 5. How is risk defined in mathematical terms? 36 6. What is value at risk and how is it used? 40 7. What is Crash Metrics ? 44 8. What is a coherent risk measure and what are its properties? 48 9. What is Modern Portfolio Theory? 51 10. What is the Capital Asset Pricing Model? 54 11. What is Arbitrage Pricing Theory? 58 12. What is Maximum Likelihood Estimation? 61 13. What is cointegration? 67 14. What is the Kelly criterion? 70 15. Why Hedge? 73 16. What is marketing to market and how does it affect risk management in derivatives trading? 79 17. What is the Efficient Markets Hypothesis? 83 18. What are the most useful performance measures? 87 19.

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

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

Exercise 5.15 In a market consisting of the three securities in Exercise 5.12, consider the portfolio on the efficient frontier with expected return µV = 21%. Compute the values of γ and µ such that the weights w in this portfolio satisfy γwC = m − µu. 118 Mathematics for Finance 5.4 Capital Asset Pricing Model In the days when computers where slow it was difficult to use portfolio theory. For a market with n = 1, 000 traded securities the covariance matrix C will have n2 = 1, 000, 000 entries. To find the efficient frontier we have to compute the inverse matrix C −1 , which is computationally intensive. Accurate estimation of C may pose considerable problems in practice. The Capital Asset Pricing Model (CAPM) provides a solution that is much more efficient computationally, does not involve an estimate of C, but offers a deep, even if somewhat oversimplified, insight into some fundamental economic issues.

For the price of one volume it teaches two Nobel Prize winning theories, with plenty more included for good measure. How many undergraduate mathematics textbooks can boast such a claim? Building on mathematical models of bond and stock prices, these two theories lead in different directions: Black–Scholes arbitrage pricing of options and other derivative securities on the one hand, and Markowitz portfolio optimisation and the Capital Asset Pricing Model on the other hand. Models based on the principle of no arbitrage can also be developed to study interest rates and their term structure. These are three major areas of mathematical finance, all having an enormous impact on the way modern financial markets operate. This textbook presents them at a level aimed at second or third year undergraduate students, not only of mathematics but also, for example, business management, finance or economics.

Portfolio Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.1 Risk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 5.2 Two Securities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 5.2.1 Risk and Expected Return on a Portfolio . . . . . . . . . . . . . . 97 5.3 Several Securities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.3.1 Risk and Expected Return on a Portfolio . . . . . . . . . . . . . . 107 5.3.2 Efficient Frontier . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114 5.4 Capital Asset Pricing Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.4.1 Capital Market Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 5.4.2 Beta Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120 5.4.3 Security Market Line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 6. Forward and Futures Contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.1 Forward Contracts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 6.1.1 Forward Price . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 6.1.2 Value of a Forward Contract . . . . . . . . . . . . . . . . . . . . . . . . . 132 6.2 Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 6.2.1 Pricing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136 6.2.2 Hedging with Futures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 7.


pages: 892 words: 91,000

Valuation: Measuring and Managing the Value of Companies by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer, Franziska Manoury

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activist fund / activist shareholder / activist investor, air freight, barriers to entry, Basel III, BRICs, business climate, business process, capital asset pricing model, capital controls, Chuck Templeton: OpenTable, cloud computing, commoditize, compound rate of return, conceptual framework, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, discounted cash flows, distributed generation, diversified portfolio, energy security, equity premium, fixed income, index fund, intangible asset, iterative process, Long Term Capital Management, market bubble, market friction, meta analysis, meta-analysis, Myron Scholes, negative equity, new economy, p-value, performance metric, Ponzi scheme, price anchoring, purchasing power parity, quantitative easing, risk/return, Robert Shiller, Robert Shiller, shareholder value, six sigma, sovereign wealth fund, speech recognition, survivorship bias, technology bubble, time value of money, too big to fail, transaction costs, transfer pricing, value at risk, yield curve, zero-coupon bond

See also Weighted average cost of capital (WACC) beta, 287–301 capital structure, 308–313 in emerging markets, 718–725 estimating cost of debt, 304–308 below-investment-grade debt, 306–307 bond ratings and yield to maturity, 304–306 interest tax shield, 307–308 estimating cost of equity, 286–303 adjusting for industry/company risk, 292–303 arbitrage pricing theory, 296–297 capital asset pricing model (CAPM), 293–294, 297–304 Fama-French three-factor model, 294–296 market implied cost of equity, 290–292 market return, 286–292 estimating in foreign currency, 490–502 in multiple business units, 387–389 for operating leases, 435–436 target weights, 308–312 Cost of debt, estimating, 304–305, 546 Cost of equity: capital asset pricing model (CAPM), 297–303 estimating, 286–303, 545–546 (see also Cost of capital, estimating cost of equity) alternatives to capital asset pricing model (CAPM), 294–297 beta and, 297–303 (see also Beta) leverage and, 768–769 levered/unlevered, 157–159, 833–836 Cost of goods sold (COGS), 237–238 Costs, fixed vs. variable, 252 Cost structure health metrics, 582–583 Coughlin, Chris, 631 Country risk premium, 712, 713–716, 724–725, 726–728 Coverage, 847–849 Covidien, 632 Credit health, 221–225 Credit ratios, and inflation, 480 Credit risk, 779 Credit spreads, 661 Cross-border valuation, 489–510.

If the cost of capital varied from 6 to 15 percent instead of 8 to 10 percent, many more companies would have P/Es below 8 and above 25. 44 CONSERVATION OF VALUE AND THE ROLE OF RISK Whether a company’s cost of capital is 8 percent or 10 percent or somewhere in between is a question of great dispute (the cost of capital is discussed in more detail in Chapter 13). For decades, the standard model for measuring differences in costs of capital has been the capital asset pricing model (CAPM). The CAPM has been challenged by academics and practitioners, but so far, no practical competing model has emerged.9 Anyway, when returns on capital across companies vary from less than 5 percent to more than 30 percent (sometimes even within the same sector), a one-percentage-point difference in the cost of capital seems hardly worth arguing about. General risk affecting all companies may be priced into the cost of capital, but that does not mean executives do not need to worry about risk.

The chapter concludes with a discussion of WACC estimation for companies whose capital structure is complex. 283 284 ESTIMATING THE COST OF CAPITAL WEIGHTED AVERAGE COST OF CAPITAL In its simplest form, the weighted average cost of capital equals the weighted average of the after-tax cost of debt and cost of equity:1 WACC = D E kd (1 − Tm ) + ke V V where D∕V = target level of debt to enterprise value using market-based (not book) values E∕V = target level of equity to enterprise value using market-based values kd = cost of debt ke = cost of equity Tm = company’s marginal income tax rate The cost of equity is determined by estimating the expected return on the market portfolio, adjusted for the risk of the company being valued. In this book, we use the capital asset pricing model (CAPM) to estimate a company’s risk adjustment factor. The CAPM adjusts for company-specific risk through the use of beta, which measures how a company’s stock price responds to movements in the overall market. Since a high correlation between a stock and the market increases the volatility of the market portfolio, investors require a high return to hold that stock. Consequently, stocks with high betas have expected returns that exceed the market return; the converse is true for lowbeta stocks.

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

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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

Valuation formed the cornerstone of the principles that Benjamin Graham and David Dodd put forth more than 70 years ago in their investment classic Security Analysis.6 SMALL- AND LARGE-CAP STOCKS Cracks in the capital asset pricing model’s predictions of stock returns appeared well before Fama and French’s research. In 1981, Rolf Banz, a graduate student at the University of Chicago, investigated the returns on stocks using the database that had been recently compiled by the Center for Research in Security Prices (CRSP) located at the university. He found that small stocks systematically outperformed large stocks, even after adjusting for risk as defined within the framework of the capital asset pricing model.7 To illustrate this point, the returns from 1926 through 2006 on 10 groups of 4,252 stocks sorted by market capitalization are shown in Table 9-1.

Click here for terms of use. 140 PART 2 Valuation, Style Investing, and Global Markets Yet finance theory has shown that if capital markets are “efficient” in the sense that known valuation criteria are already factored into prices, investing on the basis of these fundamentals factors will not improve returns. In an efficient market, only higher risk will enable investors to receive higher returns. The capital asset pricing model (CAPM) has shown that the correct measure of a stock’s risk is the correlation of its return with the overall market, known as beta.2,3 Beta can be estimated from historical data, and it represents the fundamental risk of an asset’s return that cannot be eliminated in a welldiversified portfolio and for which investors must be compensated. If beta is greater than 1, the stock requires a return greater than the market, and if it is less than 1, a lesser return is required.

In 1992, Eugene Fama and Ken French wrote an article, published in the Journal of Finance, which determined that there are two factors, one relating to the size of the stocks and the other to the valuation of stocks, that are far more important in determining a stock’s return than the beta of a stock.4 After further analyzing returns, they claimed that the evidence against the CAPM was “compelling” and that “the average return anomalies . . . are serious enough to infer that the [CAPM] model is not a useful approximation” of a stock’s return, and they suggested researchers investigate “alternative” asset pricing models or “irrational asset pricing stories.”5 2 The capital asset pricing model was developed by William Sharpe and John Lintner in the 1960s. See William Sharpe, “Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk,” Journal of Finance, vol. 19, no. 3 (September 1964), p. 442, and John Lintner, “The Valuation of Risk Assets and the Selection of Risky Investment in Stock Portfolios and Capital Budgets,” Review of Economics and Statistics, vol. 47, no. 1 (1965), pp. 221–245. 3 Greek letters are used to designate the coefficients of regression equations.


pages: 317 words: 106,130

The New Science of Asset Allocation: Risk Management in a Multi-Asset World by Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi

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asset allocation, backtesting, Bernie Madoff, Black Swan, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index fund, interest rate swap, invisible hand, market microstructure, merger arbitrage, moral hazard, Myron Scholes, passive investing, Richard Feynman, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, statistical model, survivorship bias, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve, zero-sum game

See also Indices alternative risk-adjusted, 42 alternatives, 104 asset, 194 asset class, 271–277 biases, 192 commodities, 179–185 correlation, 137 determining appropriate, 111–117 and equity exposure, 135 investable alternatives, 54 performance, 105 principal in determination of, 54 private equity, 170–173 real estate, 173–179 INDEX stocks and bonds, 168–170 and strategic asset allocation, 99–100 Beta, 10, 38, 39–57, 97 and benchmark return, 64 benefits and limitations of, 40 determination, 46–48, 48–50 and market changes, 41 and traditional alternative investments, 67 Biases, 141, 143, 192, 194 Bid-ask spread, 197 Biggs, Barton, 216 Black, Fisher, 11 Black-Litterman model, 95 Bonds, 58, 70 indices, 168–170 Bootstrapped portfolios, 94 Bottom up replication, 123 Break-even analysis, 43 Business cycle, 160, 250–270 Buy-write strategies. See Covered call Call option, 11 Cambridge Associates, 170 Can-risk capacity, 117–118 Capital Asset Pricing Model (CAPM). See CAPM (Capital Asset Pricing Model) Capital International Stock Indices, 168 Capital Market Line (CML), 5–6 CAPM (Capital Asset Pricing Model), 4–6, 18, 62–63 acceptance of, 28 and efficient market hypothesis, 6–10 and market risk, 43 Cash flow, 98 Casualty insurance, 98 CISDM CTA indices, 149, 150, 261, 262 CISDM ELS index, 193 CISDM Fund of Fund indices, 267, 268 CISDM Hedge Fund indices, 55, 131, 142, 144, 145, 185 CISDM indices, 259, 260, 261, 262, 263 Clustering, volatility, 95 Collar strategy, 234 Collateralized debt obligations (CDOs), 228, 229 Commodities, 59, 61, 65, 129, 130, 143–148, 160–165 benchmarks, 179–185, 275 futures, 12 Index return and risk performance, 162–163 volatility, 182, 185 Commodity Futures Trading Commission (CFTC), 11 Commodity pool operators (CPOs), 143 Commodity Research Bureau, 265, 266 Commodity risk, 196 Commodity trading advisors.

These author(s) proposed that if investors invested in the mean-variance efficient market portfolio, then the required rate of return of an individual security would be directly related to its marginal contribution to the volatility of that mean-variance efficient market portfolio; that is, the risk of a security (and therefore its expected return) could not be determined while ignoring its role in a diversified portfolio. A REVIEW OF THE CAPITAL ASSET PRICING MODEL The model developed by Sharpe and others is known as the Capital Asset Pricing Model (CAPM). While the results of this model are based on several unrealistic assumptions, it has dominated the world of finance and asset allocation for the past 40 years. The main foundation of the CAPM is that regardless of their risk-return preference, all investors can create desirable mean-variance efficient portfolios by combining two portfolios/assets: One is a unique, highly diversified, mean-variance efficient portfolio (market portfolio) and the other is the riskless asset.

Includes bibliographical references and index. ISBN 978-0-470-53740-4 (cloth) 1. Asset allocation. 2. Risk management. I. Crowder, Garry B., 1954- II. Kazemi, Hossein, 1954- III. Title. HG4529.5.S3366 2010 332.6--dc22 2009047243 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Contents Preface Acknowledgments CHAPTER 1 A Brief History of Asset Allocation In the Beginning A Review of the Capital Asset Pricing Model Asset Pricing in Cash and Derivative Markets Models of Return and Risk Post-1980 Asset Allocation in the Modern World Product Development: Yesterday, Today, and Tomorrow Notes CHAPTER 2 Measuring Risk What Is Risk? Traditional Approaches to Risk Measurement Classic Sharpe Ratio Other Measures of Risk Assessment Portfolio Risk Measures Other Measures of Portfolio Risk Measurement Value at Risk Notes CHAPTER 3 Alpha and Beta, and the Search for a True Measure of Manager Value What Is Alpha?


pages: 240 words: 60,660

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

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

In contrast, the EMM works much less well for stock valuation, because stock prices suffer risks more diverse and wild than those associated with diffusion. There are many other huge risks—among them, the enthusiasm of crowds that can make entire stock markets rise, the fear that can make them crash, liquidity that can dry up, counterparties that can all fail together in a crisis—that the EMM ignores and that can therefore invalidate many of its results. This is what happened in the great financial crisis. THE CAPITAL ASSET PRICING MODEL The Capital Asset Pricing Model, developed by Jack Treynor, William Sharpe, Jan Mossin, and John Lintner in the early 1960s, is an extension of the EMM that more realistically takes account of the risk not just of single stocks but of the entire stock market. Finance aficionados refer to the model affectionately as CAPM (“Cap Em”). Though economists regard CAPM as the triumph of so-called modern portfolio theory, and though CAPM engendered the Black-Scholes Model, it isn’t nearly as robust, and therefore isn’t nearly as useful.

Dedication Title Page Copyright Model I Chapter 1: A Foolish Consistency MODELS THAT FAILED I: ECONOMICS THEORIES, MODELS, AND INTUITION OF TIME AND DESIRE MODELS THAT FAILED II: POLITICS MODELS THAT FAILED III: THE MOVEMENT A LOOK AHEAD TWO IMPOSSIBLE THINGS BEFORE BREAKFAST Chapter 2: Metaphors, Models, and Theories THE DIRAC SEA ANALYTIC CONTINUATION DIG WE MUST A MODEL AIRPLANE: THE ZIPPY TYPES OF MODELS THE NATURE OF MODELS THE NATURE OF THEORIES MONOCULAR DIPLOPIA MAKING THE UNCONSCIOUS CONSCIOUS AGAIN ADDENDUM: GOETHE ON SYMBOLISM Model II Chapter 3: The Absolute THE TETRAGRAMMATON THE NAME OF THE NAME OF THE NAME THE IRREDUCIBLE NONMETAPHOR A THEORY OF THE EMOTIONS FIAT MONEY LOVE AND DESPERATION HOW TO LIVE IN THE REALM OF THE PASSIONS THE FOUR QUESTIONS SPINOZA’S ANSWERS Chapter 4: The Sublime THE BIRDS OF THE AIR THE PHENOMENA: ELECTRICITY AND MAGNETISM QUALITIES: POSITIVE AND NEGATIVE QUANTITIES: COULOMB’S LAW OF FORCE BETWEEN STATIC CHARGES VOLTA’S ITALIAN INSIGHT: CHEMISTRY IS BETTER THAN FRICTION OERSTED: ELECTRIC CURRENTS BEHAVE LIKE MAGNETS AMPÈRE: A LAW FOR THE FORCE BETWEEN CURRENTS A SYMPATHETIC UNDERSTANDING FARADAY: MOVING MAGNETS CREATE ELECTRIC CURRENTS FARADAY IMAGINES FORCE-TRANSMITTING LINES MAXWELL MODELS THE LINES MAXWELL REIFIES THE LINES MAXWELL MODIFIES AMPÈRE’S EQUATIONS MAXWELL’S THEORY: THE FIELD ITSELF MAXWELL’S EQUATIONS: THE FIELD’S GEOMETRY—CURLS AND DIVERGENCES THE GREAT CONFIRMATION: LIGHT IS THE PROPAGATION OF ELECTROMAGNETIC WAVES REALITY = PERFECTION; FACT = THEORY THE BEASTS OF THE FIELD ELECTROMAGNETISM AS METAPHOR EPILOGUE Model III Chapter 5: The Inadequate FINANCE IS NOT MATHEMATICS PRICE, VALUE, UNCERTAINTY THE EFFICIENT MARKET MODEL UNCERTAINTY VERSUS RISK RISK DEMANDS A POSSIBLE REWARD A MODEL FOR RISK RISK AND RETURN THE ONE LAW OF FINANCE THE CONCLUSION: EXCESS RETURN IS PROPORTIONAL TO RISK AN ASIDE: THE PLEASURE PREMIUM THE EMM AND THE BLACK-SCHOLES MODEL THE CAPITAL ASSET PRICING MODEL THE UNBEARABLE FUTILITY OF MODELING Chapter 6: Breaking The cycle THE PERFECT CAGE THE MYSTERIES OF THE WORLD MODELS THAT FAILED WHAT IS TO BE DONE? THE FINANCIAL MODELERS’ MANIFESTO AN ETHICAL COROLLARY MARKETS AND MORALS TAT TV AM ASI Appendix Acknowledgments Notes Index About the Author ALSO BY EMANUEL DERMAN My Life as a Quant: Reflections on Physics and Finance This edition first published in 2011 Copyright © 2011 by Emanuel Derman Registered office John Wiley & Sons Ltd, The Atrium, Southern Gate, Chichester, West Sussex, PO19 8SQ, United Kingdom For details of our global editorial offices, for customer services and for information about how to apply for permission to reuse the copyright material in this book please see our website at www.wiley.com.

See evil bailouts bare electrons Barfield, Owen Bedazzled (film) Begin, Menachem behavior, human: adequate knowledge and EMM as assumption about explanations for and humans as responsible for their actions and idolatry of models Law of One Price and laws of pragmamorphism and Ben-Gurion, David Bernoulli, Daniel Bernstein, Jeremy beta: CAPM and Betar (Brit Yosef Trumpeldor) binocular diplopia birds Black, Fischer Black-Scholes Model Merton and Blake, William Bnei Akiva (Sons of Akiva) Bnei Zion (Sons of Zion) body-mind relationship Bohr, Aage Bohr, Niels bonds: financial models and See also type of bond Boyle’s Law Brahe, Tycho brain Brave New World (Huxley) Brownian motion bundling of complex products cage: moth in perfect calibration Cape Flats Development Association (South Africa) Capital Asset Pricing Model (CAPM) capitalism caricatures: models as cash. See currency/cash Cauchy, Augustin-Louis causes Cepheid variable stars chalazion chaver (comrade) Chekhov, Anton chemistry: electromagnetic theory and Chesterton, G. K. Chinese choice chromatography Churchill, Winston Coetzee, J. M. coin tossing Coleridge, Samuel Taylor collateralized default obligations (CDOs) collective model, of nucleus Collie, Max commodities communism: in South Africa The Communist Manifesto (Marx and Engels) complex numbers, theory of computer programs consciousness Consolidated Edison stock contempt content: Goethe’s view about method and method and contradictions control: purpose of models and theories and self- and theory of controlling engineering devices Cornell, Joseph Cornford, Francis corporate bonds corporate welfare corporations: bailouts of cosmology Coulomb, Charles creative destruction credit crisis (2007) credit markets curls: electromagnetic theory and currency/cash darkness: Goethe’s view about light and light and Darwin, Charles de Klerk, F.

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

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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 process, 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 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

I found that Richard Grinold was BARRA’s director of research and consulting, and the person to whom to send a letter of application. JWPR007-Lindsey 34 May 7, 2007 16:30 h ow i b e cam e a quant BARRA’s First Rocket Scientist Unknown to me at that time, BARRA was one of the centers of the quant finance revolution. The name stood for Barr Rosenberg and Associates. Barr Rosenberg was a finance professor at Berkeley. More than anyone else, he had taken new academic theories, especially the Capital Asset Pricing Model (CAPM) and modern portfolio theory, and made them useful and accessible to practitioners. Consistent with that effort, BARRA expended considerable effort on educational activities, training clients on new ways of thinking about investing. Just how revolutionary was all this? Rosenberg had appeared on the cover of Institutional Investor magazine in 1978. The cover illustration showed him sitting in a lotus position, in flowing robes, surrounded by much smaller men in suits bowing down to him.

Defining active management as an optimization problem—trading expected return against risk—naturally connects returns to portfolios. So any set of expected returns leads to a unique optimal portfolio, and a portfolio assumed optimal leads to a unique set of expected returns. The benchmark JWPR007-Lindsey May 7, 2007 16:30 Ronald N. Kahn 43 portfolio itself naturally leads to a set of consensus expected returns (because the benchmark is the consensus portfolio). These consensus returns look like a Capital Asset Pricing Model result: They are proportional to covariances with the benchmark. Holding a portfolio that differs from the benchmark—almost the definition of active management—implies a set of expected returns that differ from the consensus. The job of active management is to forecast active returns—returns relative to those consensus returns—and optimally trade off active return against active risk, the volatility of those active returns.

The price limit here is the cash that the user wishes to extract from, or spend on, the trade. As long as these cash limits are at all reasonable, this type of order gives the computer degrees of freedom to enable it to execute trades. As a result, combined value trading makes the most efficient use of whatever liquidity is present at the call. Peter Bossaerts at Caltech tried to validate the Capital Asset Pricing Model (CAPM) using MBA students at Stanford and Yale as traders. The environment met all the CAPM assumptions: All participants had same time horizon and the same knowledge of the securities. The market contained a riskless security, the security return distributions were normal and all the available assets were tradable and held by the investors, and so on. Bossaerts expected the students to trade their portfolios to attain the highest Sharpe ratio, which he had set to be that of the capitalization weighted market.


pages: 444 words: 86,565

Investment Banking: Valuation, Leveraged Buyouts, and Mergers and Acquisitions by Joshua Rosenbaum, Joshua Pearl, Joseph R. Perella

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asset allocation, asset-backed security, bank run, barriers to entry, capital asset pricing model, collateralized debt obligation, corporate governance, credit crunch, discounted cash flows, diversification, fixed income, intangible asset, London Interbank Offered Rate, performance metric, shareholder value, sovereign wealth fund, technology bubble, time value of money, transaction costs, yield curve

Step III(c): Estimate Cost of Equity (re) Cost of equity is the required annual rate of return that a company’s equity investors expect to receive (including dividends). Unlike the cost of debt, which can be deduced from a company’s outstanding maturities, a company’s cost of equity is not readily observable in the market. To calculate the expected return on a company’s equity, the banker typically employs a formula known as the capital asset pricing model (CAPM). Capital Asset Pricing Model CAPM is based on the premise that equity investors need to be compensated for their assumption of systematic risk in the form of a risk premium, or the amount of market return in excess of a stated risk-free rate. Systematic risk is the risk related to the overall market, which is also known as nondiversifiable risk. A company’s level of systematic risk depends on the covariance of its share price with movements in the overall market, as measured by its beta (β) (discussed later in this section).

See compound annual growth rate calendarization of financial data calendar year call date call premium call price call protection call schedule callable bond capex. See capital expenditures capital asset pricing model (CAPM) capital expenditures (capex) . See also growth capex and maintenance capex in coverage ratios in free cash flow in leverage ratios limitations on low requirements, for LBOs projection of Capital IQ capital markets conditions transactions capital structure effects of changes in LBO financing . See also financing structure optimal capital structure target capital structure, for WACC capitalization capitalization ratio CAPM. See capital asset pricing model caps cash and stock transaction cash available for debt repayment cash flow generation cash flow statement in LBO analysis cash flow sweep cash interest expense cash on hand funding source cash return CDO.

See also Contents key pros and cons Competition Bureau competitors compound annual growth rate (CAGR) confidential information memorandum (CIM) sample confidentiality agreement (CA) provisions consensus estimates . See also First Call and IBES consultants contact log contractual subordination control premium conversion price convertible securities corporate finance corporate website cost of capital . See also weighted average cost of capital (WACC) cost of debt cost of equity. See also capital asset pricing model (CAPM) cost of goods sold (COGS) non-recurring items in projection of cost structure coupon covenant-lite loans covenants in definitive agreements incurrence maintenance coverage ratios. See interest coverage credit agreement credit committee credit crunch credit markets credit profile credit ratings ratings scales credit statistics cultural fit current assets current liabilities current report.


pages: 345 words: 87,745

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

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asset allocation, backtesting, Bernie Madoff, capital asset pricing model, cognitive dissonance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Ponzi scheme, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

For every non-market risk winner there must be a non-market risk loser. However, no one invests for free. After fees and expenses, most non-market risk takers (i.e., active fund investors) must underperform the market by the costs they incur. It’s simple arithmetic. During 1964, Sharpe applied beta in his revolutionary Capital Asset Pricing Model (CAPM) for which he was awarded the Nobel Prize in Economic Science. This model defines a company’s estimated cost-of-capital in relation to that company’s specific beta. The Capital Asset Pricing Model remains the backbone of modern price theory for financial markets. It’s applied extensively in valuation models of both public and private enterprises and has become an important tool for business decision making. Jack Treynor Jack Treynor was a mathematics major at Haverford College before graduating from Harvard Business School with distinction in 1955.

See Box Score Report (BSR) BtM. See Beta, firm size, and value (BtM); Book-to-market (BtM) Bucket approach Buffett, Warren Bull market Busse, Jeffrey Buy, hold, and rebalance Buy-and-hold investor Buy-high sell-low mentality Buy side of business Canadian mutual funds Capital Asset Pricing Model (CAPM) Capital gains distributions Capital Ideas (Bernstein) Capital Ideas Evolving (Bernstein) Capitalization weighted benchmarks Capitalization weighted index Capital markets line (CML) CAPM. See Capital Asset Pricing Model (CAPM) Carhart, Mark Cash/cash equivalents Cash flow analysis Category styles Certificate in Investment Performance Measurement (CIPM) CFA Institute/charter Changing investor behavior: helping people go passive investing as serious business naysayers to passive investing procrastinators and uninformed about passive investing Charitable trusts.

More recently, momentum has garnered a lot of attention, although capturing this factor in real time may prove to be difficult due to trading costs. The Early Years in Review The early academics worked hard to create a simple risk-based model for evaluating active manager performance so they could more easily identify skill. These efforts lead to several models that are the backbone of analysis today, including the Capital Asset Pricing Model (CAPM), Sharpe Ratio, Treynor Ratio, and Jensen’s Alpha. These efforts began with Harry Markowitz’s pioneering work on portfolio construction in the 1950s. He asserted that both risk and return were equally important in portfolio decisions. The tactic taken by most researchers subsequently has been to adjust a portfolio by its market risk factor called beta; this will highlight any excess return that could signal manager skill.


pages: 153 words: 12,501

Mathematics for Economics and Finance by Michael Harrison, Patrick Waldron

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Brownian motion, buy low sell high, capital asset pricing model, compound rate of return, discrete time, incomplete markets, law of one price, market clearing, Myron Scholes, Pareto efficiency, risk tolerance, riskless arbitrage, short selling, stochastic process

The frontier is the envelope of all the finite rays through risky portfolios, extending as far as the borrowing constraint allows. 2. with differential borrowing and lending rates: Figure 3F goes here. There is a range of expected returns over which a pure risky strategy provides minimum variance; lower expected returns are achieved by riskless lending; and higher expected returns are achieved by riskless borrowing. 6.5 Market Equilibrium and the Capital Asset Pricing Model 6.5.1 Pricing assets and predicting security returns Need more waffle here about prediction and the difficulties thereof and the properties of equilibrium prices and returns. We are looking for assumptions concerning probability distributions that lead to useful and parsimonious asset pricing models. The CAPM restrictions are the best known. At a very basic level, they can be expressed by saying that every Revised: December 2, 1998 CHAPTER 6.

Since the market portfolio is then on the frontier, it follows that: E[r̃q ] = (1 − βqm )E[r̃zm ] + βqm E[r̃m ] (6.5.7) N X mj r̃j (6.5.8) Cov [r̃q , r̃m ] Var[r̃m ] (6.5.9) where r̃m = j=1 βqm = This implies for any particular security, from the economic assumptions of equilibrium and two fund separation: E[r̃j ] = (1 − βjm )E[r̃zm ] + βjm E[r̃m ] (6.5.10) This relation is the ? Zero-Beta version of the Capital Asset Pricing Model (CAPM). 6.5.4 The traditional CAPM Now we add the risk free asset, which will allow us to determine the tangency portfolio, t, and to talk about Capital Market Line (return v. standard deviation) and the Security Market Line (return v. β). Normally in equilibrium there is zero aggregate supply of the riskfree asset. Recommended reading for this part of the course is ?, ?, ? and ?. Now we can derive the traditional CAPM.


pages: 402 words: 110,972

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

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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

Further innovation came in the form of factor models, notably “Barr’s better betas,” a fundamental multifactor model developed by Barr Rosenberg at Berkeley. The beta that Barr had better versions of was the one in the capital asset pricing model (CAPM). The conventional wisdom in writing a book popularizing a technical topic is that each equation included cuts book sales in half. So with great trepidation, here is a simplified version of main equation used in the CAPM: Wher e Does Alpha Come Fr om? RS Rrf Excess return to a stock (total stock return, minus risk-free rate) m Rm 99 ␧S Return to the broad market Beta, the stock’s sensitivity to the broad market . . . plus the portion of return not explained by beta, (e.g., news) Bill Sharpe shared the Nobel Prize in economics for the capital asset pricing model. This is a simple representation of the key idea that the return to a stock is explained by the return to the broad market (e.g., the S&P 500) times the stock’s sensitivity to the market (beta) plus stock-specific returns (e.g., from news).

See also Rosenberg, Barr Baseline Scenario, 281, 293 Baxter, Frank, 20 BBO, best bid and offer, 70, 79, 87 Beinart,Yossi, xiii, 159 Bernanke, Ben on fractional home ownership, 276, 306 on TARP, 315 on U.S. financial infrastructure, 285 Bernstein, Peter, 38, 130 beta, 92, 113 “Barr’s better beta”, 98–101 Bits, Bucks and BTUs, 337–339 Black, Fischer, xiii, xvi, xxxi, 67, 97 blog, xxxiv, 241 Baseline Scenario, 281 rumors, 204 bluffing, 258–259 Brattle Group, 329, 332 on energy conservation, 340 Buffett, Warren, 126, 163 on derivatives, 291–293 on the efficient markets hypothesis, 96–97 butter in Bangladesh, xxxiii, 137, 139–141. See also data mining capital asset pricing model, 98–99 “Barr’s better beta”, 98–101 Bill Sharpe, 38 CAPM. See capital asset pricing model CDO. See collateralized debt obligation CDS. See credit default swaps Center for Innovative Financial Technology, 311 CERN, 37, 104 CFTC. See Commodity Futures Trading Commission Chicago Mercantile Exchange, 6–9, 72, 286 Chriss, Neil, 76–77 chromosome, 155, 184–186, 192–193 CI. See collective intelligence CIFT. See Center for Innovative Financial Technology CME.

RDF does for relationships between tagged data elements what the XML tagging itself did for moving from format HTML tags like “Bold” to meaningful XML tags like “Price.” 38 Nerds on Wall Str eet Hits and Misses: Rational and Irrational Technology Exuberance Peter Bernstein’s book Capital Ideas (Free Press, 1993) tells the story of Bill Sharpe, who wandered Wall Street looking for enough computer time to run a simple capital asset pricing model (CAPM) portfolio optimization, while being regarded as something of a crackpot for doing so. Now these computations are routine, though not without problems arising from sensitivity to errors and noise. (These problems, in turn, are being addressed by still more computation, using resampling and other computer-intensive methods.) Yesterday’s crackpot, of course, has become today’s visionary Nobel laureate, as proven by the sheer rise in calculating power.


pages: 369 words: 128,349

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

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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

Since abnormal return is the actual return minus the normal return, a problem arises in defining the normal return (the term is used interchangeably with expected return). How do you define or measure normal return? In the IBM example, it was assumed that the normal return is 15 percent. Is the 15 percent assumption correct? Who can say? Unfortunately, there is no accepted method for estimating a stock’s normal return. Theoretical models include Nobel laureate William Sharpe’s capital asset pricing model (CAPM) and Steve Ross’s arbitrage pricing theory (APT). APT cannot be applied in a practical way because there are too many unknowns. CAPM is determinis- Market Efficiency and Anomalies tic, but the CAPM does not have much empirical support. In the words of Eugene Fama, “[I]nferences about market efficiency can be sensitive to the assumed model for expected returns” (Fama 1998, 288).

Sarkar, Asani, and Kai Li. 2002. Should U.S. Investors Hold Foreign Stocks? Federal Reserve Bank of New York’s Current Issues in Economics and Finance 8(3), 1–6. Wignall, Christian. 1994. Does International Investing Still Make Sense? Yes, and Here’s Why. Journal of Investing 3(4), 12–17. 257 258 Beyond the Random Walk Explanations of the Home Bias Coen, Alain. 2001. Home Bias and International Capital Asset Pricing Model with Human Capital. Journal of Multinational Financial Management 11(4–5), 497–513. Coval, Joshua D., and Tobias J. Moskowitz. 1999. Home Bias at Home: Local Equity Preference in Domestic Portfolios. Journal of Finance 54(6), 2045–73. Goetzmann, William N., and Alok Kumar. 2001. Equity Portfolio Diversification. NBER working paper no. 8686. Hasan, Iftekhar, and Yusif Simaan. 2000. A Rational Explanation for Home Country Bias.

Investors trade only in the securities they know about. In effect, in this model, capital markets are segmented, and the assumption that investors can use only the securities they know about in constructing their portfolios results in their being incompletely diversified. It follows that the equilibrium return demanded by less than fully diversified investors will be higher than that demanded in the full-information capital asset pricing model (CAPM).4 As more investors become aware of the stock, the extent of incomplete diversification falls and the price rises. The investor awareness model can explain the empirical evidence with respect to neglected stocks, glamour stocks, stocks featured in the media, and S&P 500 index changes (see Chapter 8). A structural uncertainty model with rational investors can also explain underreaction and overreaction.


pages: 363 words: 28,546

Portfolio Design: A Modern Approach to Asset Allocation by R. Marston

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asset allocation, Bretton Woods, capital asset pricing model, capital controls, carried interest, commodity trading advisor, correlation coefficient, diversification, diversified portfolio, equity premium, Eugene Fama: efficient market hypothesis, family office, financial innovation, fixed income, German hyperinflation, high net worth, hiring and firing, housing crisis, income per capita, index fund, inventory management, Long Term Capital Management, mortgage debt, passive investing, purchasing power parity, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, Silicon Valley, superstar cities, survivorship bias, transaction costs, Vanguard fund

., 133–140 returns, 27–30, 131–133 stocks versus, 24–25 treasury, 122–127 Brady Bonds, 114 Brady, Nicholas, 114 Brazil, 99–100 Bretton Woods, 74 BRIC countries, 99 bull market, 30 buyout funds, 192–195, 205–207 returns, 207–211 B backfill bias, 178, 180 backwardation, normal, 240 Baker, James, 81 Bank for International Settlements, 133–134 Banz, Rolf W., 49 bequests, effects of, 311–313 beta, 17 biases, hedge funds, 178–181 Black, Fischer, 154–155 C Cambridge Associates, 199 Campbell, John Y., 64 capital asset pricing model (CAPM), 41 capital gains, 36 bonds, 12–13 currency, 79–85 CAPM. See capital asset pricing model carry fee, 193 Case-Shiller index, 226–227 331 P1: a/b ind P2: c/d QC: e/f JWBT412-Marston T1: g January 6, 2011 332 Case, Karl E., 223 Center for International Securities and Derivatives (CISDM), 173, 188 Center for Research in Security Prices (CRSP), 50, 65 Central Bank of Thailand, 118 China, 99–100 CISDM. See Center for International Securities and Derivatives Clements, Jonathan, 213 CMO.

In the late 1970s, several researchers built a case that there is a smallcap premium even when small caps are adjusted for their greater risk. Banz (1981) was the first author to document the relationship between the size of a firm and its return. According to Banz, not only are returns on smallcap stocks higher than those on large-cap stocks, but there is an abnormal excess return when measured against the capital asset pricing model (CAPM) security market line. In a series of widely-cited studies, Fama and French show that the size effect (together with the book value effect to be discussed in the next chapter) is important in explaining stock market returns.2 Researchers established that small-cap stocks had another intriguing feature. Most of the small-cap premium occurs in one month, January. Keim (1983) was among the first studies to document this anomaly.3 Using daily data for the period from 1969 to 1979, Keim showed that more than 50 percent of any small-cap premium is due to January returns and that 50 percent of this January premium is achieved in the first week of trading.

An easier alternative is for the large-cap manager to be benchmarked against the S&P 500 and the small/mid-cap manager to be benchmark against the Russell 2500.10 The Sharpe ratio provides a comparison between small-cap and largecap stocks that adjusts for the total risk of each index, both systematic and unsystematic risk. Table 3.5 adjusts the risk of each index for systematic risk alone using the capital asset pricing model. This table reports the betas of each index measured relative to the Russell 3000 all-cap index. Both the Russell 1000 and 2000 indexes have near-zero alphas relative to the Russell 3000. This isn’t surprising in the case of the Russell 1000 index because it represents 92 percent of the Russell 3000 index. But it is surprising that the Russell 2000 has no positive alpha. That is, the Russell 2000 index provides no small-cap premium once it is adjusted for systematic risk.


pages: 478 words: 126,416

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

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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, 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, 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 carrier, M-Pesa, market design, millennium bug, mittelstand, money market fund, moral hazard, mortgage debt, Myron Scholes, 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, Richard Feynman, 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

The models that have been developed in financial economics are wide-ranging, and often technically ingenious. They include the Markowitz model of portfolio allocation (to which Greenspan referred) and the Black–Scholes model (the derivative pricing model to which he alluded). The key components of academic financial theory, however, are the ‘efficient market hypothesis’ (EMH), for which Eugene Fama won the Nobel Prize in 2013, and the Capital Asset Pricing Model (CAPM), for which William Sharpe won the Nobel Prize in 1990. Sharpe shared that prize with Markowitz, and Myron Scholes received a Nobel Prize in 1997, just before the famous blow-up of Long-Term Capital Management, in which Scholes was a partner; Black had died in 1995. All of these financial economists have affiliations to the University of Chicago. EMH asserts that all available information about securities is ‘in the price’.

The wise investor will think twice before rejecting the efficient market hypothesis. Yet the volume of trading we observe in securities markets today would be wholly inexplicable if the hypothesis that all information relevant to security valuation is already in the price were true. There is a logical contradiction at the heart of EMH. If all information were already in the price, what incentive would there be to gather such information in the first place? The capital asset pricing model takes the logic of EMH a stage further. The CAPM describes the equilibrium of an efficient market populated by rational agents each holding similar expectations. The financial journalist Justin Fox recounts the birth of the CAPM: its originator, Bill Sharpe, recognised the implausibility of the scenario he postulated, and his article was initially rejected for publication on precisely those grounds – the model assumptions were unduly fanciful.28 Yet within a short time the CAPM was treated as descriptive of real markets.

A computer and a large dataset are not enough: you need local knowledge, and an understanding of the economic processes at work. What you learn in the local branch, or at the nineteenth hole, may be as useful as the ability to solve difficult mathematical problems. The distinction between general economic risks that affect all firms and households (interest rates and the state of the housing market) and problems that are specific to individuals (divorce and illness) is central to the capital asset pricing model, that keystone of financial economics. Business risks are partly attributable to the specifics of a particular business and partly related to the prosperity of the general economy. The CAPM describes them as specific risk and market risk respectively. Specific risk arises when a badly managed business loses share to its competitors, or a major project suffers from cost overruns. A well-diversified portfolio will accumulate a variety of specific risks.

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

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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

CTA Performance Evaluation with Data Envelopment Analysis 83 While CTAs follow absolute return strategies that seek to make positive returns in all market conditions, benchmarks now exist for CTAs and other hedge fund strategies. Before considering benchmarks for absolute return strategies, we first review the concepts in the context of traditional asset management. Jensen’s (1968) alpha is generally a capital asset pricing model (CAPM)-based performance measure of an asset’s average return in excess of that predicted by the CAPM, given its systematic risk (beta)5 and the market (benchmark) return. Alphas also may be measured relative to additional sources of risk in multi-index models. Whereas various single-index models are based on the CAPM and assume that security returns are a function of their co-movements6 with the market portfolio, multi-index (or multifactor) models assume that returns are also a function of additional influences.7 For example, Chen, Roll, and Ross (1986) develop a model where returns are a function of factors related to cash flows and discount rates such a gross national product and inflation.

Schneeweis and Spurgin (1998) use various published indexes (Goldman Sachs Commodity Index, the Standard & Poor’s 500 stock index, the 5Within the Markowitz (1952) framework, total risk is quantified by the standard deviation of returns. Tobin (1958) extended the Markowitz efficient frontier by adding the risk-free asset, resulting in the capital market line (CML) and paving the way for the development of the capital asset pricing model, developed by Sharpe (1964), Lintner (1965), and Mossin (1966). The CAPM defines systematic risk, measured by beta (b), as the relevant portion of total risk since investors can diversify away the remaining portion. 6Usually CAPM-based performance models describe covariance with the market portfolio, however, as noted earlier, they can attempt to describe coskewness and cokurtosis as well. 7Arbitrage pricing theory (APT) establishes the conditions under which a multiindex model can be an equilibrium description (Ross, 1976). 84 PERFORMANCE Salomon Brothers government bond index, and U.S. dollar trade-weighted currency index, the MLM Index8) with absolute S&P 500 returns and intramonth S&P return volatility in a multifactor regression analysis to describe the sources of return to hedge funds, managed futures, and mutual funds.

In the next section, we examine how this kinked relationship can be quantified. 15.00% 10.00% 5.00% 0.00% –5.00% –10.00% –20.00% –15.00% –10.00% –5.00% 0.00% 5.00% 10.00% 15.00% S&P 100 Excess Returns Diversified Trading Regression Line FIGURE 9.2 Barclay Diversified Trading Index Systematic Excess Returns 188 RISK AND MANAGED FUTURES INVESTING 0.200 0.150 0.100 0.050 0.000 –0.050 –0.100 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns Systematic Trading Regression Line FIGURE 9.3 Barclay Systematic Trading Index MLMI Excess Returns 0.060 0.040 0.020 0.000 –0.020 –0.040 –0.060 –0.080 –0.175 –0.150 –0.125 –0.100 –0.075 –0.050 –0.025 0.000 0.025 0.050 0.075 0.100 0.125 S&P 100 Excess Returns MLM Index Regression Line FIGURE 9.4 MLM Index Measuring the Long Volatility Strategies of Managed Futures 189 FITTING THE REGRESSION LINE The previous discussion provides a general framework in which to describe empirically the long volatility exposure embedded within CTA trendfollowing strategies. To fit the kinked regression demonstrated in Figures 9.1 through 9.4, we use a piecewise linear capital asset pricing model (CAPM)–type model. The model can be described as: Rtf − Rf = (1 − D)[a low + b low(ROEX − Rf)] + D[a high + b high(ROEX − Rf)] (9.1) where Rtf = return to the trend-following strategy Rf = risk-free rate ROEX = return to the S&P 100 a low, b low= regression coefficients to the left-hand side of the kink a high, b high= regression coefficients to the right-hand side of the kink D = 1 if ROEX − Rf > the threshold D = 0 if ROEX − Rf < or equal to the threshold.


pages: 500 words: 145,005

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

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

So the possibility of stocks going bankrupt was not the hidden source of risk that could explain our results. Still, those Loser stocks certainly did look risky. And might not scary-looking stocks, such as those whose prices had plummeted, have to earn a higher rate of return (a “risk premium”) in the market? You might think so, but such thinking was not kosher in modern financial economics. At that time, the right and proper way to measure the risk of a stock was to use the capital asset pricing model (CAPM) developed independently by financial economists John Lintner and William Sharpe. According to the CAPM, the only risk that gets rewarded in a rational world is the degree to which a stock’s return is correlated with the rest of the market. If you form a portfolio composed of a bunch of highly risky stocks whose prices bounce around a lot, the portfolio itself will not be especially risky if the price movements of each of the component stocks are independent of one another, because then the movements will on average cancel out.

Instead, they proposed what is now known as the Fama–French Three Factor Model, in which, in addition to the traditional beta, two extra explanatory factors were added to rationalize the anomalous high returns to small companies and value stocks. Fama and French showed that the returns on value stocks are correlated, meaning that a value stock will tend to do well when other value stocks are doing well, and that the same is true for small-cap stocks. But Fama and French were forthright in conceding that they did not have any theory to explain why size and value should be risk factors. Unlike the capital asset pricing model, which was intended to be a normative theory of asset prices based on rational behavior by investors, there was no theoretical reason to believe that size and value should predict returns. Those factors were used because empirical research had shown them to matter. To this day, there is no evidence that a portfolio of small firms or value firms is observably riskier than a portfolio of large growth stocks.

But behavioral approaches are taken seriously, and on many issues the debate between the rational and behavioral camps has dominated the literature in financial economics for over two decades. The linchpin for keeping this debate grounded and (mostly) productive is its focus on data. As Gene Fama often says when he is asked about our competing views: we agree about the facts, we just disagree about the interpretation. The facts are that the capital asset pricing model has clearly been rejected as an adequate description of the movements of stock prices. Beta, the only factor that was once thought to matter, does not appear to explain very much. And a pile of other factors that were once supposedly irrelevant are now thought to matter a great deal, although the question of why exactly they matter remains controversial. The field appears to be converging on what I would call “evidence-based economics.”

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

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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

For example, a beta of 1.3 means that a 1% rise or fall in the market on average results in a 1.3% rise or fall in the security or fund in question. High-beta stocks and funds are risky. A low-beta stock or fund may be less risky, but it may also be highly risky with a low correlation with the market. See capital asset pricing model. Bid price: A broker’s price to buy a stock or bond. Bond: Debt issued by a corporation or governmental entity. Carries a coupon, or the amount of interest it yields. Bonds are usually of greater than one-year maturity. (Treasury securities of 1–10 years’ maturity are called notes.) Book value: A company’s assets minus intangible assets and liabilities; very roughly speaking, a company’s net assets. Capital asset pricing model (CAPM): A theory relating risk and expected return. Basically, it states that the return of a security or portfolio is equal to the risk-free rate plus a risk premium defined by Glossary 189 its beta.

Reinvestment risk: The risk that future bond interest will have to be reinvested at a lower interest rate. Return: The change in the value of a portfolio over a given period, including dividends and other distributions. Riskless rate: The return earned on a riskless asset, usually a 30- or 90-day Treasury bill. This is the base return that all investors can be expected to earn. According to modern portfolio theory and the capital asset pricing model, return in excess of the riskless rate (also known as the risk premium) can only be obtained by bearing market risk. Risky asset: Any asset exposed to market risk. R squared (R2 ): The square of the correlation coefficient. It defines the amount of a returns series which can be explained by an index or factor. For example, a mutual fund with a .80 R2 relative to the S&P 500 has 80% of its returns explained by this index.


pages: 741 words: 179,454

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

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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, 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, 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, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, labour market flexibility, 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, 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 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

., 44, 341, 346 2001 tax cuts, 298 business schools, 308-313 bonuses, 317-318 compensation, 313-320 BusinessWeek, 170 buy and flick, 139 Byrd, Richard Evelyn, 256 Byrne, David, 46 Byrne, Rhonda, 45 C Caesar, 295 calculators, 122 call options, 209 Volkswagen (VW), 257 Callan, Erin, 288, 329 Calomiris, Charles, 273 Canadian dollars, 21 Canary Wharf, 79 Cantor, Eddie, 338 capital definition of, 280 flows, 205 gains, 160 injections into banks, 348-350 introductions, 247 leveraged, 244 Modigliani-Miller propositions, 119 structure arbitrage, 242 velocity of, 69 capital asset pricing model (CAPM), 117 capitalism, 102 Capitalism: A Love Story, 165 Capitalism: The Unknown Ideal, 297 CAPM (capital asset pricing model), 173 Capra, Frank, 65 carceral continuum, 312 careers certifications, 309-310 finance, 308-313 bonuses, 317-318 compensation, 313-320 Carlyle Group, The, 154, 163, 318 Carlyle, Thomas, 102 Carnegie Mellon University, 119 Carr, Fred, 145 Carroll, Lewis, 31 CARS (certificate for automobile receivables), 173 Carter, Jimmy, 74, 364 Caruso-Cabrera, Michelle, 95 Casablanca, 77, 311 Case, Steve, 58 cash flow, 138 forecasting, 160 General Electric (GE), 61 cash for clunkers, 348 Cassano, Joseph, 232 Cat’s Cradle, 339 catastrophe risk, 232 Catillo, Bernal Díaz del, 131 Cavendish Laboratory (Cambridge, England), 101 Cayman Islands, 220 Cayne, James, 318 CBOs (collateralized bond obligations), 173 CDOs (collateralized debt obligations), 173, 176 defaults of, 284 celebrity central bankers, age of, 297-300 celebrity financiers, 324-326 Celtic tiger, 83.

The physicist Paul Dirac observed that: “In physics, we try to tell people in such a way that they understand something that nobody knew before. In the case of poetry, it’s the exact opposite.”6 Economics, as practiced at Chicago, with its mix of dogma, political fundamentalism, and mathematics, was neither poetry nor physics. Theories—rational expectations, real business cycle theory, portfolio theory, efficient market hypothesis, capital structure theory, capital asset pricing models, option pricing, agency theory—rolled off the academic production line. Many economists received recognition in the form of the Nobel prize in Economics (technically the “Severige Riksbank [Swedish Central Bank] Prize in Economic Sciences in Memory of Alfred Nobel” founded in 1968). Some historians assert that every recent economics Nobel prize winner was either from the University of Chicago, was at Chicago at the time of doing their prize-winning work, had at some time visited the city or had simply inhaled the campus air—especially bottled and sent to them.

Markowitz was restating Antonio, in William Shakespeare’s Merchant of Venice: “My ventures are not in one bottom trusted, nor to one place; nor is my whole estate upon the fortune of this present year; therefore my merchandise makes me not sad.” Despite Friedman’s grumbling, Markowitz received his Ph.D. Building on Markowitz’s work, in the 1960s, Jack Treynor, William Sharpe, John Lintner, and Jan Mossin developed the capital asset pricing model (CAPM). The CAPM calculated a theoretically appropriate required rate of return for assets, such as an individual security or a portfolio. Where an asset is added to a well-diversified portfolio, the additional return required is related to the risk unique to that security, which cannot be diversified away. The CAPM is one of modern finance’s iconic equations: E[Ri] = Rf + Beta [E[Rm] – Rf] where: E[Ri] is the expected return on the asset.


pages: 1,088 words: 228,743

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

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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, 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, 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, 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, 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

Index AAA/AA/A-rated bonds absolute valuation academic investors active investing active risk puzzle (Litterman) active strategies adaptive markets hypothesis (Lo) advisors, CTAs agriculture alpha—beta barbell alpha—beta separation alphas CAPM currency carry hedge funds long horizon investors portable alpha alternative assets assets list commodities hedge funds liquidity momentum strategies PE funds premia real estate risk factors alternative betas AM see arithmetic mean ambiguity aversion Amihud, Yakov announcement days arbitrage behavioral finance CRP front-end trading equity value strategies term structure models Argentina arithmetic mean (AM) art investing asset classes 1990—2009 alternative assets “bad times” performance currency carry derivatives foreign exchange forward-looking indicators growth sensitivities historical returns inflation long history momentum strategies performance 1990—2009 profitable strategies risk factors style diversification traditional trend following understanding returns value strategies volatility selling world wealth assets 1968—2007 asset richening AUM Berk—Green management model cyclical variation empirical “horse races” ERPC feedback loops forward-looking measures growth illiquidity liquidity long-horizon investors market relations multiple asset classes prices/pricing privately held real assets risky assets seasonal regularities survey-based returns tactical forecasting tail risks time-varying illiquidity premia volatility see also asset classes assets under management (AUM) asymmetric information asymmetric returns asymmetric risk at-the-money (ATM) options seasonal regularities tail risks volatility selling attention bias AUM see assets under management BAB see betting against beta backfill bias backwardation “bad times” carry strategies catastrophes crashes crises inflation rare disasters bank credibility Bank of England Barcap Index BBB-rated bonds behavioral finance applications arbitrage biases cross-sectional trading heuristics historical aspects macro-inefficiencies micro-inefficiencies momentum over/underreaction preferences prospect theory psychology rational learning reversal effects speculative bubbles value stocks BEI see break-even inflation benchmarks, view-based expected returns Berk—Green asset management model Bernstein, Peter betas alpha—beta barbell BAB currency carry equity hedge funds long-horizon investors risk time-varying betting against beta (BAB) biases attention behavioral finance confirmation conservatism currency carry downgrading extrapolation forward rate hedge funds heuristic simplifications high equity premium hindsight historical returns learning limits memory momentum overconfidence overfitting overoptimism reporting representativeness reversal tendencies self-attribution self-deception survey data terminology volatility selling binary timing model Black—Litterman optimizers Black—Scholes (BS) option-pricing formula Black—Scholes—Merton (BSM) world blind men and elephant poem (Saxe) bond risk premium (BRP) approximate identities bond yield business cycles covariance risk cyclical factors decomposed-year Treasury yield drivers ex ante measures historical returns inflation interpreting BRP IRP macro-finance models nominal bonds realized/excess return safe haven premium supply—demand survey-based returns tactical forecasting targets terminology theories YC bonds AAA/AA/A-rated balanced portfolios BBB-rated credit spreads ERPB government historical records HY bonds IG bonds inflation-linked long-term nominal non-government relative valuation stock—bond correlation top-rated yields see also bond risk premium; corporate bonds booms break-even inflation (BEI) Bretton Woods system BRIC countries BRP see bond risk premium BSM see Black—Scholes—Merton bubbles absolute valuation memory bias money illusion real estate Shiller’s four elements speculative Buffet, Warren building block approach business cycles asset returns economic regime analysis ex ante indicators realized returns buybacks B-S see Black—Scholes option-pricing formula C-P BRP see Cochrane—Piazzesi BRP forward rate curve calls seasonal regularities tail risks volatility selling Campbell, John Campbell—Cochrane habit formation model Capital Asset Pricing Model (CAPM) alphas carry strategies Consumption CAPM covariance with “bad times” disagreement models ERP Intertemporal CAPM liquidity-adjusted market frictions market price equation multiple risk factors risk factors risk-adjusted returns risk-based models skewness stock—bond correlation supply—demand volatility Capital Ideas (Bernstein) capitalism capitalization (cap) rate CAPM see Capital Asset Pricing Model carry strategies 1990—2009 active investing asset classes business cycles credit carry currency ERP financing rates foreign exchange forward-looking indicators forward-looking measures generic proxy role historical returns long-horizon investors non-zero yield spreads real asset investing roll Sharpe ratios 2008 slide tactical forecasting cash, ERPC cash flow catastrophes see also “bad times” CAY see consumption/wealth ratio CCW see covered call writing CDOs see collateralized debt obligations CDSs see credit default swaps central banks Chen three-factor stock returns model China Citi (Il—)Liquidity indices Cochrane—Piazzesi BRP (C-P BRP) forward rate curve see also Campbell—Cochrane collateral return collateralized debt obligations (CDOs) comfortable approaches commodities characteristics equity value strategies excess returns expected returns expected risk premia futures historical returns inflation momentum return decomposition returns 1984—2009 supply—demand seasonals term structure trading advisors value indicators commodity momentum performance rational stories simple strategies trend following tweaks when it works well why it works see also momentum strategies commodity trading advisors (CTAs) composite ranking cross-asset selection models compound returns conditioners confirmation bias conservatism constant expected returns constant relative risk aversion (CRRA) Consumption CAPM consumption/wealth ratio (CAY) contemporaneous correlation contrarian strategies blunders feedback loops forward indication approach see also reversal convenience yield corporate bonds credit spreads CRP forward-looking indicators front-end trading IG bonds liquidity sample-specific valuation tactical forecasting correlation asset returns correlation premium correlation risk default correlations equities implied risk factors tail risks costs control currency carry enhancing returns taxes trading costs country-specific vulnerability indices covariance with “bad times” covariance risk risk factors covered call writing (CCW) crashes markets see also “bad times” credit default swaps (CDSs) credit-pricing models credit risk credit risk premium (CRP) analytical models attractive opportunities business cycles credit default swaps credit spreads decomposing credit spread default correlations emerging markets debt front-end trading historical excess returns IG bonds low ex post premia mortgage-backed securities non-government debt portfolio risk reduced-form credit-pricing models reward—risk single-name risk swap—Treasury spreads tactical forecasting terminology theory credit spreads AAA/AA/A-rated bonds BBB-rated bonds business cycles CRP cyclical effects decomposition empirical “horse races” forward-looking indicators high-yield bonds rolling yield top-rated bonds volatility yield-level dependence credit and tactical forecasting creditworthiness crises 2007—2008 crisis currency carry liquidity money markets see also “bad times” cross-asset selection forecasting models cross-sectional market relations cross-sectional trading CRP see credit risk premium CRRA see constant relative risk aversion CTAs see commodity trading advisors currency base of returns carry empirical “horse races” equity value strategies inflation see also foreign exchange currency carry baseline variants combining carry conditioners costs diversification emerging markets ex ante opportunity financial crashes foreign exchange historical returns hyperinflation indicators interpreting evidence maturities pairwise carry trading portfolio construction ranking models regime indicators seasonals selection biases strategy improvements “timing” the strategy trading horizons unwind episodes why strategies work cyclical effects credit spreads growth seasonal regularities see also business cycles D/P see dividend yield data mining see also overfitting; selection bias data sources of time series data series construction day-of-the-week effect DDM see dividend discount model debt supercycle default correlations, CDOs default rates, HY bonds deflation delta hedging demand see supply—demand demographics derivatives Dimson, Elroy direct hedge funds disagreement models discount rates discounted cash flows discretionary managers disinflation disposition effect distress diversification currency carry drawdown control long-horizon investors return risk factors style diversification return (DR) dividend discount model (DDM) equities ERP forward-looking indicators growth rate debates dividend growth dividend yield (D/P) DJCS HF index dollars base of returns cost averaging currency carry foreign exchange downgrading bias downside beta DR see diversification return drawdown control duration risk duration timing dynamic strategies equity value strategies portfolio construction risk factors E/P see earnings/price ratio earnings E/P ratio EPS equity returns forecasts growth rates yield see also earnings/price ratio earnings-per-share (EPS) earnings/price (E/P) ratio absolute valuation drivers forward-looking indicators measures choices relative valuation value measures economic growth see also growth efficiency behavioral finance macro-inefficiencies market inefficiency micro-inefficiencies efficient markets hypothesis (EMH) elephant and blind men poem (Saxe) EMBI indices emerging markets carry strategies currency carry debt equity returns future trends growth EMH see efficient markets hypothesis empirical multi-factor finance models endogenous return and risk feedback loops market timing research endowments energy sector commodity momentum trend following volatility selling enhancing returns costs horizon investors risk management skill EPS see earnings per share equilibrium accounting equilibrium model equities 1990—2009 business cycles carry strategies correlation premium empirical “horse races” forward-looking indicators inflation long history momentum sample-specific valuation tactical forecasting ten-year rolling averages value strategies see also stock . . .

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

In market equilibrium, an asset’s expected return equals the required return that rational investors together demand. Risk-averse investors do not use the riskless rate for discounting, unless the cash flow being discounted is itself riskless; the discount rate also reflects the required compensation for the riskiness of an asset’s expected future cash flows. This compensation in turn reflects both the amount of risk and the intensity of investor aversion toward risk. • According to the Capital Asset Pricing Model (CAPM), an asset’s amount of risk is fully captured by its (equity) market beta, while general investor risk aversion determines the size of the market risk premium. Each asset’s expected return in excess of a common riskless rate equals the product of the asset’s beta (sensitivity to market movements) and the common market risk premium. The difference in expected returns across assets reflects only differences in the betas of the assets.


pages: 586 words: 159,901

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

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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

EM theory turns in part on what the definition of the "right" price of a stock or other financial asset should be, since in an efficient world, the market price should be more or less identical to the right one. How, then, should a stock be priced?^" The preferred model that grew up alongside the EMH holds that the key to pricing any asset is its riskiness. That has a common-sense appeal, but the professors of finance have a special view of risk. In the Sharpe-Lintner-Black model (SLB, named after William Sharpe, John Lintner, and Fisher Black), also known as the capital asset pricing model (CAPM, pronounced cap-em), risk is defined as the volatility of an asset's expected returns — its variance around a norm — ex- MARKET MODELS pressed in comparison to some benchmark, usually a "riskless" investment like Treasury' bills. Risk, then, is not the possibility of loss in the colloquial sense, but the likelihood of deviation from an expected return. A security's characteristic risk is called its beta (or P, depending on your typographical preferences).

"Drop in Returns Is Expected for Venture-Capital Firms," Wall Street Journal. November 19, p. B2. Menchik, Paul L., and Martin David (1983). "Income Distribution, Lifetime Savings, and Bequests," American Economic Reviewli, pp. 672-690. Mendoza, Roberto G. (1989). "Treasurer's Conference: The Changing International Banking Environment," mimeo, J.P. Morgan, January 30. Merton, Robert C. (1973). 'An Intertemporal Capital Asset Pricing Model," Econometrica 41, pp. 867-887. — (1992). "Options," in Newman et al. (1992). Meulendyke, Ann-Marie (1989). U.S. Monetary Policy and Financial Markets (New York: Federal Reserve Bank of New York). Mfume. Kweisi (1993)- Statement before the Committee on Banking, Finance, and Urban Affairs, U.S. House of Representatives, October 7. Michie, R.C. (1992). "Development of Stock Markets," in Newman et al. (1992).

., 227-228 bubbles, 178 Buffett, Warren, 271 bulge bracket, 82 Bundesbank, 307 Bush, George, 90 Bush, Neil, 89 Business Conditions Digest, 136 business cycles changes in, 94 "disappearance" of, 136 duration, 54 financial markets and, 121-123 1990-92 vs. eariier cycles, 158-161 stock markets and, 148 buybacks, stock, 72, 74 California Public Employees Retirement System (Calpers), 289-290, 293 calls, 30 campaign contributions, 98 Cantor, Richard, 152, 153 capacity utilization, 124 capital cost of, 298 difficulty measuring, 140 as factor of production, 237 fictitious, 13 as imaginary (Ross), 237 vs. revenue (Marx), 245 capital asset pricing model, 162-163 flaws in, 167-169 political aspects, 185 capital controls, 315 IMF on, 107 capital expenditures, 72 explaining, 148 and financial structure, 153-155 internal finance, 3 and q ratio, 145-148 see also q ratio capital flows. See Globalization capital, human, firm-specific, 248 capital markets and formation of rentier class, 238 importance of railroads to, 188 prefer M&A to internal growth, 281 and real economy, 121-126 social effects, 8 capital structure and investment, 153-155, 174 leveraged buyouts, 298 pecking order, 149 transactions-cost analysis, 249 see also Modigliani-Miller theorem capitalism abolition within capitalism Berle and Means on, 255 Marx on, 240 petty, American naturalization of, 251 as system, 245 capitalist triumphalism, 187 capitalization of income streams, 13, 22 buildings as capitalized rents, 80 capitalization of misery, by World Bank, 49-50 caps, 36 Card, David, 141 Case Shiller Weiss, 186 Caves, Richard, 280 central banks, 23 and class struggle, 219, 308 control of short-term interest rates, 25-26 do economics better than academics, 137 history, 116 "independence", 307-308 Keynes's criticism of 1920s tightness, 196 market pressures on, 308-309 see also Federal Reserve centralization, futures/options markets, 32-33 CEO salaries, 239 Champion International, 288 chartists, 105 Chase group, 262 Chemical Bank, 262 Chernobyl, 54 Chicago, early futures markets, 29 Chicago Board of Trade, 32 Chicago Board Options Exchange, 33 Chile, 201 pension privatization, 304-305 Chinese paper, 282 Chinese wall, 99 Chrysler, 286 churches, 288 Ciancanelli, Penny, 235 Citibank, 296-297, 314 class struggle and bond markets, 122-123 and central banking, 219, 308 indebtedness and, 232 Jensen on armed suppression, 276 leveraged buyouts as, 274 Clayton & Dubalier, 271 Clearing House Interbank Payment System, 45 clearinghouses, futures and options, 32-33 Cleaver, Harry, 320 Clinton, Bill, 104 acceptance of slow growth, 134 Clinton, Hillary, 314 clustering, 182 Coase, Ronald H., 249-251, 297 Colby, William, 104 Colgate-Palmolive, 113 College Retirement Equities Fund (CREF), 289 Collier, Sophia, 310 Columbia Savings, 88 commercial banks, 81-84 commodity prices, futures markets and, 33; see also futures markets common stock, 12 Community Capital Bank, 311 community development banks, 311-314 community development organizations, co-optation of, 315 community land trusts, 314-315 compensatory borrowing, 65 competition managerialist view of, 260 return of, 1970s, 260 Comstock, Lyndon, 311 Conant, Charles, 94-95 Conference Board, 136, 291 consciousness credit and, 236-237 rentier, profit with passage of time, 238 consensus, 133 consumer credit, 64-66, 77 in a Marxian light, 234 in 1930s depression, 156-157 rare in Keynes's day, 242 see also households Consumer Expenditure Survey, 70 consumption, 189 contracts, 249; see afao transactions-cost economics control. 5ee corporations, governance cooperatives, 321 managers hired by workers, 239 weaknesses of ownership structure, 88 corporate control, market for, 277-282 Manne on, 278 corporations debt distribution of, 1980s, 159 and early 1990s slump, 158-161 development, and stock market, 14 emergence, and Federal Reserve, 92-96 emergence of complex ownership, 188 evolution, 253 form as presaging worker control (Marx), 239-240 importance of railroads in emergence, 188 localist critique of, 241 managers' concern for stock price, 171 multinational evolution, and financial markets, 112-113 investment clusters, 111-112 nonfinancial, 72-76 finances (table), 75 financial interests, 262 refinancing in early 1990s, l6l role in economic analysis, 248 shareholders conribute nothing or less, 238 soulful, 258, 263; see afeo social investing stock markets' role in constitution of, 254 transforming, 320-321 virtues of size, 282 corporations, governance, 246-294 Baran and Sweezy on, 258 Berle and Means on, 252-258 abuse of owners by managers, 254 interest-group model, 257-258 Berle on collective capitalism, 253-254 boaids of directors, 27-29, 246, 257, 259, 263, 272 financial representatives on, 265 keiretsu, 275 of a "Morganized" firm, 264 rentier agenda, 290 structure, 299 competition's obsolescence/return, 260 debt and equity, differences, 247 EM theory and Jensenism as unified field theory, 276 financial control 359 WALL STREET meaning, 264 theories of, rebirth in 1970s, 260-263 financial interests asserted in crisis, 265 financial upsurge since 1980s, 263-265 Fitch/Oppenheimer controversy, 261-262 Galbraith on, 258-260 Golden Age managerialism, 258-260 Herman on, 260 influence vs. ownership, 264—265 international comparisons, 248 Jensenism. 5eeJensen, Michael market for corporate control, 277-282 narrowness of debate, 246 Rathenau on, 256 shareholder activism of 1990s, 288-291 Smith on, 255-256 Spencer on, 256-257 stockholder-bondholder conflicts, 248 theoretical taxonomy, 251-252 transactions cost economics, 248-251 transformation, 320-321 useless shareholders, 291-294 correlation coefficient, 116 correlation vs. causation, 145 cost of capital, 184, 298 Council of Institutional Investors, 290 Cowles, Alfred, 164 crack spread, 31 Cramer, James, 103 crank, 243 credit/credit markets assets, holders of, 59-61 as barrier to growth, 237 as boundary-smasher (Marx), 235 centrality of, 118-121 and consciousness, 236-237 European vs.


pages: 313 words: 101,403

My Life as a Quant: Reflections on Physics and Finance by Emanuel Derman

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Berlin Wall, bioinformatics, Black-Scholes formula, Brownian motion, capital asset pricing model, Claude Shannon: information theory, Donald Knuth, Emanuel Derman, fixed income, Gödel, Escher, Bach, haute couture, hiring and firing, implied volatility, interest rate derivative, Jeff Bezos, John Meriwether, John von Neumann, law of one price, linked data, Long Term Capital Management, moral hazard, Murray Gell-Mann, Myron Scholes, Paul Samuelson, pre–internet, publish or perish, quantitative trading / quantitative finance, Richard Feynman, Sharpe ratio, statistical arbitrage, statistical model, Stephen Hawking, Steve Jobs, stochastic volatility, technology bubble, the new new thing, transaction costs, value at risk, volatility smile, Y2K, yield curve, zero-coupon bond, zero-sum game

What counts as much or more is the trading system and the discipline it imposes, the operational errors it disallows and the intuition that traders gain from being able to experiment with a model. Fscher had his own way of thinking about markets. He was deeply inspired by the so-called "general equilibrium" approach of the capital asset pricing model, the idea that prices and markets equilibrate when the expected return per unit of risk is the sane for all securities. This belief was the source of much of his intuition, and was the method he first used to derive the Black-Scholes differential equation. In late July of 1995, shortly before his death, in response to a question I sent him about these matters, he emailed me: "I view all our work on fixedincome models as resulting from the application of the capital asset pricing model to fixed-income markets" I had a touching glimpse of his love for this approach a few years before he died when, together with a few of my colleagues, I tried to assess the effect of transactions costs and hedging frequency on our trading desk's options prices.

I understood nothing about hedging or risk-neutrality, and I paid no attention to the stock market. Several years later, Larry, Mark, and I were sent to a two-week MIT executive summer session on finance, taught by Stuart Myers from his textbook with Brealey. We lived in a campus dorm and luxuriated in our freedom from corporate life, running on the MIT track in the late afternoons and eating in Cambridge in the evenings. Myers's course focused on the Capital Asset Pricing Model, and I was captivated by the apparent similarity between financial theory and thermodynamics. I saw a perhaps-too-facile correspondence between heat and money, temperature and risk, and entropy and the Sharpe ratio, but have never since figured out how to exploit it. The course was brief and intense and required more work than we put into it. One of the lecturers was Terry Marsh, now a Professor at Berkeley and a founding partner of the financial software firm Quantal.


pages: 354 words: 26,550

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

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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

The unexpected component of the announcements is computed as the difference between the announced value and the mean or median economists’ forecast. The unexpected component is the key variable used in estimation of the impact of an event on prices. Theoretically, equities are priced as present values of future cash flows of the company, discounted at the appropriate interest rate determined by Capital Asset Pricing Model (CAPM), the arbitrage pricing theory of Ross (1976), or the investor-specific opportunity cost: equity price = ∞ E[Earningst ] (1 + Rt )t t=1 (12.3) where E[Earningst ] are the expected cash flows of the company at a future time t, and Rt is the discount rate found appropriate for discounting time t dividends to present. Unexpected changes to earnings generate rapid price 174 HIGH-FREQUENCY TRADING responses whereby equity prices quickly adjust to new information about earnings.

Fabozzi Associates. McQueen, Grant and V. Vance Roley, 1993. “Stock Prices, News, and Business Conditions.” Review of Financial Studies 6, 683–707. Mech, T., 1993. “Portfolio Return Autocorrelation.” Journal of Financial Economics 34, 307–344. Mende, Alexander, Lucas Menkhoff and Carol L. Osler, 2006. “Price Discovery in Currency Markets.” Working paper. Merton, Robert C., 1973. “An Intertemporal Capital Asset Pricing Model.” Econometrica 41, 867–887. Merton, Robert C., 1973b. “The Theory of Rational Option Pricing.” Bell Journal of Economics and Management Science 4, 141–183. Muradoglu, G., F. Taskin and I. Bigan, 2000. “Causality Between Stock Returns and Macroeconomic Variables in Emerging Markets.” Russian and East European Finance and Trade 36(6), 33–53. Naik, Narayan Y., Anthony Neuberkert and S. Viswanathan, 1999.

., 174 Boston Options Exchange (BOX), 9 Bowman, R., 174 Boyd, John H., 180 Bredin, Don, 184 Brennan, M.J., 147, 192, 195 Brock, W.A., 13 Broker commissions, post-trade analysis of, 285, 287 Broker-dealers, 10–13, 25 Brooks, C., 55 Brown, Stephen J., 59 Burke, G., 56 Burke ratio, 53t, 56 Business cycle, of high-frequency trading business, 26–27 Caglio, C., 142 Calmar ratio, 53t, 56 Cancel orders, 70 Cao, C., 131, 139, 142 Capital asset pricing model (CAPM), market-neutral arbitrage, 192–195 Capitalization, of high-frequency trading business, 34–35 Capital markets, twentieth-century structure of, 10–13 Capital turnover, 21 Carpenter, J., 253 Carry rate, avoiding overnight, 2, 16, 21–22 Cash interest rates, 40 Caudill, M., 113 Causal modeling, for risk measurement, 254 Chaboud, Alain P., 191 Chakravarty, Sugato, 158–159, 277 Challe, Edouard, 189 Chan, K., 67 Chan, L.K.C., 180, 289, 295 Index Chen, J., 208–209 Chicago Board Options Exchange (CBOE), 9 Chicago Mercantile Exchange (CME), 9, 198 Choi, B.S., 98 Chordia, T., 192, 195, 279 Chriss, N., 274, 275, 295 Chung, K., 67–68 Citadel, 13 Clearing, broker-dealers and, 25 CME Group, 41 Cohen, K., 130 Co-integration, 101–102 Co-integration-based tests, 89 Coleman, M., 89 Collateralized debt obligations (CDOs), 263 Commercial clients, 10 Commodities.


pages: 338 words: 106,936

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

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Albert Einstein, algorithmic trading, Antoine Gombaud: Chevalier de Méré, Asian financial crisis, bank run, beat the dealer, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, Brownian motion, butterfly effect, capital asset pricing model, Carmen Reinhart, Claude Shannon: information theory, collateralized debt obligation, collective bargaining, dark matter, Edward Lorenz: Chaos theory, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, George Akerlof, Gerolamo Cardano, Henri Poincaré, invisible hand, Isaac Newton, iterative process, John Nash: game theory, Kenneth Rogoff, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, martingale, Myron Scholes, new economy, Paul Lévy, Paul Samuelson, prediction markets, probability theory / Blaise Pascal / Pierre de Fermat, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Coase, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical arbitrage, statistical model, stochastic process, The Chicago School, The Myth of the Rational Market, tulip mania, V2 rocket, Vilfredo Pareto, volatility smile

Although Treynor didn’t have a formal background in financial theory either, his business school background had exposed him to a set of problems that he was well suited to work on, and so much of his work at ADL involved financial institutions. Meanwhile, he worked on more theoretical research projects on the side, often motivated by the kinds of problems ADL clients encountered. By the time Black arrived at ADL, Treynor had already developed a new way of understanding the relationship between risk, probability, and expected value, now known as the Capital Asset Pricing Model (CAPM). The basic idea underlying CAPM was that it should be possible to assign a price to risk. Risk, in this context, means uncertainty, or volatility. Certain kinds of assets — U.S. Treasury bonds, for instance — are essentially risk-free. Nonetheless, they yield a certain rate of return, so that if you invest in Treasury bonds, you are guaranteed to make money at a fixed rate. Most investments, however, are inherently risky.

.”: Whether this position is just is an important question, but that the Black-Scholes model holds a privileged position in the first place seems clear. See Haug and Taleb (2011). “. . . the American Financial Association awards the Fischer Black Prize . . .”: The quote is from the AFA’s website’s description of the Fischer Black Prize. See http://www.afajof.org/association/fischerblack.asp. “. . . now known as the Capital Asset Pricing Model (CAPM)”: Treynor (1961) was not the only person to come up with the CAPM, though it is now widely recognized that he was the first. Others with claims to have developed the CAPM include William Sharpe (1964), who won the Nobel Prize for his contribution to asset pricing in 1990; and John Lintner (1965). See, for instance, French (2003) for more on the provenance of the CAPM; see also Bernstein (1993)

Hoboken, NJ: John Wiley and Sons. Forbes magazine. 2011. “The World’s Billionaires 2011.” Available at http://www.forbes.com/lists/2011/10/billionaires_2011.html. Forfar, David O. 2007. “Fischer Black.” Available at http://www.history.mcs.standrews.ac.uk/Biographies/Black_Fischer.html. Fox, Justin. 2009. The Myth of the Rational Market. New York: Harper Business. French, Craig W. 2003. “The Treynor Capital Asset Pricing Model.” Journal of Investment Management 1 (2): 60–72. Galison, Peter. 1997. Image and Logic: A Material Culture of Microphysics. Chicago: University of Chicago Press. — — — . 2003. Einstein’s Clocks, Poincaré’s Maps: Empires of Time. New York: W. W. Norton. Galison, Peter, and Bruce Hevly, eds. 1992. Big Science. Stanford, CA: Stanford University Press. Gebhard, Louis A. 1979.


pages: 368 words: 32,950

How the City Really Works: The Definitive Guide to Money and Investing in London's Square Mile by Alexander Davidson

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accounting loophole / creative accounting, algorithmic trading, asset allocation, asset-backed security, bank run, banking crisis, barriers to entry, Big bang: deregulation of the City of London, capital asset pricing model, central bank independence, corporate governance, Credit Default Swap, dematerialisation, discounted cash flows, diversified portfolio, double entry bookkeeping, Edward Lloyd's coffeehouse, Elliott wave, Exxon Valdez, forensic accounting, global reserve currency, high net worth, index fund, inflation targeting, intangible asset, interest rate derivative, interest rate swap, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, margin call, market fundamentalism, Nick Leeson, North Sea oil, Northern Rock, pension reform, Piper Alpha, price stability, purchasing power parity, Real Time Gross Settlement, reserve currency, Right to Buy, shareholder value, short selling, The Wealth of Nations by Adam Smith, transaction costs, value at risk, yield curve, zero-coupon bond

Weighted-average cost of capital (WACC) is often used as the discount rate. This represents the cost of capital to the company. It is the average of the cost of equity and debt, weighted in proportion to the amounts of equity and debt capital deemed to be financing the business. The cost of equity, which is part of WACC, is the expected return on equity, which is most often measured by the Capital Asset Pricing Model (CAPM). The CAPM finds the required rate of return on a stock by comparing its performance with the market. It expresses this return as equal to the riskfree rate of return plus the product of the equity risk premium and the stock’s beta. The beta measures the sensitivity of a share price to movements in the general stock market. The CAPM stipulates that the market does not reward investors for taking unsystematic (company-specific) risk because it can be eliminated through diversification.

It is no textbook, but it provides a good overview and some back-of-the-envelope calculation methods and, almost unheard of, makes the subject entertaining. The Real Cost of Capital by Tim Ogier, John Rugman and Lucinda Spicer, Financial Times/Prentice Hall, 2004, is a useful introduction to measuring cost of capital. The authors, a team of three at PricewaterhouseCoopers, explain the Capital Asset Pricing Model (CAPM), but warn of its deficiencies. The guidance on estimating the international weighted average cost of capital – using mainly versions of CAPM – breaks new ground, and there is an assault on DCF forecasts, for which cost of capital is used as a key interest rate. The book explores real options valuation as an alternative. Risk management Dealing with Financial Risk: A Guide to Financial Risk Management by David Shirreff, Economist Books, 2004, is a journalistic trot through the basics of financial risk management.

Index 419 fraud 204 9/11 terrorist attacks 31, 218, 242, 243, 254, 257 Abbey National 22 ABN AMRO 103 accounting and governance 232–38 scandals 232 Accounting Standards Board (ASB) 236 administration 17 Allianz 207 Alternative Investment Market (AIM) 44–45, 131, 183, 238 Amaranth Advisors 170 analysts 172–78 fundamental 172–74 others 177–78 Spitzer impact 174–75 technical 175–77 anti-fraud agencies Assets Recovery Agency 211–13 City of London Police 209 Financial Services Authority 208 Financial Crime and Intelligence Division 208 Insurance Fraud Bureau 209 Insurance Fraud Investigators Group 209 International Association of Insurance Fraud Agencies 207, 210, 218 National Criminal Intelligence Service 210 Serious Fraud Office 213–15 Serious Organised Crime Agency 210–11 asset finance 24–25 Association of Investment Companies 167 backwardation 101 bad debt, collection of 26–28 Banco Santander Central Hispano 22 Bank for International Settlements (BIS) 17, 27, 85, 98, 114 bank guarantee 23 Bank of Credit and Commerce International (BCCI) 10, 214 Bank of England 6, 10–17 Court of the 11 credit risk warning 98 framework for sterling money markets 81 Governor 11, 13, 14 history 10, 15–16 Inflation Report 14 inflation targeting 12–13 interest rates and 12 international liaison 17 lender of last resort 15–17 Market Abuse Directive (MAD) 16 monetary policy and 12–15 Monetary Policy Committee (MPC) 13–14 Open-market operations 15, 82 repo rate 12, 15 role 11–12 RTGS (Real Time Gross Settlement) 143 statutory immunity 11 supervisory role 11 Bank of England Act 1988 11, 12 Bank of England Quarterly Model (BEQM) 14 Banking Act 1933 see Glass-Steagall Act banks commercial 5 investment 5 Barclays Bank 20 Barings 11, 15, 68, 186, 299 Barlow Clowes case 214 Barron’s 99 base rate see repo rate Basel Committee for Banking Supervision (BCBS) 27–28 ____________________________________________________ INDEX 303 Basel I 27 Basel II 27–28, 56 Bear Stearns 95, 97 BearingPoint 97 bill of exchange 26 Bingham, Lord Justice 10–11 Blue Arrow trial 214 BNP Paribas 145, 150 bond issues see credit products book runners 51, 92 Borsa Italiana 8, 139 bps 90 British Bankers’ Association 20, 96, 97 building societies 22–23 demutualisation 22 Building Societies Association 22 Capital Asset Pricing Model (CAPM) see discounted cash flow analysis capital gains tax 73, 75, 163, 168 capital raising markets 42–46 mergers and acquisitions (M&A) 56–58 see also flotation, bond issues Capital Requirements Directive 28, 94 central securities depository (CSD) 145 international (ICSD) 145 Central Warrants Trading Service 73 Chancellor of the Exchequer 12, 13, 229 Chicago Mercantile Exchange 65 Citigroup 136, 145, 150 City of London 4–9 Big Bang 7 definition 4 employment in 8–9 financial markets 5 geography 4–5 history 6–7 services offered 4 world leader 5–6 clearing 140, 141–42 Clearing House Automated Payment System (CHAPS) 143 Clearstream Banking Luxembourg 92, 145 commercial banking 5, 18–28 bad loans and capital adequacy 26–28 banking cards 21 building societies 22–23 credit collection 25–26 finance raising 23–25 history 18–19 overdrafts 23 role today 19–21 commodities market 99–109 exchange-traded commodities 101  fluctuations 100 futures 100 hard commodities energy 102 non-ferrous metals 102–04 precious metal 104–06 soft commodities cocoa 107 coffee 106 sugar 107 Companies Act 2006 204, 223, 236 conflict of interests 7 consolidation 138–39 Consumer Price Index (CPI) 13 contango 101 Continuous Linked Settlement (CLS) 119 corporate governance 223–38 best practice 231 Cadbury Code 224 Combined Code 43, 225 compliance 230 definition 223 Directors’ Remuneration Report Regulations 226 EU developments 230 European auditing rules 234–35 Greenbury Committee 224–25 Higgs and Smith reports 227 International Financial Reporting Standards (IFRS) 237–38 Listing Rules 228–29 Model Code 229 Myners Report 229 OECD Principles 226 operating and financial review (OFR) 235– 36 revised Combined Code 227–28 Sarbanes–Oxley Act 233–34 Turnbull Report 225 credit cards 21 zero-per-cent cards 21 credit collection 25–26 factoring and invoice discounting 26 trade finance 25–26 credit derivatives 96–97 back office issues 97 credit default swap (CDS) 96–97 credit products asset-backed securities 94 bonds 90–91 collateralised debt obligations 94–95 collateralised loan obligation 95 covered bonds 93 equity convertibles 93 international debt securities 92–93  304 INDEX ____________________________________________________ junk bonds 91 zero-coupon bonds 93 credit rating agencies 91 Credit Suisse 5, 136, 193 CREST system 141, 142–44 dark liquidity pools 138 Debt Management Office 82, 86 Department of Trade and Industry (DTI) 235, 251, 282 derivatives 60–77 asset classes 60 bilateral settlement 66 cash and 60–61 central counterparty clearing 65–66 contracts for difference 76–77, 129 covered warrants 72–73 futures 71–72 hedging and speculation 67 on-exchange vs OTC derivatives 63–65 options 69–71 Black-Scholes model 70 call option 70 equity option 70–71 index options 71 put option 70 problems and fraud 67–68 retail investors and 69–77 spread betting 73–75 transactions forward (future) 61–62 option 62 spot 61 swap 62–63 useful websites 75 Deutsche Bank 136 Deutsche Börse 64, 138 discounted cash flow analysis (DCF) 39 dividend 29 domestic financial services complaint and compensation 279–80 financial advisors 277–78 Insurance Mediation Directive 278–79 investments with life insurance 275–76 life insurance term 275 whole-of-life 274–75 NEWICOB 279 property and mortgages 273–74 protection products 275 savings products 276–77 Dow theory 175 easyJet 67 EDX London 66 Egg 20, 21 Elliott Wave Theory 176 Enron 67, 114, 186, 232, 233 enterprise investment schemes 167–68 Equiduct 133–34, 137 Equitable Life 282 equities 29–35 market indices 32–33 market influencers 40–41 nominee accounts 31 shares 29–32 stockbrokers 33–34 valuation 35–41 equity transparency 64 Eurex 64, 65 Euro Overnight Index Average (EURONIA) 85 euro, the 17, 115 Eurobond 6, 92 Euroclear Bank 92, 146, 148–49 Euronext.liffe 5, 60, 65, 71 European Central Bank (ECB) 16, 17, 84, 148 European Central Counterparty (EuroCCP) 136 European Code of Conduct 146–47, 150 European Exchange Rate Mechanism 114 European Harmonised Index of Consumer Prices 13 European Union Capital Requirements Directive 199 Market Abuse Directive (MAD) 16, 196 Market in Financial Instruments Directive (MiFID) 64, 197–99 Money Laundering Directive 219 Prospectus Directive 196–97 Transparency Directive 197 exchange controls 6 expectation theory 172 Exxon Valdez 250 factoring see credit collection Factors and Discounters Association 26 Fair & Clear Group 145–46 Federal Deposit Insurance Corporation 17 Federation of European Securities Exchanges 137 Fighting Fraud Together 200–01 finance, raising 23–25 asset 24–25 committed 23 project finance 24 recourse loan 24 syndicated loan 23–24 uncommitted 23 Financial Action Task Force on Money Laundering (FATF) 217–18 financial communications 179–89 ____________________________________________________ INDEX 305 advertising 189 corporate information flow 185 primary information providers (PIPs) 185 investor relations 183–84 journalists 185–89 public relations 179–183 black PR’ 182–83 tipsters 187–89 City Slickers case 188–89 Financial Ombudsman Service (FOS) 165, 279–80 financial ratios 36–39 dividend cover 37 earnings per share (EPS) 36 EBITDA 38 enterprise multiple 38 gearing 38 net asset value (NAV) 38 price/earnings (P/E) 37 price-to-sales ratio 37 return on capital employed (ROCE) 38 see also discounted cash flow analysis Financial Reporting Council (FRC) 224, 228, 234, 236 Financial Services Act 1986 191–92 Financial Services Action Plan 8, 195 Financial Services and Markets Act 2001 192 Financial Services and Markets Tribunal 94 Financial Services Authority (FSA) 5, 8, 31, 44, 67, 94, 97, 103, 171, 189, 192–99 competition review 132 insurance industry 240 money laundering and 219 objectives 192 regulatory role 192–95 powers 193 principles-based 194–95 Financial Services Compensation Scheme (FSCS) 17, 165, 280 Financial Services Modernisation Act 19 financial services regulation 190–99 see also Financial Services Authority Financial Times 9, 298 First Direct 20 flipping 53 flotation beauty parade 51 book build 52 early secondary market trading 53 grey market 52, 74 initial public offering (IPO) 47–53 pre-marketing 51–52 pricing 52–53 specialist types of share issue accelerated book build 54  bought deal 54 deeply discounted rights issue 55 introduction 55 placing 55 placing and open offer 55 rights issues 54–55 underwriting 52 foreign exchange 109–120 brokers 113 dealers 113 default risk 119 electronic trading 117 exchange rate 115 ICAP Knowledge Centre 120 investors 113–14 transaction types derivatives 116–17 spot market 115–16 Foreign Exchange Joint Standing Committee 112 forward rate agreement 85 fraud 200–15 advanced fee frauds 204–05 boiler rooms 201–04 Regulation S 202 future regulation 215 identity theft 205–06 insurance fraud 206–08 see also anti-fraud agencies Fraud Act 2006 200 FTSE 100 32, 36, 58, 122, 189, 227, 233 FTSE 250 32, 122 FTSE All-Share Index 32, 122 FTSE Group 131 FTSE SmallCap Index 32 FTSE Sterling Corporate Bond Index 33 Futures and Options Association 131 Generally Accepted Accounting Principles (GAAP) 237, 257 gilts 33, 86–88 Giovanni Group 146 Glass-Steagall Act 7, 19 Global Bond Market Forum 64 Goldman Sachs 136 government bonds see gilts Guinness case 214 Halifax Bank 20 hedge funds 8, 77, 97, 156–57 derivatives-based arbitrage 156 fixed-income arbitrage 157 Hemscott 35 HM Revenue and Customs 55, 211 HSBC 20, 103 Hurricane Hugo 250  306 INDEX ____________________________________________________ Hurricane Katrina 2, 67, 242 ICE Futures 5, 66, 102 Individual Capital Adequacy Standards (ICAS) 244 inflation 12–14 cost-push 12 definition 12 demand-pull 12 quarterly Inflation Report 14 initial public offering (IPO) 47–53 institutional investors 155–58 fund managers 155–56 hedge fund managers 156–57 insurance companies 157 pension funds 158 insurance industry London and 240 market 239–40 protection and indemnity associations 241 reform 245 regulation 243 contingent commissions 243 contract certainty 243 ICAS and Solvency II 244–45 types 240–41 underwriting process 241–42 see also Lloyd’s of London, reinsurance Intercontinental Exchange 5 interest equalisation tax 6 interest rate products debt securities 82–83, 92–93 bill of exchange 83 certificate of deposit 83 debt instrument 83 euro bill 82 floating rate note 83 local authority bill 83 T-bills 82 derivatives 85 forward rate agreements (FRAs) 85–86 government bonds (gilts) 86–89 money markets 81–82 repos 84 International Financial Reporting Standards (IFRS) 58, 86, 173, 237–38 International Financial Services London (IFSL) 5, 64, 86, 92, 112 International Monetary Fund 17 International Securities Exchange 138 International Swap Dealers Association 63 International Swaps and Derivatives Association 63 International Underwriting Association (IUA) 240 investment banking 5, 47–59 mergers and acquisitions (M&A) 56–58 see also capital raising investment companies 164–69 real estate 169 split capital 166–67 venture capital 167–68 investment funds 159–64 charges 163 investment strategy 164 fund of funds scheme 164 manager-of-managers scheme 164 open-ended investment companies (OEICs) 159 selection criteria 163 total expense ratio (TER) 164 unit trusts 159 Investment Management Association 156 Investment Management Regulatory Organisation 11 Johnson Matthey Bankers Limited 15–16 Joint Money Laundering Steering Group 221 KAS Bank 145 LCH.Clearnet Limited 66, 140 letter of credit (LOC) 23, 25–26 liability-driven investment 158 Listing Rules 43, 167, 173, 225, 228–29 Lloyd’s of London 8, 246–59 capital backing 249 chain of security 252–255 Central Fund 253 Corporation of Lloyd’s 248–49, 253 Equitas Reinsurance Ltd 251, 252, 255–56 Franchise Performance Directorate 256 future 258–59 Hardship Committee 251 history 246–47, 250–52 international licenses 258 Lioncover 252, 256 Member’s Agent Pooling Arrangement (MAPA) 249, 251 Names 248, one-year accounting 257 regulation 257 solvency ratio 255 syndicate capacity 249–50 syndicates 27 loans 23–24 recourse loan 24 syndicated loan 23–24 London Interbank Offered Rate (LIBOR) 74, 76 ____________________________________________________ INDEX 307 London Stock Exchange (LSE) 7, 8, 22, 29, 32, 64 Alternative Investment Market (AIM) 32 Main Market 42–43, 55 statistics 41 trading facilities 122–27 market makers 125–27 SETSmm 122, 123, 124 SETSqx 124 Stock Exchange Electronic Trading Service (SETS) 122–25 TradElect 124–25 users 127–29 Louvre Accord 114 Markets in Financial Instruments Directive (MiFID) 64, 121, 124, 125, 130, 144, 197–99, 277 best execution policy 130–31 Maxwell, Robert 186, 214, 282 mergers and acquisitions 56–58 current speculation 57–58 disclosure and regulation 58–59 Panel on Takeovers and Mergers 57 ‘white knight’ 57 ‘white squire’ 57 Merrill Lynch 136, 174, 186, 254 money laundering 216–22 Egmont Group 218 hawala system 217 know your client (KYC) 217, 218 size of the problem 222 three stages of laundering 216 Morgan Stanley 5, 136 multilateral trading facilities Chi-X 134–35, 141 Project Turquoise 136, 141 Munich Re 207 Nasdaq 124, 138 National Strategy for Financial Capability 269 National Westminster Bank 20 Nationwide Building Society 221 net operating cash flow (NOCF) see discounted cash flow analysis New York Federal Reserve Bank (Fed) 16 Nomads 45 normal market share (NMS) 132–33 Northern Rock 16 Nymex Europe 102 NYSE Euronext 124, 138, 145 options see derivatives Oxera 52  Parmalat 67, 232 pensions alternatively secured pension 290 annuities 288–89 occupational pension final salary scheme 285–86 money purchase scheme 286 personal account 287 personal pension self-invested personal pension 288 stakeholder pension 288 state pension 283 unsecured pension 289–90 Pensions Act 2007 283 phishing 200 Piper Alpha oil disaster 250 PLUS Markets Group 32, 45–46 as alternative to LSE 45–46, 131–33 deal with OMX 132 relationship to Ofex 46 pooled investments exchange-traded funds (ETF) 169 hedge funds 169–71 see also investment companies, investment funds post-trade services 140–50 clearing 140, 141–42 safekeeping and custody 143–44 registrar services 144 settlement 140, 142–43 real-time process 142 Proceeds of Crime Act 2003 (POCA) 211, 219, 220–21 Professional Securities Market 43–44 Prudential 20 purchasing power parity 118–19 reinsurance 260–68 cat bonds 264–65 dispute resolution 268 doctrines 263 financial reinsurance 263–64 incurred but not reported (IBNR) claims insurance securitisation 265 non-proportional 261 offshore requirements 267 proportional 261 Reinsurance Directive 266–67 retrocession 262 types of contract facultative 262 treaty 262 retail banking 20 retail investors 151–155 Retail Prices Index (RPI) 13, 87 264  308 INDEX ____________________________________________________ Retail Service Provider (RSP) network Reuters 35 Royal Bank of Scotland 20, 79, 221 73 Sarbanes–Oxley Act 233–34 securities 5, 29 Securities and Futures Authority 11 self-regulatory organisations (SROs) 192 Serious Crime Bill 213 settlement 11, 31, 140, 142–43 shareholder, rights of 29 shares investment in 29–32 nominee accounts 31 valuation 35–39 ratios 36–39 see also flotation short selling 31–32, 73, 100, 157 Society for Worldwide Interbank Financial Telecommunications (SWIFT) 119 Solvency II 244–245 Soros, George 114, 115 Specialist Fund Market 44 ‘square mile’ 4 stamp duty 72, 75, 166 Sterling Overnight Index Average (SONIA) 85 Stock Exchange Automated Quotation System (SEAQ) 7, 121, 126 Stock Exchange Electronic Trading Service (SETS) see Lloyd’s of London stock market 29–33 stockbrokers 33–34 advisory 33 discretionary 33–34 execution-only 34 stocks see shares sub-prime mortgage crisis 16, 89, 94, 274 superequivalence 43 suspicious activity reports (SARs) 212, 219–22 swaps market 7 interest rates 56 swaptions 68 systematic internalisers (SI) 137–38 Target2-Securities 147–48, 150 The Times 35, 53, 291 share price tables 36–37, 40 tip sheets 33 trading platforms, electronic 80, 97, 113, 117 tranche trading 123 Treasury Select Committee 14 trend theory 175–76 UBS Warburg 103, 136 UK Listing Authority 44 Undertakings for Collective Investments in Transferable Securities (UCITS) 156 United Capital Asset Management 95 value at risk (VAR) virtual banks 20 virt-x 140 67–68 weighted-average cost of capital (WACC) see discounted cash flow analysis wholesale banking 20 wholesale markets 78–80 banks 78–79 interdealer brokers 79–80 investors 79 Woolwich Bank 20 WorldCom 67, 232 Index of Advertisers Aberdeen Asset Management PLC xiii–xv Birkbeck University of London xl–xlii BPP xliv–xlvi Brewin Dolphin Investment Banking 48–50 Cass Business School xxi–xxiv Cater Allen Private Bank 180–81 CB Richard Ellis Ltd 270–71 CDP xlviii–l Charles Schwab UK Ltd lvi–lviii City Jet Ltd x–xii The City of London inside front cover EBS Dealing Resource International 110–11 Edelman xx ESCP-EAP European School of Management vi ICAS (The Inst. of Chartered Accountants of Scotland) xxx JP Morgan Asset Management 160–62 London Business School xvi–xviii London City Airport vii–viii Morgan Lewis xxix Securities & Investments Institute ii The Share Centre 30, 152–54 Smithfield Bar and Grill lii–liv TD Waterhouse xxxii–xxxiv University of East London xxxvi–xxxviii


pages: 447 words: 104,258

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

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

Index 4-moments CAPM actual (ACT) number of days AI see Alternative Investments “algorithmic” trading Alternative Investments (AI) American options bond options CRR pricing model option pricing rho amortizing swaps analytic method, VaR annual interest compounding annualized volatility autocorrelation corrective factor historical volatility risk measures APT see Arbitrage Pricing Theory AR see autoregressive process Arbitrage Pricing Theory (APT) ARCH see autoregressive conditional heteroskedastic process ARIMA see autoregressive integrated moving average process ARMA see autoregression moving average process ask price asset allocation attribution asset swaps ATM see at the money ATMF see at the money forward options at the money (ATM) convertible bonds options at the money forward (ATMF) options attribution asset allocation performance autoregression moving average (ARMA) process autoregressive (AR) process autoregressive conditional heteroskedastic (ARCH) process autoregressive integrated moving average (ARIMA) process backtesting backwardation basket CDSs basket credit derivatives basket options BDT see Black, Derman, Toy process benchmarks Bermudan options Bernardo Ledoit gain-loss ratio BGM model see LIBOR market model BHB model (Brinson’s) bid price binomial distribution binomial models binomial processes, credit derivatives binomial trees Black, Derman, Toy (BDT) process Black and Karasinski model Black–Scholes formula basket options beyond Black–Scholes call-put parity cap pricing currency options “exact” pricing exchange options exotic options floor pricing forward prices futures/forwards options gamma processes hypotheses underlying jump processes moneyness sensitivities example valuation troubles variations “The Black Swan” (Taleb) bond convexity bond duration between two coupon dates calculation assumptions calculation example callable bonds in continuous time duration D effective duration forwards FRNs futures mathematical approach modified duration options physical approach portfolio duration practical approach swaps uses of duration bond futures CFs CTD hedging theoretical price bond options callable bonds convertible bonds putable bonds bond pricing clean vs dirty price duration aspects floating rate bonds inflation-linked bonds risky bonds bonds binomial model CDSs convexity credit derivatives credit risk exotic options forwards futures government bonds options performance attribution portfolios pricing risky/risk-free spot instruments zero-coupon bonds see also bond duration book value method bootstrap method Brinson’s BHB model Brownian motion see also standard Wiener process bullet bonds Bund (German T-bond) 10-year benchmark futures callable bonds call options call-put parity jump processes see also options Calmar ratio Capital Asset Pricing Model (CAPM) 4-moments CAPM AI APT vs CAPM Sharpe capitalization-weighted indexes capital market line (CML) capital markets caplets CAPM see Capital Asset Pricing Model caps carry cash and carry operations cash flows cash settlement, CDSs CBs see convertible bonds CDOs see collateralized debt obligations CDSs see credit default swaps CFDs see contracts for difference CFs see conversion factors charm sensitivity cheapest to deliver (CTD) clean prices clearing houses “close” prices CML see capital market line CMSs see constant maturity swaps Coleman, T.

Also, the stock returns and corresponding variances and covariances used in the Markowitz methodology are computed from historical data: they are only estimators of the actual – unknown – expected returns, variances and covariances. For large numbers of stocks, the resulting error can seriously affect the outcome of the portfolio optimization. To escape this, Sharpe has developed his CAPM or Capital Asset Pricing Model, based on the following principle: stocks returns are linked together by a single common factor, F (that will be specified later on), through a linear regression. The returns ri and rF are considered to be distributed as a normal distribution. So that, for the stock i(ri, σi), the equation of the regression line of i in F is (4.3) assuming i residuals are such as σ(i, j) = 0 and σ(i, rF) = 0.

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

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Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, 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, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

The notion of judging investment strategies on the basis of riskadjusted returns is entirely reasonable. For example, if a strategy makes 20 percent per year and the benchmark makes 10 percent, but the strategy exposes the investor to three times the risk, the strategy has not beaten the index after risk adjustment. The question is: How does one define and quantify risk? Quantifying risk requires a risk model. The most well known is the capital asset pricing model21 (CAPM), which explains systematic differences in the returns of securities in terms of a single risk factor, the security’s relative volatility. This is to say, the risk of a stock is quantified by its Theories of Nonrandom Price Motion 341 volatility relative to the volatility of the market as a whole. Thus, if a stock is twice as volatile as the market index, its returns should be twice that of the market.

It merely says that when those gains are adjusted for risk, they will not be better than investing in an index fund. So long as the term risk is left undefined, EMH defenders are free to conjure up new forms of risk after the fact. As Elliott wavers have proven, after-the-fact fiddling allows any prior observations to be explained or explained away. And that, it seems to me, is what Fama and French did.57 They invented a new, ad hoc risk model using three risk factors to replace the old standby, the capital asset pricing model,58 which uses only one risk factor, a stock’s volatility relative to the market index. Quite conveniently, the two new risk factors that Fama and French decided to add were the price-to-book ratio and market capitalization. By citing these as proxies for risk, Fama and French neatly explained away their predictive power. “According to the new risk model, stocks of smaller firms (low-market cap) or firms with low market-tobook ratios are fundamentally riskier companies and thus must offer higher average returns to compensate the investors willing to own them.

That book cites as the original source of the illustration R.N. Shepard, Mind Sights: Original Visual Illusions (New York: W.H. Freeman & Company, 1990). 3. T. Gilovich, How We Know What Isn’t So: The Fallibility of Human Reason in Everyday Life (New York: Free Press, 1991). 4. See “Defying Psychiatric Wisdom, These Skeptics Say Prove It,” New York Times (March 9, 2004), F1–F3. 5. Alpha refers to the capital asset pricing model term, which represents the Y-intercept obtained by regressing the returns of an investment strategy on the returns of a market index. It represents the portion of return that is not attributable to the volatility of the strategy (its beta). Meritorious investment strategies have alphas that are positive to a statistically significant degree. 6. J. Murphy, Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications (New York: New York Institute of Finance, 1999), 20, where market trends are said to be clearly visible. 7.


pages: 695 words: 194,693

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

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Albert Einstein, Andrei Shleifer, asset allocation, asset-backed security, banking crisis, Benoit Mandelbrot, Black Swan, Black-Scholes formula, Bretton Woods, Brownian motion, 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, transatlantic slave trade, tulip mania, wage slave

See census contracts (rentes) retirement: Babylonian women entrepreneurs and, 56–57; Condorcet on pensions for, 273, 511, 516; Defoe on pensions for, 325; demographic trends and, 7, 516–18; extended families providing for, 7, 55; individual accounts vs. government funds for, 513–14; investments in Old Babylonian period and, 55; Lowenfeld on benefits of investment for, 417; money fief for soldier and, 243; pension funds holding asset-backed securities for, 385; worldwide pension provisions for, 511, 516. See also life annuities; Social Security return on investment, 6–7; alternative use of capital and, 237; Capital Asset Pricing Model and, 508–9 Rialto, 222, 227–29; government bonds traded in, 231, 236, 237, 253; in seventeenth century, 350 Ricardo, David, 407 Rim-Sin, King, 49, 50, 57, 58, 60 risk: Capital Asset Pricing Model and, 508; consumption loans and, 5; Dow Theory and, 487; Dutch investment fund and, 385; entrepreneurial, and Chinese state, 140; European innovations altering attitudes toward, 203; insurance and, 365; investment trusts of 1920s and, 500–501; minimized by colonization, 418; option payoffs and, 281, 283; reallocation of, 4; Regnault’s statistical analysis of, 279, 286; Scholastics on just compensation for, 235–36; securitization and, 386; of social change, 8.

In mid-century, even as Wall Street had turned mostly away from simple, diversified portfolio investing toward a variety of forecasting methods and fundamental security analysis, the seeds of mathematics and statistics planted in American finance by Cowles and Fisher began to grow and lead to a new financial revolution. In this chapter, we look at how the Markowitz model and a model that followed close upon it, the Capital Asset Pricing Model (CAPM), which revived a global framework for investing and introduced new, unforeseen conflicts between finance and nation-states. INVESTMENT AND EXACT SCIENCE Harry Markowitz essentially applied statistical methods to the earlier insight of Henry Lowenfeld and Irving Fisher. He wrote down an exact formula for optimally diversifying across stocks. While Lowenfeld had suggested an equally balanced portfolio across the assets of different countries and Fisher had recommended a well-diversified investment trust, Markowitz asked how to achieve the best, most diversified solution.

See also agency problem bureaucracy, of Chinese empire, 139–40; accounting systems and, 201; paper vouchers in, 189; problem of managing, 167, 169, 171; Zhouli as paradigm for, 172–74 Burough, Stephen, 311 Byzantine empire: fall of, 226; Ravenna under, 113; Venice and, 225, 226, 229, 230 Cabot, John, 309 Cabot, Sebastian, 309–10 call options, 280–82, 283–84. See also option pricing; options capital: alternative use of, 237, 243; corporate form and, 289; corporate form for VOC and, 318; provided by investment, 6–7; reallocation of, 4; theological debate about usury and, 237 Capital Asset Pricing Model (CAPM), 504, 508–9, 511 capital calls, 310, 327 capitalism: Chinese financial development and, 137, 140, 141, 197–98, 200, 437; Church’s view of time and, 234; colonialism and, 418–22; Great Depression as failure of, 482; Keynes on shortcomings of, 456–57; Lenin on end of, 446–48; Marx’s challenge to, 405, 407–8, 417; Mesopotamian precursor of, 55; repeatedly appearing in historical record, 304; Russian rejection of, 449–51, 452–53 capital markets: Bretton Woods Agreement and, 460; in eighteenth- and nineteenth-century China, 197–98, 200; Europe’s intense reliance on, 520; Lowenfeld on benefits of, 417; Marx on, 408, 465; of seventeenth-century Europe, 322, 323, 325; technological development and, 197; of twentieth-century China, 423; in the West compared to China, 198–99.


pages: 267 words: 71,123

End This Depression Now! by Paul Krugman

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airline deregulation, Asian financial crisis, asset-backed security, bank run, banking crisis, Bretton Woods, capital asset pricing model, Carmen Reinhart, centre right, correlation does not imply causation, credit crunch, Credit Default Swap, currency manipulation / currency intervention, debt deflation, Eugene Fama: efficient market hypothesis, financial deregulation, financial innovation, Financial Instability Hypothesis, full employment, German hyperinflation, Gordon Gekko, Hyman Minsky, income inequality, inflation targeting, invisible hand, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, low skilled workers, Mark Zuckerberg, money market fund, moral hazard, mortgage debt, negative equity, paradox of thrift, Paul Samuelson, price stability, quantitative easing, rent-seeking, Robert Gordon, Ronald Reagan, Upton Sinclair, We are the 99%, working poor, Works Progress Administration

And the 1987 stock crash, in which the Dow plunged nearly 23 percent in a day for no clear reason, should have raised at least a few doubts about market rationality. These events, however, which Keynes would have considered evidence of the unreliability of markets, did little to blunt the force of a beautiful idea. The theoretical model that finance economists developed by assuming that every investor rationally balances risk against reward—the so-called Capital Asset Pricing Model, or CAPM (pronounced cap-em)—is wonderfully elegant. And if you accept its premises, it’s also extremely useful. CAPM not only tells you how to choose your portfolio; even more important from the financial industry’s point of view, it also tells you how to put a price on financial derivatives, claims on claims. The elegance and apparent usefulness of the new theory led to a string of Nobel Prizes for its creators, and many of the theory’s adepts also received more mundane rewards: armed with their new models and formidable math skills—the more arcane uses of CAPM require physicist-level computations—mild-mannered business school professors could and did become Wall Street rocket scientists, earning Wall Street paychecks.

.: Social Security and, 224 tax cuts of, 124, 227 Bush, Kate, 20 Bush administration, 116 business investment: confidence and, 201, 206 government spending cuts and, 143–44 business investment, slump in, 41, 52, 117 lack of demand and, 24–25, 26, 33, 136, 145 long-term effects of, 16 California: defense industry in, 236 housing bubble in, 111 Calvin and Hobbes, 191 Cameron, David, 200–201 Canada, 198–99 Capital Asset Pricing Model (CAPM), 98–99 capital ratios, 58–59 Carney, Jay, 124–25 Carter, Jimmy, deregulation under, 61 Carville, James, 132 “Case for Flexible Exchange Rates, The” (Friedman), 170 Case-Shiller index, 112 causation: common, 83 correlation vs., 83, 198, 232–33, 237 Cheney, Dick, 124 Chicago Board of Trade, 6 China, 146, 159 U.S. trade with, 221 Citibank, 63, 68 Citicorp, 63, 85 Citigroup, 63, 85, 116 Clague, Ewan, 35 Clinton, Bill, 36 Cochrane, John, 106, 107 Cold War, 236 Cole, Adam, 78 collateralized loan obligations, 54, 55 college graduates, unemployment and underemployment among, 11–12, 16, 37, 144–45 commodities, prices of, 159–60 Community Reinvestment Act, 65 confidence: business, 201, 206 consumer, 201 investor, 132, 188, 192, 194–97, 200, 213 unemployment and, 94–96 “confidence fairy,” 195, 200, 201 Congress, U.S., 192–93 deregulation and, 67 polarization of, 89 TARP enacted by, 116 2008 financial crisis blamed on, 64, 65 2012 election and, 226, 227–28 see also House of Representatives, U.S.; Senate, U.S.


pages: 295 words: 66,824

A Mathematician Plays the Stock Market by John Allen Paulos

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Benoit Mandelbrot, Black-Scholes formula, Brownian motion, business climate, butterfly effect, capital asset pricing model, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, double entry bookkeeping, Elliott wave, endowment effect, Erdős number, Eugene Fama: efficient market hypothesis, four colour theorem, George Gilder, global village, greed is good, index fund, intangible asset, invisible hand, Isaac Newton, John Nash: game theory, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Myron Scholes, Nash equilibrium, Network effects, passive investing, Paul Erdős, Paul Samuelson, Ponzi scheme, price anchoring, Ralph Nelson Elliott, random walk, Richard Thaler, Robert Shiller, Robert Shiller, short selling, six sigma, Stephen Hawking, survivorship bias, transaction costs, ultimatum game, Vanguard fund, Yogi Berra

If your portfolio or stock is statistically determined to be relatively more volatile than the market as a whole, then changes in the market will bring about exaggerated changes in the stock or portfolio. If it is relatively less volatile than the market as a whole, then changes in the market will bring about attenuated changes in the stock or portfolio. This brings us to the so-called Capital Asset Pricing Model, which maintains that the expected excess return on one’s stock or portfolio (the difference between the expected return on the portfolio, Rp, and the return on risk-free treasury bills, Rf) is equal to the notorious beta, symbolized by β, multiplied by the expected excess return of the general market (the difference between the market’s expected return, Rm, and the return on risk-free treasury bills, Rf).

Brian auditors Aumann, Robert availability error average values compared with distribution of incomes risk as variance from averages average return compared with median return average value compared with distribution of incomes buy-sell rules and outguessing average guess risk as variance from average value averaging down Bachelier, Louis Bak, Per Barabasi, Albert-Lazló Bartiromo, Maria bear markets investor self-descriptions and shorting and distorting strategy in Benford, Frank Benford’s Law applying to corporate fraud background of frequent occurrence of numbers governed by Bernoulli, Daniel Beta (B) values causes of variations in comparing market against individual stocks or funds strengths and weaknesses of technique for finding volatility and Big Bang billiards, as example of nonlinear system binary system biorhythm theory Black, Fischer Black-Scholes option formula blackjack strategies Blackledge, Todd “blow up,” investor blue chip companies, P/E ratio of Bogle, John bonds Greenspan’s impact on bond market history of stocks outperforming will not necessarily continue to be outperformed by stocks Bonds, Barry bookkeeping. see accounting practices bottom-line investing Brock, William brokers. see stock brokers Buffett, Warren bull markets investor self-descriptions and pump and dump strategy in Butterfly Economics (Ormerod) “butterfly effect,” of nonlinear systems buy-sell rules buying on the margin. see also margin investments calendar effects call options. see also stock options covering how they work selling strategies valuation tools campaign contributions Capital Asset Pricing Model capital gains vs. dividends Central Limit Theorem CEOs arrogance of benefits in manipulating stock prices remuneration compared with that of average employee volatility due to malfeasance of chain letters Chaitin, Gregory chance. see also whim trading strategies and as undeniable factor in market chaos theory. see also nonlinear systems charity Clayman, Michelle cognitive illusions availability error confirmation bias heuristics rules of thumb for saving time mental accounts status quo bias Cohen, Abby Joseph coin flipping common knowledge accounting scandals and definition and importance to investors dynamic with private knowledge insider trading and parable illustrating private information becoming companies/corporations adjusting results to meet expectations applying Benford’s Law to corporate fraud comparing corporate and personal accounting financial health and P/E ratio of blue chips competition vs. cooperation, prisoner’s dilemma complexity changing over time horizon of sequences (mathematics) of trading strategies compound interest as basis of wealth doubling time and formulas for future value and present value and confirmation bias definition of investments reflecting stock-picking and connectedness. see also networks European market causing reaction on Wall Street interactions based on whim interactions between technical traders and value traders irrational interactions between traders Wolfram model of interactions between traders Consumer Confidence Index (CCI) contrarian investing dogs of the Dow measures of excellence and rate of return and cooperation vs. competition, prisoner’s dilemma correlation coefficient. see also statistical correlations counter-intuitive investment counterproductive behavior, psychology of covariance calculation of portfolio diversification based on portfolio volatility and stock selection and Cramer, James crowd following or not herd-like nature of price movements dart throwing, stock-picking contest in the Wall Street Journal data mining illustrated by online chatrooms moving averages and survivorship bias and trading strategies and DeBondt, Werner Deciding What’s News (Gans) decimalization reforms decision making minimizing regret selling WCOM depression of derivatives trading, Enron despair and guilt over market losses deviation from the mean. see also mean value covariance standard deviation (d) variance dice, probability and Digex discounting process, present value of future money distribution of incomes distribution of wealth dynamic of concentration UN report on diversified portfolios. see stock portfolios, diversifying dividends earnings and proposals benefitting returns from Dodd, David dogs of the Dow strategy “dominance” principle, game theory dot com IPOs, as a pyramid scheme double-bottom trend reversal “double-dip” recession double entry bookkeeping doubling time, compound interest and Dow dogs of the Dow strategy percentages of gains and losses e (exponential growth) compound interest and higher mathematics and earnings anchoring effect and complications with determination of inflating (WCOM) P/E ratio and stock valuation and East, Steven H.


pages: 263 words: 75,455

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

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

“Alpha” and Adjusted Performance Raw compound annual growth rates and spreads provide some information about the performance of the various price ratios, but its factor-adjusted performance provides a fuller picture of its utility. We want to know how each decile portfolio's exposure to the market contributes to each price ratio's performance over the market. To assess each portfolio's adjusted performance, we control for and calculate the capital asset pricing model (CAPM) estimate of “alpha,” which we discuss in some detail below. We use the market capitalization-weight index of all NYSE/AMEX/Nasdaq as our market return. Our risk-free measure is the four-week Treasury bill. Table 7.2 sets out our calculations of alpha. Bolded figures are statistically significant at the 5 percent level, which can be interpreted as the probability that these results appear by random chance.

The procedures researchers use to estimate alpha can be complicated, but the idea is simple: How much value does a strategy create after controlling for a variety of risk factors? FIGURE 12.10(a) Five-Year Rolling Alpha for Quantitative Value (1974 to 2011) FIGURE 12.10(b) Ten-Year Rolling Alpha for Quantitative Value (1974 to 2011) To help with robustness, we estimate alpha using several different asset-pricing models. We control for general market risk using the capital asset pricing model2; we adjust for market, size, and value exposures with the Fama and French three-factor model3; we account for momentum using the four-factor model4; and, finally, we account for liquidity by adding the Lubos Pastor and Robert Stambaugh market-wide liquidity factor to create the comprehensive five-factor model.5 Figures 12.10(a) and (b) confirm that the Quantitative Value strategy consistently generates alpha on rolling 5- and 10-year bases, regardless of the model we choose to inspect.


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

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Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs

Most important, as dean Nitin Nohria noted, students must respond to human realities: “team members of varying skill and motivation, vendors who may be more or less reliable, and customers with their own views of what they want as opposed to what one wants them to buy.” It’s all a long way from learning the capital asset pricing model in a classroom. Not many people will go to Harvard or Stanford business schools, but everyone can learn lessons from the way those schools are reallocating students’ time. The truth is the capital asset pricing model is still important for business students to learn, but it no longer makes sense for them to learn it in classrooms, gathered together in physical proximity but scarcely interacting at all. Learning basic concepts online is enormously faster and more effective than doing it in a classroom.


pages: 244 words: 79,044

Money Mavericks: Confessions of a Hedge Fund Manager by Lars Kroijer

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activist fund / activist shareholder / activist investor, Bernie Madoff, capital asset pricing model, corporate raider, diversification, diversified portfolio, family office, fixed income, forensic accounting, Gordon Gekko, hiring and firing, implied volatility, index fund, intangible asset, Jeff Bezos, Just-in-time delivery, Long Term Capital Management, merger arbitrage, new economy, Ponzi scheme, risk-adjusted returns, risk/return, shareholder value, Silicon Valley, six sigma, statistical arbitrage, Vanguard fund, zero-coupon bond

This does not mean that edge does not exist – I’m on the board of a few hedge funds that I certainly believe have edge in the market, and they are well worth their fees as a result. After making my own scepticism about the high levels of fees floating around the financial system clear to anyone who cares to listen (and many that don’t), I often get asked how I think people should be investing their money. The above may sound like financial mumbo-jumbo, but it is eminently practicable in the real world – sort of a real and practical adapted version of a capital asset pricing model (CAPM). You buy a portfolio of world stock-index funds, corporate and government debts, and do so in the cheapest way, while trying to tax-optimise and adjust your gearing level. The calculations behind this project might be somewhat complex, with many indices involved, but relying too much on high-level calculations also misses the point that the model calculations are only as good as the assumptions you put in, and that the expectation of great precision is misleading.

Index Abramovich, Roman Absolute Returns for Kids (ARK) added value, 2nd, 3rd, 4th, 5th Africa poverty alleviation projects Aker Yards, 2nd, 3rd, 4th, 5th, 6th alpha and beta, 2nd, 3rd AP Fondet arbitrage, merger 2nd asset-stripping assets under management (AUM), 2nd, 3rd, 4th, 5th background checking bank bailouts 2008–09 Bank of Ireland Bear Stearns Berkeley Square, 2nd, 3rd Berkshire Hathaway Bezos, Jeff Black-Scholes-Merton option-pricing formula Blair, Tony Bloomberg, 2nd bonds corporate, 2nd government, 2nd, 3rd zero-coupon bonuses, 2nd, 3rd British Airways Buffett, Warren Bure burn-out Busson, Arpad capital gross invested, 2nd, 3rd regulatory seed, 2nd capital asset pricing model (CAPM) cascade effect, 2nd cash deposits, 2nd insurance The Children’s Investment Fund Management (TCI) churning Collery, Peter compensation structures, 2nd, 3rd, 4th see also bonuses competitive edge, 2nd, 3rd, 4th, 5th Conti, Massimo, 2nd corporate bonds, 2nd correlation, market, 2nd, 3rd, 4th, 5th, 6th, 7th country indices Credit Suisse, 2nd, 3rd Cuccia, Enrico Dagens Industry debt crises (2011) debt investments derivative trading discounted fees, 2nd discounts to net asset value diversification, 2nd, 3rd, 4th dividends, 2nd early investors edge, competitive, 2nd, 3rd, 4th, 5th efficient market frontier Enskilda Baken entertainment events entrepreneurship, 2nd equity redistribution Eurohedge, 2nd, 3rd European Fund Manager of the Year Award event assessment, 2nd exchange traded funds (ETFs), 2nd, 3rd, 4th expenses firm, 2nd, 3rd, 4th, 5th fund-related, 2nd, 3rd, 4th, 5th, 6th, 7th, 8th family life, 2nd fees see incentive fees; management fees; performance fees Fidelity Financial Times firm costs, 2nd, 3rd, 4th, 5th Ford, Tom Fresenius FSA (Financial Services Authority), 2nd, 3rd, 4th, 5th fundamental value analysis funds of funds, 2nd, 3rd, 4th, 5th, 6th futures gearing, 2nd, 3rd, 4th, 5th, 6th Gentry, Baker, 2nd, 3rd Goldman Sachs, 2nd government bonds, 2nd, 3rd gross invested capital, 2nd, 3rd Gross, Julian Grosvenor Square HBK Investments, 2nd, 3rd headhunting health, 2nd hedge funds collapse of, 2nd expenses see expenses fees see incentive fees; management fees; performance fees industry growth, 2nd, 3rd mid-cap/large-cap bias nature of operational planning opportunities for young managers ownership structures partnership break-ups short-term performance staff recruitment, 2nd starting up top managers value generated by Henkel herd mentality, 2nd Hohn, Chris holding company discounts incentive fees, 2nd, 3rd, 4th index funds, 2nd, 3rd, 4th, 5th, 6th insurance, cash deposit insurance sector, 2nd interviews investor activism Italian finance JP Morgan Keynes, John Maynard Korenvaes, Harlen, 2nd Lage, Alberto, 2nd, 3rd large-cap bias Lazard Frères, 2nd, 3rd, 4th, 5th Lebowitz, Larry leverage, 2nd Liechtenstein, Max liquidity London bombings (7 July 2005) long run, 2nd, 3rd long securities Long Term Capital Management (LTCM) Lyle, Dennis Macpherson, Elle managed accounts management fees, 2nd, 3rd, 4th, 5th, 6th, 7th, 8th discounted, 2nd funds of funds, 2nd mutual funds tracker funds Mannesmann market capitalisation, 2nd, 3rd market correlation, 2nd, 3rd, 4th, 5th, 6th, 7th market exposure, 2nd, 3rd, 4th, 5th, 6th market neutrality, 2nd mean variance optimisation Mediobanca merger arbitrage, 2nd Merrill Lynch Merton, Robert mid-cap bias Montgomerie, Colin Morgan Stanley, 2nd, 3rd, 4th, 5th, 6th, 7th TMT (telecom, media and technology) conferences Morland, Sam, 2nd, 3rd MSCI World index, 2nd, 3rd mutual funds NatWest Nelson, Jake net asset-value (NAV), 2nd Nokia Norden O’Callaghan, Brian, 2nd, 3rd, 4th, 5th, 6th, 7th, 8th, 9th, 10th, 11th, 12th, 13th Och, Dan oil tanker companies oilrig sector, 2nd options trading out-of-the-money put options ownership structure partnership break-ups pension funds, 2nd performance fees, 2nd, 3rd, 4th Perry, Richard personal networks Philips, David portfolio theory poverty alleviation prime brokerage private jet companies Ramsay, Gordon Rattner, Steve recruitment, 2nd redemption notices regulatory capital returns, 2nd rights issues risk, 2nd, 3rd risk profile, 2nd, 3rd, 4th, 5th, 6th, 7th Rohatyn, Felix Ronaldo Rosemary Asset Management Rothschild, Mayer Royal Bank of Scotland Rubenstein, David rump (stub) trades salaries see compensation structures Samson, Peter SAS airline SC Fundamental seed capital, 2nd shipping companies short securities short-term performance six-stigma events Smith Capital Partners, 2nd softing special situations stakeholders Standard & Poor’s 500 index, 2nd, 3rd, 4th standard deviation, 2nd, 3rd, 4th star managers Start-up of the Year awards Stern, Dan stub trades Superfos Svantesson, Lennart talent introduction groups tax, 2nd, 3rd, 4th Telefonica Moviles time horizon for investments, 2nd, 3rd Torm Totti, David tracker funds trade commission trade sourcing trade theses US market value investing Vanguard index fund, 2nd VIX index, 2nd Vodafone warrants, 2nd, 3rd Westbank Wien, Byron Wilson, Susan world indices, 2nd zero-coupon bonds Zilli, Aldo PEARSON EDUCATION LIMITED Edinburgh Gate Harlow CM20 2JE Tel: +44 (0)1279 623623 Fax: +44 (0)1279 431059 Website: www.pearson.com/uk First published in Great Britain in 2010 Second edition 2012 Electronic edition published 2012 © Pearson Education Limited 2012 (print) © Pearson Education Limited 2012 (electronic) The right of Lars Kroijer to be identified as author of this work has been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.


pages: 483 words: 141,836

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

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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, 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, The Myth of the Rational Market, Thomas Bayes, too big to fail, transaction costs, value at risk, yield curve

IGT may be inferior for explaining relative prices, but it roots the absolute level of prices in fundamental economics: issuers making real cash flow transactions, and investors making probability judgments about their holdings. When you think about the market for large-capitalization U.S. stocks, MPT seems pretty reasonable. When you think about the market for emerging market real estate, IGT makes more sense. In the real world in which MPT was the dominant theory, the dominant model of market equilibrium was the capital asset pricing model (CAPM). It held that the expected excess return of any asset—remember excess means the return above a risk-free rate of interest—is equal to the asset’s beta times the expected excess return of the market. This follows from MPT and EMH, with some specific assumptions about the market and investors. Investment Growth Theory In the parallel universe of IGT, we get a different formula.

He continued backward until he got to the point where the problem was posed. Pascal and Fermat were astonished to discover that the backward induction method gave the same answer as Fermat’s forward solution. The value of Thorp, Black, Scholes, and Merton’s work was that the backward argument from arbitrage was identical to a forward argument based on stochastic partial differential equations, plus an equilibrium argument based on the capital asset pricing model. In the early 1970s, the word derivative was invented to describe options because their price can be derived, in theory at least, from mathematical reasoning. If the option deviated from the theoretical price, again in theory, anyone could make riskless profits, also known as arbitrage. Today you usually see the word derivative explained as meaning the value of the derivative security depends on some underlying asset.

See also Probability betting/probability and foundation of frequentism and in history/law “prior beliefs” and risk defining capital technology startups and Beat the Dealer (Thorp) Beat the Market (Thorp and Kassouf) Behavioral Game Theory (Camerer) Bennet, Rick Bernoulli, Jakob Berns, Gregory Bernstein, Peter Betting: Kelly bets probability and public sports Beyond Counting (Grosjean) Beyond Individual Choice (Bacharach) Big Short, The (Lewis) Black, Alethea Black, Fischer Black-Scholes-Merton model Black Swan, The (Taleb) Black Wednesday Bloom, Murray Teigh Bogle, John Bond ratings Bookstaber, Richard Born Losers (Sandage) Bounds of Reason, The (Gintis) Brenner, Reuven and Gabrielle Bringing Down the House (Mezrich) British Treasury Broke, (Adams) Bronze Age Bronze Age Economics (Earle) Bubble investors Bulls, Bears, and Brains (Leitzes) Burton, Robert Alan Business Cycles and Equilibrium (Black) Busting Vegas (Mezrich) Calvet, Laurent E. Camerer, Colin Capital: allocation at-risk formation requirement Capital asset pricing model (CAPM) Capital Ideas (Bernstein) Capital Offense (Hirsh) Carnap, Rudolph Cash, Lehman Brothers and Cash Nexus (Ferguson) Chance, Luck, and Statistics (Levinson) Chances Are (Kaplan) Change of numeraire Checklist Manifesto, The (Gawande) Chernow, Ron Chicago Board Options Exchange Volatility Index (VIX) Chief risk officer (CRO) Chiles, James Cincinnati Kid, The ( Jessup) Citibank Clearinghouses CMOs.


pages: 471 words: 124,585

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

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Admiral Zheng, Andrei Shleifer, Asian financial crisis, asset allocation, asset-backed security, Atahualpa, bank run, banking crisis, banks create money, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Bretton Woods, BRICs, British Empire, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, collateralized debt obligation, colonial exploitation, commoditize, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, Daniel Kahneman / Amos Tversky, deglobalization, diversification, diversified portfolio, double entry bookkeeping, Edmond Halley, Edward Glaeser, Edward Lloyd's coffeehouse, financial innovation, financial intermediation, fixed income, floating exchange rates, Fractional reserve banking, Francisco Pizarro, full employment, German hyperinflation, Hernando de Soto, high net worth, hindsight bias, Home mortgage interest deduction, Hyman Minsky, income inequality, information asymmetry, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, iterative process, John Meriwether, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labour mobility, Landlord’s Game, liberal capitalism, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, means of production, Mikhail Gorbachev, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, mortgage tax deduction, Myron Scholes, Naomi Klein, negative equity, Nick Leeson, Northern Rock, Parag Khanna, pension reform, price anchoring, price stability, principal–agent problem, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, quantitative hedge fund, RAND corporation, random walk, rent control, rent-seeking, reserve currency, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, seigniorage, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spice trade, structural adjustment programs, technology bubble, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Thorstein Veblen, too big to fail, transaction costs, value at risk, Washington Consensus, Yom Kippur War

budget deficit and federal debt 117-18 and Enron 170-71 and home ownership 267 businesses see companies; entrepreneurs Business Week 122-3 Calais 73 Calancha, Fray Antonio de la 23 Californian energy deregulation 170 California Public Employees’ fund 222 call options 12 Cambi, Bernardo 187 Cambodia 278 Camdessus, Michel 312 Canada 147 cancer 184 Canetti, Elias 105 Cantillon, Richard 145 Canton see Guangzhou capital: adequacy see banks appreciation 125 controls 303 ‘dead’ 275 export/mobility 122 market see capital market Capital Asset Pricing Model (CAPM) 323 capitalism: and accumulation 17 and the company 119-20 evolutionary processes in 348-9 and hyperinflation 106 and money 17 and war 297-8 and welfare state 211 capital market: alleged improvement 6 liberalization 310-12 Capital One 353 CAPM see Capital Asset Pricing Model Capra, Frank 247 Caribbean countries 99 Carlyle 337 Carnegie, Andrew 297 Carter, Jimmy 254 Carville, James 65 Case-Shiller index 261 cash: absence of see electronic money; moneyless societies ‘nexus’ 17 in people’s hands 29 see also coins; paper money Castile 20. see also Spain Castlemilk 280 Castlereagh, Lord 83 Castro, Fidel 213 Castro, Sergio de 214 catastrophes see disasters cat bonds 227 Cato Institute 276 Cauas, Jorge 214 Cavallo, Domingo 114-15 CDOs see collateralized obligations CDS see credit default swaps census (contract) 73 Center for Responsible Lending 270-71 Central America 99 central banks 49-50 and Black Monday (1987) 166 and bubbles 122 establishment of 57 explicit targets 116 independence of 116 and irrational markets 174 monarchs and 141 monopolies on note issue 49 and oil price rises 308 and subprime crisis 9 and war 100 see also Bank of England etc.

For one thing, they were simultaneously pursuing multiple, uncorrelated trading strategies: around a hundred of them, with a total of 7,600 different positions.82 One might go wrong, or even two. But all these different bets just could not go wrong simultaneously. That was the beauty of a diversified portfolio - another key insight of modern financial theory, which had been formalized by Harry M. Markowitz, a Chicago-trained economist at the Rand Corporation, in the early 1950s, and further developed in William Sharpe’s Capital Asset Pricing Model (CAPM).83 Long-Term made money by exploiting price discrepancies in multiple markets: in the fixed-rate residential mortgage market; in the US, Japanese and European government bond markets; in the more complex market for interest rate swapsbf - anywhere, in fact, where their models spotted a pricing anomaly, whereby two fundamentally identical assets or options had fractionally different prices.


pages: 416 words: 39,022

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

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

xix xix xxi PART I THE MASSIVE CHANGES IN THE WORLD OF FINANCE Introduction 1 The Regulatory Context 1.1 Precautionary surveillance 1.2 The Basle Committee 1.2.1 General information 1.2.2 Basle II and the philosophy of operational risk 1.3 Accounting standards 1.3.1 Standard-setting organisations 1.3.2 The IASB 2 Changes in Financial Risk Management 2.1 Definitions 2.1.1 Typology of risks 2.1.2 Risk management methodology 2.2 Changes in financial risk management 2.2.1 Towards an integrated risk management 2.2.2 The ‘cost’ of risk management 2.3 A new risk-return world 2.3.1 Towards a minimisation of risk for an anticipated return 2.3.2 Theoretical formalisation 1 2 3 3 3 3 5 9 9 9 11 11 11 19 21 21 25 26 26 26 vi Contents PART II EVALUATING FINANCIAL ASSETS Introduction 3 4 29 30 Equities 3.1 The basics 3.1.1 Return and risk 3.1.2 Market efficiency 3.1.3 Equity valuation models 3.2 Portfolio diversification and management 3.2.1 Principles of diversification 3.2.2 Diversification and portfolio size 3.2.3 Markowitz model and critical line algorithm 3.2.4 Sharpe’s simple index model 3.2.5 Model with risk-free security 3.2.6 The Elton, Gruber and Padberg method of portfolio management 3.2.7 Utility theory and optimal portfolio selection 3.2.8 The market model 3.3 Model of financial asset equilibrium and applications 3.3.1 Capital asset pricing model 3.3.2 Arbitrage pricing theory 3.3.3 Performance evaluation 3.3.4 Equity portfolio management strategies 3.4 Equity dynamic models 3.4.1 Deterministic models 3.4.2 Stochastic models 35 35 35 44 48 51 51 55 56 69 75 79 85 91 93 93 97 99 103 108 108 109 Bonds 4.1 Characteristics and valuation 4.1.1 Definitions 4.1.2 Return on bonds 4.1.3 Valuing a bond 4.2 Bonds and financial risk 4.2.1 Sources of risk 4.2.2 Duration 4.2.3 Convexity 4.3 Deterministic structure of interest rates 4.3.1 Yield curves 4.3.2 Static interest rate structure 4.3.3 Dynamic interest rate structure 4.3.4 Deterministic model and stochastic model 4.4 Bond portfolio management strategies 4.4.1 Passive strategy: immunisation 4.4.2 Active strategy 4.5 Stochastic bond dynamic models 4.5.1 Arbitrage models with one state variable 4.5.2 The Vasicek model 115 115 115 116 119 119 119 121 127 129 129 130 132 134 135 135 137 138 139 142 Contents 4.5.3 The Cox, Ingersoll and Ross model 4.5.4 Stochastic duration 5 Options 5.1 Definitions 5.1.1 Characteristics 5.1.2 Use 5.2 Value of an option 5.2.1 Intrinsic value and time value 5.2.2 Volatility 5.2.3 Sensitivity parameters 5.2.4 General properties 5.3 Valuation models 5.3.1 Binomial model for equity options 5.3.2 Black and Scholes model for equity options 5.3.3 Other models of valuation 5.4 Strategies on options 5.4.1 Simple strategies 5.4.2 More complex strategies PART III GENERAL THEORY OF VaR Introduction vii 145 147 149 149 149 150 153 153 154 155 157 160 162 168 174 175 175 175 179 180 6 Theory of VaR 6.1 The concept of ‘risk per share’ 6.1.1 Standard measurement of risk linked to financial products 6.1.2 Problems with these approaches to risk 6.1.3 Generalising the concept of ‘risk’ 6.2 VaR for a single asset 6.2.1 Value at Risk 6.2.2 Case of a normal distribution 6.3 VaR for a portfolio 6.3.1 General results 6.3.2 Components of the VaR of a portfolio 6.3.3 Incremental VaR 181 181 181 181 184 185 185 188 190 190 193 195 7 VaR Estimation Techniques 7.1 General questions in estimating VaR 7.1.1 The problem of estimation 7.1.2 Typology of estimation methods 7.2 Estimated variance–covariance matrix method 7.2.1 Identifying cash flows in financial assets 7.2.2 Mapping cashflows with standard maturity dates 7.2.3 Calculating VaR 7.3 Monte Carlo simulation 7.3.1 The Monte Carlo method and probability theory 7.3.2 Estimation method 199 199 199 200 202 203 205 209 216 216 218 viii Contents 7.4 Historical simulation 7.4.1 Basic methodology 7.4.2 The contribution of extreme value theory 7.5 Advantages and drawbacks 7.5.1 The theoretical viewpoint 7.5.2 The practical viewpoint 7.5.3 Synthesis 8 Setting Up a VaR Methodology 8.1 Putting together the database 8.1.1 Which data should be chosen?

. , N N the portfolio risk can develop as follows:   N N σP2 = var  Xj αj + βP RM + Xj εj  j =1 j =1 2 = βP2 σM + N 1 2 σ N 2 j =1 εj 2 = βP2 σM + 1 2 σ N ε Equities 93 Here, the average residual variance has been introduced: σε2 = N 1 2 σ N j =1 εj The first term of the decomposition is independent of N , while the second tends towards 0 when N becomes very large. This analysis therefore shows that the portfolio risk σP2 can be broken down into two terms: 2 2 • The systematic component βP2σM2 (non-diversifiable risk). • The specific component Xj σεj (diversifiable risk). 3.3 MODEL OF FINANCIAL ASSET EQUILIBRIUM AND APPLICATIONS 3.3.1 Capital asset pricing model Unlike the previous models, this model, developed independently by W. Sharpe39 and J. Lintner40 and known as CAPM (MEDAF in French) is interested not in choosing a portfolio for an individual investor but in the behaviour of a whole market when the investors act rationally41 and show an aversion to risk. The aim, in this situation, is to determine the exact value of an equity. 3.3.1.1 Hypotheses The model being examined is based on a certain number of hypotheses.

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


pages: 493 words: 132,290

Vultures' Picnic: In Pursuit of Petroleum Pigs, Power Pirates, and High-Finance Carnivores by Greg Palast

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anti-communist, back-to-the-land, bank run, Berlin Wall, Bernie Madoff, British Empire, capital asset pricing model, capital controls, centre right, Chelsea Manning, clean water, collateralized debt obligation, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, energy security, Exxon Valdez, invisible hand, means of production, Myron Scholes, offshore financial centre, random walk, Ronald Reagan, sensible shoes, transfer pricing, uranium enrichment, Washington Consensus, Yogi Berra

Gambling in the stock market by investment banks would be, necessarily, eliminated. No more picking stocks, a fool’s errand. Financial rewards would be small, but risk would vanish. The world’s financial panics would be left to history, and all economic booms and busts smoothed into calm waves. I met Black not long after he’d put this Drunk’s Random Walk into an academic paper with his friend Myron Scholes. They called it the Capital Asset Pricing Model.19 About the same time, a newly expanding investment bank, a small house on the edge of the financial universe, Goldman Sachs, also fell in love with Dr. Black’s Magic Model, and hired all his best students and, eventually, Dr. Black himself. Black’s magic crew at Goldman looked at the stock market but, instead of seeing securities, saw simply a soup of financial molecules, which could be manipulated and sliced and rejoined in strange and wonderful combinations.

The quickest of the Chicago students, often armed with mighty computer algorithms that could make Einstein sweat, praised Friedman and the free market to the sky—and made billions proving Friedman wrong. The market could be fixed, fondled, fucked with, bent, and the suckers kept deaf, dumb, and blind, have their pockets slashed, lose their jobs, homes, and pensions to the arbs, the hedge fund operators, and Enron traders guided by their secret, well-proven theorem: Just ask the Sack. Just ask the Vulture. But I had their sorcerer’s stone, the Capital Asset Pricing Model, right in my hand. The preppies and climbers around me knew exactly what to do with their little stones: Goldman and friends were paying a quarter million dollars a year starting salary for Chicago B-School trainees. (Chicago was the hot place, and unlike Harvard grads, Chicago kids weren’t afraid of arithmetic.) Some of my classmates earned millions, but most earned tens of millions, some billions.

Rosen had worked his way up through the union ranks from a job on the factory shop floor at General Electric, where he worked as an assembly-line mechanic after getting his degree in physics from U Chicago. (This Aristotelian choice was echoed in his son Carl, who took work as a subway electrician after graduating Harvard.) So there we were, me and The Black One, two Maoists (who had no idea what “Maoism” was) who studied the Chicago models and numbers night and day. We had this idea: What if some skinny long-haired kids figured out a way to use the Capital Asset Pricing Model not to make a killing but to stop the killers? In her llama-wool poncho at the business school, Lonigro was so strangely out of place that I stopped her and told her that I didn’t know what she was doing but I wanted to do it with her. She walked me to her Southside apartment, picking up weeds from between the sidewalk cracks to make us a luncheon salad. Inside, spread across a fifteen-foot wall, floor to ceiling, were huge sheets of butcher paper with a rough-drawn map of the planet.


pages: 823 words: 220,581

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

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

In the case of the stock market, it means at least four things: that the collective expectations of stock market investors are accurate predictions of the future prospects of companies; that share prices fully reflect all information pertinent to the future prospects of traded companies; that changes in share prices are entirely due to changes in information relevant to future prospects, where that information arrives in an unpredictable and random fashion; and that therefore stock prices ‘follow a random walk,’ so that past movements in prices give no information about what future movements will be – just as past rolls of dice can’t be used to predict what the next roll will be. These propositions are a collage of the assumptions and conclusions of the ‘efficient markets hypothesis’ (EMH) and the ‘capital assets pricing model’ (CAPM), which were formal extensions to Fisher’s (pre-Depression) time value of money theories. Like the Fisher theories of old, these new theories were microeconomic in nature, and presumed that finance markets are continually in equilibrium. There were several economists who developed this sophisticated equilibrium analysis of finance. In what follows I s on the work of W. F. Sharpe.

(Ibid: 19; emphasis added) Unfortunately, both neoclassical and behavioral economists ignored this caveat, and applied the axioms that von Neumann and Morgenstern developed to situations of one-off gambles, in which the objective risk that would apply in a repeated experiment was replaced by the subjective uncertainty of a single outcome. Neoclassical economists combined the concept of expected utility with their ordinal, ‘indifference curve’ theory of consumer choice to develop the Capital Assets Pricing Model, despite the fact that von Neumann was adamant that he wanted to replace the concept of indifference curves with his concept of cardinal utility: we hope we have shown that the treatment by indifference curves implies either too much or too little: if the preferences of the individual are not at all comparable, then the indifference curves do not exist. If the individual’s preferences are all comparable, then we can even obtain a (uniquely defined) numerical utility which renders the indifference curves superfluous.

Guthrie (1952) ‘The shape of the average cost curve,’ American Economic Review, 42(5): 832–8. Fama, E. F. (1970) ‘Efficient capital markets: a review of theory and empirical work,’ Journal of Finance, 25(2): 383–417. Fama, E. F. and K. R. French (1999) ‘The corporate cost of capital and the return on corporate investment,’ Journal of Finance, 54(6): 1939–67. Fama, E. F. and K. R. French (2004) ‘The Capital Asset Pricing Model: theory and evidence,’ Journal of Economic Perspectives, 18(3): 25–46. Feher, D. C. (1999) Debt Deflation: The Birth of a Concept and Its Development over Time, Unpublished honors thesis, University of Western Sydney. Financial Crisis Inquiry Commission (2011) The Financial Crisis Inquiry Report: Final Report of the National Commission on the Causes of the Financial and Economic Crisis in the United States.


pages: 827 words: 239,762

The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite by Duff McDonald

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Albert Einstein, barriers to entry, Bayesian statistics, Bernie Madoff, Bob Noyce, Bonfire of the Vanities, business process, butterfly effect, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collateralized debt obligation, collective bargaining, commoditize, corporate governance, corporate raider, corporate social responsibility, creative destruction, deskilling, discounted cash flows, disintermediation, Donald Trump, family office, financial innovation, Frederick Winslow Taylor, full employment, George Gilder, glass ceiling, Gordon Gekko, hiring and firing, income inequality, invisible hand, Jeff Bezos, job-hopping, John von Neumann, Joseph Schumpeter, Kenneth Arrow, London Whale, Long Term Capital Management, market fundamentalism, Menlo Park, new economy, obamacare, oil shock, pattern recognition, performance metric, Peter Thiel, Plutocrats, plutocrats, profit maximization, profit motive, pushing on a string, Ralph Nader, Ralph Waldo Emerson, RAND corporation, random walk, rent-seeking, Ronald Coase, Ronald Reagan, Sand Hill Road, Saturday Night Live, shareholder value, Silicon Valley, Skype, Steve Jobs, survivorship bias, The Nature of the Firm, the scientific method, Thorstein Veblen, union organizing, urban renewal, Vilfredo Pareto, War on Poverty, William Shockley: the traitorous eight, women in the workforce, Y Combinator

There’s nothing scientific about conducting research via the case method. The only people who believe that are HBS’s own faculty, for reasons of academic self-respect. “[They] tell themselves that they [are] helping to build a new intellectual edifice by the time-honored means of deductive reasoning,”11 says author Laurence Shames. He’s not being complimentary. Spender asks which have proved more valuable to financial services: the invention of the capital asset pricing model, portfolio management and options theories (none of which emerged from HBS), or twenty thousand instances of “ex ante relevance [that] is actually irrelevant to the quality business schools because the objective of their research and education is to develop new knowledge, anticipate change, and so force business beyond present practice.” While the occasional HBS faculty member does reshape the thinking and language through which business school students are being inducted into the religion of capitalism, most of them are simply fellow travelers.

The history reaffirmed HBS’s commitment to its case method—by 1980, the School’s $15 million research budget exceeded the overall budget of any other graduate business school in the world,7 and in 1980–81, HBS shipped almost 100 million pages of materials from its case inventory of 18,000 to more than 6,000 customers.8 (By 1995, the research budget had topped $44 million.9) But that commitment aside, by the end of the 1980s, A Delicate Experiment represented not much more than an historical artifact, a story of the way things used to be. In 1986, BusinessWeek put McArthur on its cover, under the title “Remaking an Institution: The Harvard B-School.”10 The biggest change was the rise of the finance faculty, Michael Jensen foremost among them. The new science of managerial decision making was encapsulated in the capital asset pricing model and discounted cash flow, models that had no space for questions like customer loyalty and responsibility to one’s employees. It was no coincidence that the School finally dropped its Trade Union Program early in the decade. The rhetoric coming out of HBS about finance sounded as if it had been written by the financial services lobby itself. And it might as well have been. When the School launched its Global Financial System (GFS) project in 1992, its advisory board included executives from the likes of J.

One reason those at or from HBS did not comprehend the increasing fragility of the financial system was that they had been enthusiastic supporters of many of the factors that had made it so: efficient markets theory, shareholder capitalism, the perverse incentives of Michael Jensen–inspired compensation packages, “innovation spirals” at the likes of Enron, and HBS-endorsed “risk management” systems at giant financial organizations that were nothing of the sort. “A kind of market fundamentalism took hold in business education,” HBS professor Rakesh Khurana told the New York Times in 2009. “The new logic of shareholder primacy absolved management of any responsibility for anything other than financial results.”9 And the tools they used in service of that logic had only cemented it in its place. The capital asset pricing model (CAPM), for example, hadn’t originated at HBS, but it had been studied and refined there, by Michael Jensen, Robert Merton, and others. The effect of CAPM had been similar to that of agency theory in that it reduced a task (in this case, stock selection) to a single number, beta, which was derived from a regression analysis of a stock’s historical movements in relation to the overall market.


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

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 process, butterfly effect, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, Chance favours the prepared mind, 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, 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, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, short selling, sovereign wealth fund, statistical arbitrage, Steven Pinker, stochastic process, survivorship bias, 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 Efficient Markets Hypothesis follows a simple chain of economic logic to its counterintuitive conclusion. Cardano’s martingale, Bachelier’s random walk, Samuelson’s proof, and Fama’s statistics all lead to the same place: prices must fully reflect all available information. The Efficient Markets Hypothesis didn’t appear in a vacuum, however. It was part of a new quantitative movement in financial economics, along with Harry Markowitz’s optimal portfolio theory; William Sharpe’s Capital Asset Pricing Model (which we’ll come back to in chapter 8); and Fischer Black, Myron Scholes, and Robert C. Merton’s option pricing formula. These discoveries appeared within a few years of each other, and they illuminated aspects of market behavior that had remained mysterious for centuries. Of all the discoveries in the new quantitative finance movement, however, the Efficient Markets Hypothesis was the crown jewel.

During that period, this striving became a powerful stimulus in the mathematization of economic theory.”20 Debreu was referring to a series of breakthroughs that not only greatly expanded our understanding of economic theory, but also held out the tantalizing possibility of practical applications in fiscal and monetary policy, financial stability, and central planning. These breakthroughs include game theory, general equilibrium theory, the economics of uncertainty, long-term economic growth theory, portfolio theory and the Capital Asset Pricing Model, option pricing theory, macroeconometric modeling, computable general equilibrium modeling, and rational expectations.21 Many of these contributions have been recognized by the Nobel Prize committee, and they’ve permanently changed the field of economics from an obscure branch of moral philosophy pursued by gentlemen-scholars, to a full-fledged scientific endeavor not entirely unlike the deductive process with which Isaac Newton explained the motion of the planets from three simple laws.

There’s a positive association between risk and reward among all financial investments. Assets with higher reward also have higher risk. Principle 2: Alpha, Beta, and the CAPM. The expected return of an investment is linearly related to its risk (in other words, plotting risk versus expected return on a graph should show a straight line), and is governed by an economic model known as the Capital Asset Pricing Model, or CAPM (more on this later). Principle 3: Portfolio Optimization and Passive Investing. Using statistical estimates derived from Principle 2 and the CAPM, portfolio managers can construct diversified long-only portfolios of fi nancial assets that offer investors attractive risk-adjusted rates of return at low cost. Principle 4: Asset Allocation. Choosing how much to invest in broad asset classes is more important than picking individual securities, so the asset allocation decision is sufficient for managing the risk of an investor’s savings.


pages: 389 words: 109,207

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

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

Sharpe subscribed to the view that successful portfolio managers are like successful astrologists—good at convincing the wealthy and gullible that their services are valuable. For two years Sharpe was a professor at UC Irvine. He came to know Thorp, and they had a number of friendly parries over market efficiency. At Irvine, Sharpe was working on the theory that would make him famous, the Capital Asset Pricing Model. Sharpe moved to Stanford. In 1975 Thorp invited him back to UC Irvine to lecture. During the visit, Thorp tried again to win Sharpe over to his position. Thorp had just been starting out as a market-beating(?) investor when Sharpe taught at UC Irvine. Now he had a track record. Thorp described some of the trades he’d made to Sharpe. One was a 1974 trade in an American Motors Corporation (AMC) convertible bond maturing in 1988.

“In some cases, the funds’ trading is dictated”: Laing 1974. “an incipient but growing switch”: Laing 1974. “just one of many tools”: Laing 1974. “The whole computer-model bit is ridiculous”: Laing 1974. “The better one was one of those crazy funds”: Laing 1974. Lost $107,000 on U.S. Financial: Laing 1974. Phoned attorneys: Laing 1974. Asked money managers if they beat the market: Bernstein 1992, 75; Thorp, interview. Capital Asset Pricing Model: Sharpe 1964. AMC convertible bond deal: Kurson 1999a, 42–44. “Situations that simple”: Kurson 1999a, 44. Sharpe on “active” and “passive” investors: Thorp, interview; see also Sharpe 1991. The Sting inspired by delayed wire service con: Cooney 1982, 76. I am all but certain that the alias “Kelly,” adopted by Robert Redford’s character, is a coincidence. But screenwriter David Ward was well versed in the history of the wire services and associated confidence games.


pages: 293 words: 88,490

The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber

asset allocation, bank run, bitcoin, butterfly effect, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial innovation, fixed income, Flash crash, Henri Poincaré, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, Richard Feynman, Richard Feynman, risk/return, Saturday Night Live, self-driving car, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve

I was suspicious that in many cases they used odd time periods, like from 1989 to 1993. It might have been data fitting in some cases, but in others it was that the world caught on to the opportunities and they were traded away. Thus the very nature of the financial system is to defeat ergodicity. Temporal instability is the bane of some of the most fundamental financial models. Take the capital asset pricing model—for which Sharpe shared the Nobel Prize. Fama and MacBeth (1973) and others report favorable estimates of the capital asset pricing model over a sample that runs until 1965, but it falls apart when the sample was updated to include the 1970s and 1980s. (The solution: add more variables.) In physics it may not strain credulity to model processes as ergodic—that is, to assert that time and history do not really matter—but in social and historical sciences it is ridiculous.


pages: 819 words: 181,185

Derivatives Markets by David Goldenberg

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Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial innovation, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, law of one price, locking in a profit, London Interbank Offered Rate, Louis Bachelier, margin call, market microstructure, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, price mechanism, random walk, reserve currency, risk/return, riskless arbitrage, Sharpe ratio, short selling, stochastic process, stochastic volatility, time value of money, transaction costs, volatility smile, Wiener process, Y2K, yield curve, zero-coupon bond, zero-sum game

This page intentionally left blank INDEX adjusted intrinsic value (AIV) 381, 396–7, 406; for European call, definition of 375–6 adjusted time premium (ATP) 396, 397 All-or-None (AON) orders 214, 215 American options 328 annualized dividend yields 88 anticipatory selling 339 anticipatory buying 339–40 arbitrage: arbitrage definitions 100–2; arbitrage opportunities 75, 77–8, 100–1, 103, 167, 172, 188, 192, 208, 260, 292, 450, 474; pricing by 464; risk-free arbitrage 100, 373; risky arbitrage 100-1 arithmetic Brownian motion (ABM) model of prices: equivalent martingale measures (EMMs) 530–1; option pricing in continuous time 540–1 back stub period 294 backwardation, contango and 198–9 Bank of International Settlements (BIS) 246 basic American call (put) option pricing model 332–4 basic European option pricing model, interpretation of 397–8 basic (naked) strategies 347–63 basis risk 223, 237, 238; cross hedging and 244; spot price risk and 178–82 bid-asked spread, trading within 133–4 bid prices 127, 134, 136, 161, 336 binomial option pricing model (BOPM) 436–48, 467–8, 475–6, 485–506; arbitrage, pricing by 464; binomial process (any N) 439; binomial process (N=1) 448; combination function C(N,j) 443–4; concept checks: algorithm for determination of B, verification of 485; binomial completeness, rule of thumb on 449; binomial model, time modeling in 438; calculation of combination function C(N,j) 444; hedge ratio interpretation 482; hedging a European call option in BOPM (N=2) 477; solution to 505; option price behavior (N=2) 477; solution to 505–6; path 2 contribution analysis 496; path 3 contribution analysis solution to 506; path structures of binomial process, working with 442; solution to 472; price paths for N-period binomial model 442; solution to 471–2; pricing terminal options 446; underlying stock price uncertainty modeling 438-40; valuation of option (time=0) using RNVR 490; verification of numerical example (N=2) numbers 489; verification of option values (N=2) in comparison with replicating portfolio method 493; European call option valuation at expiration 446; exercises for learning development of 501–5; fundamental theorem of asset pricing (FTAP2) 490; hedging a European call option (N=2) 477–85; implementation (N=2) 485–90; joint probability of given path 444; key concepts 501; logic of BOPM (N=1) and its drivers 463; multiperiod model (N >1), path integral approach 493–500; numerical example (N=2) 487–90; option price behavior (N=2) 476; option valuation for 445–8; path structure of binomial process 440–2, 442–4; paths, thinking of BOPM in terms of 493–9; price paths, total number of 440–2; price paths ending at specific terminal price, total number of 442–4; pricing option at expiration 445–6; pricing option currently (time t=0) 446–8; proof that the BOPM (N=1) is complete, three parts of 517; proof of 638 INDEX model for general N 499–500; replication, no-arbitrage and 464; riskneutral valuation, exogenous variables and 615; risk-neutral valuation relationship: derivation as 467–8; interpretation of 468; as risk-neutral valuation relationship (RNVR) formula (N > 1) 490–3; as set of BOPMs (N=1) 491; stock price behavior (N=2) 475–6; stock price evolution: for binomial process (N=2) 440; for N-period binomial process, summary of 444–5; stock price tree (N=2) 488; stock price uncertainty 439; time in discrete time framework, modeling of 437–8; trinomial model (three stock outcomes) 464 Black-Scholes option pricing model: option pricing in continuous time 566–85, 588–9; from Bachelier 571–83; historical volatility estimator method 583–4; implied volatility estimator method 585; importance of 588–9; parsimony of 588; potential for 589; reduction of GBM to ABM with drift 567–70; risk-neutral transition density functions, generation of unknown from knowns 570–1; volatility estimation in Black-Scholes model 583–5; risk-neutral valuation, contribution to 598, 601; valuation of forward contracts in continuous time (assets with a dividend yield) 88 block trade eligibility 214, 228 block trade minimum 214, 228 Bond Equation 552, 554–5 boundaries, absorption of 541 Brownian motion paths, non-smoothness of 560; see also arithmetic Brownian motion (ABM); geometric Brownian motion (GBM) Buffett, Warren 252 buyers and sellers, matching of 125, 126–7 buying back stock 339 buying forward 7–8 calendar spreads 199 ‘calling away’ of stock 422 capital asset pricing model (CAPM) 447, 605 capital gains: effect on stock prices 98–9; capital gains process 98–9, 111 carrying charge hedging 188–93; convergence, implications for 189; equilibrium (no-arbitrage) in full carrying charge market 190–3; overall profits on 189 cash and carry transactions 5 cash commodity prices 35 cash flows: for annual rate swap 302; in non-intermediated swaps 282–4 cash settlement vs. commodity settlement 157; implications from problem of 157 CBOE (Chicago Board Options Exchange) 324–5, 334; asked price entries 335, 336; bid entries 335, 336; equity option specifications 343; exchange-traded option contracts 325; last sale entries 335, 336; Merck call options and price quotes 334–7; mini equity option specifications 344; net entries 335, 336; open interest entries 335, 336; volume entries 335, 336 certainty equivalent (CE): certainty equivalent cash flow 397; risk-neutral valuation and 603, 604 Clearing Houses: counterparty risk 140; futures exchange and 140; guarantor of trades 140; intermediation by 14–15; membership 140; operations and functions 139–53 clearing of trades 126; process of, offsetting futures trades and 141–4 close of market 145 clustering (persistence), volatility and 585 combination function C(N,j) 443–4 combinations of positions 50 combining charts to see profits from hedged positions 54–5 commentary on financial futures contracts price quotes 216–17 commitment prices 41 commitment to buy 67 INDEX commodities, ways to buy and sell 5 commodity forward contracts: paying fixed and receiving floating in 276; as single period swaps 275–6 Commodity Futures Trading Commission (CFTC) 123, 124, 125, 140, 215, 229 Commodity Pool Operators (CPOs) 123 Commodity Trading Advisors (CTAs) 123 complete markets 449 complete risk-expected return analysis of riskless hedge (BOPM, N=1) 607–18; direct calculation of ?

71; calculation of equilibrium forward prices 78; solution 86; pricing zero-coupon bond with face value equal to current forward price of underlying commodity 73; solution to 86; pricing zero-coupon bonds 72; solution to 86; settling a forward commitment 72; zero-coupon bond, pricing on basis of forward contract at compounded riskfree rate 73 consensus in risk-neutral valuation 598–9; with consensus 599; without consensus 599–601 consumption capital asset pricing model (CCAPM) 605 contango and backwardation 198–9 context in study of options markets 326–7 contingent claim pricing 514–17 continuation region 385 641 continuous compounding and discounting 69–71 continuous dividends from stocks, modeling yields from 93–4 continuous yields, modeling of 90–4 contract life, payments over 88 contract month listings 214, 215, 228 contract offerings 227–8 contract size 19, 214, 215, 227, 228 contract specifications 17, 18–19 contracts offered 257–8 convenience, risk-neutral valuation by 631–2 convenience yield 89 convergence of futures to cash price at expiration 189 convexity of option price 406 correlation effect 165–6 cost-of-carry 89; model of, spread and price of storage for 195 counterparty risk 11, 12–13, 140 covered call hedging strategy 419–27; economic interpretation of 426–7; protective put strategies, covered calls and 419; writes, types of 420–6 credit spreads 298–9 cumulative distribution function 544 currency futures 213–17; contract specifications 213–15; forward positions vs. futures positions 220; pricing vs. currency forward pricing 225; quote mechanism, future price quotes 216–17; risk management strategies using 217–24 currency spot and currency forwards 103–9 currency swaps, notional value of 274 current costs: of generating alternative payoffs 78; payoffs and 66; related strategies and, technique of going back and forth between 393 current price as predictor of future stock prices 531 daily price limits 228, 229 daily settlement process 144–51, 153; financial futures contracts and 216, 260 642 INDEX dealer intermediated plain vanilla swaps 284–93; arbitraging swaps market 292–3; asked side in 286; bid side in 285; dealer’s spread 286; example of 284–6; hedging strategy: implications of 291–2; outline of 288–90; plain vanilla swaps as hedge vehicles 286–92 dealer’s problem, finding other side to swap 294–8; asked side in 295; bid side in 295; credit spreads in spot market (AA-type firms) 296; dealer swap schedule (AA-type firms) 295; selling a swap 296; swap cash flows 298; synthetic floating-rate financing (AA-type firms) 297; transformation from fixed-rate to floating rate borrowing 297–8 decision-making: option concept in 324; process of, protection of potential value in 36–7 default in forward market contracting 11–12 deferred spot transactions 78–9 delayed exercise premium 331, 337 delivery dates 19 demutualization 139–40 derivative prices: co-movements between spot prices and 26; underlying securities and 66 directional trades 371–2 discounted option prices 527–8 discounted stock price process 524–5, 527–8, 530 discrete-time martingale, definition of 521 diversifiable risk 225 diversification, maximum effect of 419–20 dividend-adjusted geometric mean (for S&P 500) 227 dividend payments, effect on stock prices 94–8 dividend payout process 97, 111; connection between capital gains process and 111–13 dollar equivalency 227, 234, 239–40 dollar returns, percentage rates of 366 domestic economy (DE) 103–4, 105 dominance principle 372, 373; implications of 374–88 double expectations (DE) 534–5 duration for interest-rate swaps 300 dynamic hedging 473–506; BOPM as riskneutral valuation relationship (RNVR) formula (N > 1) 490–3; hedging a European call option (N=2) 477–85; implementation of binomial option pricing model for (N=2) 485–90; multiperiod BOPM model (N=3) 494; multiperiod BOPM model (N > 1), path integral approach 493–500; numerical example of binomial option pricing model (N=2) 487–90; option price behavior (N=2) 476; path structure for multi-period BOPM model (N=3) 497; stock price behavior (N=2) 475–6; stock price evolution (N-period binomial process), summary of 499; value contributions for multi-period BOPM model (N=3) 498; see also binomial option pricing model (BOPM) economy-wide factors, risk and 225–6 effective date 293 effective payoff 220, 233 effective price, invoice price on delivery and 153–6 efficient market hypotheses (EMH) 517; features of 532; guide to modeling prices 529–33; option pricing in continuous time 558, 560, 561; semi-strong form of 531; strong form of 531, 532; weak form of 531 EFP eligibility 214 embedded leverage 79–80 endogenous variables 614–15 equilibrium forward prices 402; comparison with equilibrium futures prices 193–5; valuation of forward contracts (assets without dividend yield) 78 equilibrium (no-arbitrage) in full carrying charge market 190–3; classical short selling a commodity 192; Exchange Traded Funds (ETF) 191–2; formal arbitrage opportunity 192; non-interest carrying changes, arb without 192–3; setting up arb 190; unwinding arb 190–2 INDEX equity in customer’s account 145, 148 equivalent annual rate (EAR) 70 equivalent martingale measures (EMMs) 507–38; arithmetic Brownian motion (ABM) model of prices 530–1; computation of EMMs 529; concept checks: contingent claim pricing, working with 514; martingale condition, calculation of 525; option pricing, working with 514; two period investment strategy under EMM, proof for (t=0) 521; solution to 538; contingent claim pricing 514–17; concept check: interpretation of pricing a European call option 514; pricing a European call option 514–15; pricing any contingent claim 515–17; current price as predictor of future stock prices 531; discounted option prices 527–8; discounted stock price process 524–5, 527–8, 530; discrete-time martingale, definition of 521; double expectations (DE) 534–5; efficient market hypotheses (EMH) basis for modeling 517; features of 532; guide to modeling prices 529–33; semi-strong form of 531; strong form of 531, 532; weak form of 531; equivalent martingale representation of stock prices 524–6; examples of EMMs 517–21; exercises for learning development of 537; fair game, notion of 518–19; fundamental theorem of asset pricing (FTAP_1) 509, 511–12, 517, 528–9, 530, 532, 533; ‘independence,’ degrees of 536; investment strategy under, twoperiod example 519–21; key concepts 537; martingale properties 533–6; nonconstructive existence theorem for 529; numeraire, concept of 524; option prices, equivalent martingale representation of 526–8; option pricing in continuous time 540; option price representation 543; physical probability measure, martingale hypothesis for 530; pricing states 509; primitive ArrowDebreu (AD) securities, option pricing and 508–14; concept check: pricing 643 ADu() and ADd() 514; exercise 1, pricing B(0,1) 510; exercise 2, pricing ADu() and ADd() 511–14; random variables 536; random walk model of prices 530–1; risk-averse investment 522; risk-neutral investment 521–2, 523; riskneutral valuation 596–7; construction of 601–3; risk premiums in stock prices and 532–3; riskless bonds 509; Sharpe ratio 526; state-contingent financial securities 508; ‘state prices’ 509; stock prices and martingales 521–6; sub (super) martingale, definition of 524; summary of EMM approach 528–9; tower property (TP) 533–4; uncorrelated martingale increments (UCMI) 531, 535–6; wealth change, fair game expectation 520 Eurodollar (ED) deposit creation 253 Eurodollar (ED) futures 220–1, 245, 246, 249, 250, 252–64; ‘buying’ and ‘selling’ futures 256; cash settlement, forced convergence and 258–61; contract specifications for 254–5; forced conversion of 260; interest-rate swaps 278; strips of 280–1; lending (offering) 249–50; liabilities and 246; open positions, calculation of profits and losses on 262–4; placing 248–9; quote mechanism 256–8; spot Eurodollar market 245–54; taking 249; timing in 257 European call options: synthesis of: modelbased option pricing (MBOP) 453–64; hedge ratio and dollar bond position, definition of (step 2) 455; implications of replication (step 4) 462–4; parameterization (step 1) 454; replicating portfolio, construction of 456–62; replication, pricing by 463; valuation at expiration 446; see also hedging a European call option in BOPM (N=2) European options 328, 333, 342, 357, 375, 398, 445, 553 European Put-Call Parity 416, 417, 418, 419, 426, 429; financial innovation with 401–5; implications of 394–400; 644 INDEX American option pricing model, analogue for European options 396–8; European call option 394–6; European option pricing model, interpretation of 397–8; European put option 398–9; synthesis of forward contracts from puts and calls 399–400 exchange membership 139–40 exchange rate risks and currency futures positions 217–20; Lufthansa example 217–20 exchange rates, New York closing snapshot (April 7, 2014) 104 exchange rule in financial futures contracts 214, 228 exchange-traded funds (ETFs) 191–2, 226 exercise of options 328 exercise price 328, 336 exercises for learning development: binomial option pricing model (BOPM) 501–5; equivalent martingale measures (EMMs) 537; financial futures contracts 266–8; hedging with forward contracts 56–61; hedging with futures contracts 205–7; interest-rate swaps 315–16; market organization for futures contracts 158–9; model-based option pricing (MBOP) 469–71; option pricing in continuous time 590–3; option trading strategies 364–6, 431–3; options markets 341–2; rational option pricing (ROP) 409–12; risk-neutral valuation 634–5; spot, forward, and futures contracting 27–9; valuation of forward contracts (assets with dividend yield) 116–17; valuation of forward contracts (assets without dividend yield) 83–5 exit mechanism in forward market contracting 15–16 exogenous variables in risk-neutral valuation 614–15 expiration date in options markets 336 expiration month code 336 fair game, notion of 518–19 fancy forward prices 19, 25 Fed Funds Rate (FFR) 251 Federal Funds (FF) 249–50, 251, 252 Federal Reserve system (US) 249 financial engineering techniques 337–8 financial futures contracts 211–70; all-orNone (AON) orders 215; Bank of International Settlements (BIS) 246; basis risk 223, 237, 238; cross hedging and 244; block trade eligibility 214, 228; block trade minimum 214, 228; commentary 216–17; concept checks: backwardation and contango, markets in?

and B 481; solving for dollar position in bonds under scenario 1 (over period 2) 483; up state, replication in 480 replication: model-based option pricing (MBOP): dynamic and static 450; hedging and 453; replicability and 449; no-arbitrage and (BOPM) 464; partial replication of European call option (embedded forward contract) 388–91; super-replication 404; valuation of forward contracts (assets without dividend yield) 77, 80; see also replicability; static replication reset date 293 resetting floating rates 293 reverse hedge 618, 620, 621 ‘reversing’ of trades 15–16 risk-adjusted discount rate (RADR) 447; valuation of forward contracts (assets with dividend yield) 94; valuation of 660 INDEX forward contracts (assets without dividend yield) 75 risk associated with long call options, neutralization of 598–9 risk aversion: hedging with forward contracts 37; risk-averse investment 522 risk cancellation condition 623 risk-free arbitrage 100, 373 risk management strategies using currency futures 217–24 risk management using stock index futures 231–45; cross-hedging 243–5; monetizing S&P 500 Spot Index 231–4; naive hedge ratio, adjustment for riskminimizing hedge ratio 239–41; non S&P 500 portfolios, adjustment of hedge for 243–5; pricing and hedging preliminaries 231; profits from traditional hedge 235–6; risk, return analysis of traditional hedge 236–8; risk-minimizing hedge using forward vs. futures contracts 241–3; risk-minimizing hedging 238–9 risk-neutral investment 521–2, 523 risk-neutral transition density function (RNTDF) 543–4, 547, 569, 570, 571; for ABM process to which GBM is reducible 569, 570; of GBM 571 risk-neutral valuation 595–635; BlackScholes’ contribution 598, 601; BOPM, exogenous variables and 615; certainty equivalent (CE) 603, 604; complete riskexpected return analysis of riskless hedge (BOPM, N=1) 607–18; direct calculation of c 612–15; direct calculation of s 611–12; expected return of hedge portfolio 616–18; hedge portfolio, percentage returns for 616–18; perfect positive correlation, statistics result of 609–11; volatility of hedge portfolio 608–11; with consensus 599; consensus and (with and without) 598–9; consumption capital asset pricing model (CCAPM) 605; convenience, riskneutral valuation by 631–2; endogenous variables 614–15; equivalent martingale measures (EMMs) 596–7; construction of 601–3; exercises for learning development of 634–5; exogenous variables 614–615; formal risk-neutral probabilities, interpretation of 603–5; formal valuation without replication 601–5; fundamental theorem of asset pricing (FTAP2), another version of 606; fundamental theorems of asset pricing (FTAP1 and FTAP2) 596–7, 601–2, 605, 606, 624, 631; general equilibrium (GE) models 615; Girsanov’s theorem 605; independent securities and risks 600; key concepts 634; law of one price (LOP) 597; marginal rate of substitution (MRS) 604, 605; market completeness 598; market price of risk (MPR) 624; equivalent martingale measures (EMMs) and 605–6; mathematical modeling 596–7; no-arbitrage assumption 596–7, 598, 602, 604, 605, 606, 608; nonhedgeable risks 599–601; nonreplicability 599–601, 603, 604, 605, 606; non-replicable contingent claims, extra risks of 600; option valuation 624–33; direct valuation by risk-averse investor 626–31; manipulations 624–6; for risk-neutral investors 631–3; partial equilibrium (PE) models 614; perfect positive correlation 609–11; physical probability, risk-neutralization of 604; preference-free risk-neutral valuation 598, 600; price contingent claims with unhedgeable risks 599–601; pricing by arbitrage and FTAP2 597–8; pricing mechanism 596; relative risks of hedge portfolio’s return, analysis of 618–24; risk-averse investor in hedge portfolio, role of risk premia for 620–4; risk neutrality in hedge portfolio, initial look at 618–20; replicability 597–8, 598, 600, 601, 603, 605, 606, 614, 615, 631, 633; reverse hedge 618, 620, 621; risk associated with long call options, neutralization of 598–9; risk cancellation condition 623; risk-neutral valuation relationship (BOPM): derivation as 467–8; interpretation of 468; risk premia INDEX 598, 603, 616, 621–2, 627, 629; risk premia, diversifiable risks and 599; risk premia cancellation condition 623–4, 628; riskless hedge 607, 616, 620, 628, 632; senses of 596; Sharpe ratio 624; equivalent martingale measures (EMMs) and 605–6; terminological navigation 596–7; unique pricing of a contingent claim 597–8; volatility risk 600; without consensus 599–601 risk premia: diversifiable risks and 599; option pricing in continuous time 554, 558, 561, 567, 588; risk-neutral valuation 598, 603, 616, 621–2, 627, 629; risk premia cancellation condition 623–4, 628; in stock prices, equivalent martingale measures (EMMs) and 532–3 risk reduction: with (-for-one) hedging 183–5; with traditional hedging 179–82; informational effect 181–2; OLS regression 181–2; portfolio variance calculation 179–81 riskless bonds 509 riskless hedge 607, 616, 620, 628, 632 rolling hedge strategy: efficient market hypothesis (EMH) 223; interpretations of profits from rolling hedge 221–3; Metallgesellschaft example 223; numerical example of 223–4 rule book chapters 228 rule of thumb 449 scenarios, hedging with forward contracts: adding profit tables to determine profits from fully hedges position 52–4; hedging with forward contracts 44–5; long position contracts 38–9; short position contracts 42 segregated consumer funds 123–5 seller’s options 17 selling forward contracts 40–1, 47–8 selling hedges 168 selling short 293 settlement prices: hedging with forward contracts 35; market organization for futures contracts 145–6, 151 settlement procedure 214, 228, 229, 258–9 661 settlement variation 146 Sharpe ratio: equivalent martingale measures (EMMs) and 526, 605–6; risk-neutral valuation and 624 shifted arithmetic Brownian motion (ABM) model of prices 541–2; reduced process 570 short a European call option on the underlying 348, 355–7; economic characteristics 357 short a European put option on the underlying 348, 359–60; economic characteristics 360 short a zero-coupon riskless bond and hold to maturity 348, 362–3; economic characteristic 363 short forward position, payoff to 39–43 short hedge 168 short positions: assumption of 147–8; forward market contracting 7; options markets 339–40 short sales, covering of 339 short the underlying 348, 349–51; economic characteristics 351 single period swaps, commodity forward contracts as 275–6 SouthWest Airlines 12–13 S&P 500 Fact Sheet 226 S&P 500 Futures 228 S&P 500 Index 88 speculation on option prices 327 spot, forward, and futures contracting 3–32; commodities, ways to buy and sell 5; concept checks: drawing conclusions from spot price charts 22–3; solution to 32; foreign currencies, forward prices on 25; foreign currencies, futures prices on 26; solution to 32; past as guide to future price behavior 21; exercises for learning development 27–9; finite-lived instruments 20; foreign currencies: forward prices on 24–5; futures prices on 25–6; foreign exchange risk 3–5; forward market contracting 7–13; futures market contracting 13–19; Gold pricing on London Bullion Market 20–3; key concepts 27; mapping out 662 INDEX prices 20–6; multi-grade spot commodities, determination of standards for pricing 23; over the counter (OTC) markets 12–13, 14, 17; randomness, state of nature and 23; spot market contracting 5–7; time lines 20, 23 spot commodities, S&P 500 futures contracts as 233–4 spot Eurodollar market 245–54; Eurodollar time deposits, creation of 252–4; spot 3-month Eurodollar time deposits 246–8; spot trading terminology 248–50 spot market contracting: cash and carry transactions 5; concept checks: exploration of spot rates in long-term mortgage market 11; solution to 29; present and future spot prices 21; solution to 32; price quotes in spot markets 6–7; solution to 29; present and future spot prices 20–3; price quotes in spot markets 6–7; spot, forward, and futures contracting 5–7; spot agreements (and terms of) 5–6; spot (cash), features of 5; spot market 6; spot mortgage market 11; spot price 6; spot transactions 6 spot prices, forward contracts and 34–5 spread basis, definition of 200–1 spreads as speculative investment 199–203 standard equity option 336 standard stock option 334 standardization, forward markets and 14 state-contingent financial securities 508 static replication: European Put-Call Parity (no dividends) and 388–94; current costs and related strategies, technique of going back and forth between 393; fully replicating European call option (embedded insurance contract) 391–2; partially replicating European call option (embedded forward contract) 388–91; working backwards from payoffs to costs to derive European Put-Call Parity 393–4; principle of 393–4 Stigum’s Money Market (Stigum, M.) 252 stochastic differential equations (SDEs) 553, 559, 562–3, 564, 566, 567–8, 570, 571, 583 stochastic integral equations (SIEs) 559, 560, 561, 564, 565–6, 567 stochastic processes 540–1, 543, 562, 587, 588 stock forwards when stock pays dividends 88–90 stock index futures 225–30; commentary 230; futures contracts, introduction of 167; S&P 500 futures quotes, quote mechanism for 230; S&P 500 Spot Index 225–7; effective payoff on monetization of 233; monetization of 231–4; S&P 500 Stock Index Futures Contract Specifications 227–9 stock price evolution (BOPM): for binomial process (N=2) 440; for N-period binomial process, summary of 444–5, 499; number of price paths 441; number of price paths ending at specific prices 443 stock price tree 488 stock prices: affect of capital gains on 98–9; affect of dividend payments on 94–8; martingales and 521–6 stock returns, modeling with and without dividends 109–15 storage and price (cost) of 195–7 strategic, option-like scenarios 324 strike price 328 strike price code 336 strip cash flows, generation of 277 strips of forward contracts 277–8 sub-replication 404 sub (super) martingale, definition of 524 subsequent inventory sale price, locking in of 195 super-replication 404 swap cash flows: decomposition into implicit bonds 303; graphical representation of 318 swap spread 294 swapping fixed for floating payments 276 swaps as strips of forward contracts 274–8 INDEX swaps pricing 301–14; example of 301–3; fixed-rate bond, valuation of 303–5; floating-rate bond, valuation of 305–8; implied forward rates (IFRs) 309–11; par swap rate 301; interpretations of 311–14; swap at initiation, valuation of 308–9 synthesis of negative correlation, hedging as 165–7 synthetic equivalents on basic (naked) strategies 416–18 synthetic fixed-rate bonds 291–2 synthetic fixed-rate financing 290 synthetic risk, diversifying away of 167 synthetic strategies, natural strategies and 416 synthetic treasury bill vs. actual bill 165 systematic, market risk after diversification, protection against 168 tailing the hedge 241–2 tenor of swap 293 terminological navigation 596–7; interestrate swaps 278–81, 293–4 tick size 228, 229 ticker symbol 214, 215, 228, 229, 261 time in discrete time framework, modeling of 437–8 time lines 20, 23 time premia 326, 330–1, 333, 337 total stock process with dividends (before dividends are paid) 110 total stock return process 98–9 tower property (TP) 533–4 tracking equity in investor’s accounts 151–3 trade date 293 trading futures contracts, questions on organizational structures for 141 trading hours 214, 228 traditional; hedge, risk and return analysis on 236–8; basis risk 238; holding period rate 237; intermediate execution, basis risk and 237–8; liquidity advantage in execution 237 transfer of obligations 16 transition density function for shifted arithmetic Brownian motion 545–6 663 transportation across time, storage as 195 treasury bill synthesis 166–7 trinomial model (three stock outcomes) 464 turning points 22 unallocated foreign exchange (FX) reserves 248 uncertainty (volatility): naked (unhedged) positions and 45; see also volatility (uncertainty) 45 uncorrelated martingale increments (UCMI) 531, 535–6 underlying assets or scenarios 327, 334; identification of long and short positions in 339–40 underlying stock price uncertainty, modeling of 438–40 unique pricing of a contingent claim 597–8 valuation of floating-rate bonds prior to maturity 306–7 valuation of forward contracts (assets with dividend yield) 87–119; annualized dividend yields 88; arbitrage definitions 100–2; Black-Scholes option pricing model 88; capital gains, affect on stock prices 98–9; capital gains process 98–9, 111; concept checks: arbitrage opportunities, working with 101–2; solution 118–19; calculation of total stock price return minus dividend yield 99; solution 118; direct and indirect costs 89; solution 117–18; modeling continuous dividend yields for stocks 94; modeling continuous dividend yields for stocks: solution 118; modeling zerocoupon bond yields 92; pricing currencies forwards 105; solution 119; pricing foreign exchange contracts 106; stock price, effect of dividend payments on 97; continuous dividends from stocks, modeling yields from 93–4; continuous yields, modeling of 90–4; contract life, payments over 88; convenience yield 89; cost of carry 89; currency spot and 664 INDEX currency forwards 103–9; dividend payments, affect on stock prices 94–8; dividend payout process 97; connection between capital gains process and 111–13; domestic economy (DE) 103–4, 105; exchange rates, New York closing snapshot (April 7, 2014) 104; exercises for learning development of 116–17; foreign economy (FE) 103–4; foreign exchange (FX) forward contracts: example of pricing 107–9; pricing using no-arbitrage 106–7; foreign exchange (FX) markets, price quotes in 103–5; forward contracts on dividend-paying stocks, pricing with no-arbitrage 100–3; forward contracts on stocks with dividend yield, pricing with net interest model 99–100; forward pricing using no-arbitrage 102–3; infinitesimal intervals 93; instantaneous yields 90–2, 93–4; key concepts 116; Log Bond equation 96; net interest model 99–100; non-stochastic differential equations 90–4; present value (PV) 94; pricing currency forwards 105; pricing foreign exchange forward contracts using noarbitrage 106–7; risk-adjusted discount rate (RADR) 94; S&P 500 Index 88; stock forwards when stock pays dividends 88–90; stock prices: affect of capital gains on 98–9; affect of dividend payments on 94–8; stock returns, modeling with and without dividends 109–15; total stock process with dividends (before dividends are paid) 110; total stock return process 98–9; zero-coupon bonds, modeling yields from 90–2 valuation of forward contracts (assets without dividend yield) 65–86; arbitrage opportunities 75; commitment to buy 67; concept checks: annualized, continuously compounded 3%, worth after 2 months?


pages: 349 words: 134,041

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

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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

3 Mean/variance – the risk of financial markets is reduced to two statistics: mean (average) return and variability of the returns, standard deviation or variance as measure of volatility. For investors, the wilder the swings in price, the higher the risk. Risk is now a known known. No one told risk. There is the unknown unknown – pure uncertainty, things that never happened before. 4 Risk/reward – William Sharpe, John Lintner and Jack Treynor showed, using the CAPM (capital asset price model), that risk and return were related. If you took more risk then you needed higher returns. Old time investors wept with joy. They had been doing CAPM without knowing it. Fund managers had their own papal fashions, known as investment styles: see Table 3.1. Table 3.1 N Investment styles Style What is it? What does it mean? Index funds The fund manager invests to match some index like the S & P 500.

However, the text is different. 6 ‘What Worries Warren’ (3 March 2003) Fortune. 13_INDEX.QXD 17/2/06 4:44 pm Page 325 Index accounting rules 139, 221, 228, 257 Accounting Standards Board 33 accrual accounting 139 active fund management 111 actuaries 107–10, 205, 289 Advance Corporation Tax 242 agency business 123–4, 129 agency theory 117 airline profits 140–1 Alaska 319 Allen, Woody 20 Allied Irish Bank 143 Allied Lyons 98 alternative investment strategies 112, 308 American Express 291 analysts, role of 62–4 anchor effect 136 Anderson, Rolf 92–4 annuities 204–5 ANZ Bank 277 Aquinas, Thomas 137 arbitrage 33, 38–40, 99, 114, 137–8, 171–2, 245–8, 253–5, 290, 293–6 arbitration 307 Argentina 45 arithmophobia 177 ‘armpit theory’ 303 Armstrong World Industries 274 arrears assets 225 Ashanti Goldfields 97–8, 114 Asian financial crisis (1997) 4, 9, 44–5, 115, 144, 166, 172, 207, 235, 245, 252, 310, 319 asset consultants 115–17, 281 ‘asset growth’ strategy 255 asset swaps 230–2 assets under management (AUM) 113–4, 117 assignment of loans 267–8 AT&T 275 attribution of earnings 148 auditors 144 Australia 222–4, 254–5, 261–2 back office functions 65–6 back-to-back loans 35, 40 backwardation 96 Banca Popolare di Intra 298 Bank of America 298, 303 Bank of International Settlements 50–1, 281 Bank of Japan 220 Bankers’ Trust (BT) 59, 72, 101–2, 149, 217–18, 232, 268–71, 298, 301, 319 banking regulations 155, 159, 162, 164, 281, 286, 288 banking services 34; see also commercial banks; investment banks bankruptcy 276–7 Banque Paribas 37–8, 232 Barclays Bank 121–2, 297–8 13_INDEX.QXD 17/2/06 326 4:44 pm Page 326 Index Baring, Peter 151 Baring Brothers 51, 143, 151–2, 155 ‘Basel 2’ proposal 159 basis risk 28, 42, 274 Bear Stearns 173 bearer eurodollar collateralized securities (BECS) 231–3 ‘behavioural finance’ 136 Berkshire Hathaway 19 Bermudan options 205, 227 Bernstein, Peter 167 binomial option pricing model 196 Bismarck, Otto von 108 Black, Fischer 22, 42, 160, 185, 189–90, 193, 195, 197, 209, 215 Black–Scholes formula for option pricing 22, 185, 194–5 Black–Scholes–Merton model 160, 189–93, 196–7 ‘black swan’ hypothesis 130 Blair, Tony 223 Bogle, John 116 Bohr, Niels 122 Bond, Sir John 148 ‘bond floor’ concept 251–4 bonding 75–6, 168, 181 bonuses 146–51, 244, 262, 284–5 Brady Commission 203 brand awareness and brand equity 124, 236 Brazil 302 Bretton Woods system 33 bribery 80, 303 British Sky Broadcasting (BSB) 247–8 Brittain, Alfred 72 broad index secured trust offerings (BISTROs) 284–5 brokers 69, 309 Brown, Robert 161 bubbles 210, 310, 319 Buconero 299 Buffet, Warren 12, 19–20, 50, 110–11, 136, 173, 246, 316 business process reorganization 72 business risk 159 Business Week 130 buy-backs 249 ‘call’ options 25, 90, 99, 101, 131, 190, 196 callable bonds 227–9, 256 capital asset pricing model (CAPM) 111 capital flow 30 capital guarantees 257–8 capital structure arbitrage 296 Capote, Truman 87 carbon trading 320 ‘carry cost’ model 188 ‘carry’ trades 131–3, 171 cash accounting 139 catastrophe bonds 212, 320 caveat emptor principle 27, 272 Cayman Islands 233–4 Cazenove (company) 152 CDO2 292 Cemex 249–50 chaos theory 209, 312 Chase Manhattan Bank 143, 299 Chicago Board Options Exchange 195 Chicago Board of Trade (CBOT) 25–6, 34 chief risk officers 177 China 23–5, 276, 302–4 China Club, Hong Kong 318 Chinese walls 249, 261, 280 chrematophobia 177 Citibank and Citigroup 37–8, 43, 71, 79, 94, 134–5, 149, 174, 238–9 Citron, Robert 124–5, 212–17 client relationships 58–9 Clinton, Bill 223 Coats, Craig 168–9 collateral requirements 215–16 collateralized bond obligations (CBOs) 282 collateralized debt obligations (CDOs) 45, 282–99 13_INDEX.QXD 17/2/06 4:44 pm Page 327 Index collateralized fund obligations (CFOs) 292 collateralized loan obligations (CLOs) 283–5, 288 commercial banks 265–7 commoditization 236 commodity collateralized obligations (CCOs) 292 commodity prices 304 Commonwealth Bank of Australia 255 compliance officers 65 computer systems 54, 155, 197–8 concentration risk 271, 287 conferences with clients 59 confidence levels 164 confidentiality 226 Conseco 279–80 contagion crises 291 contango 96 contingent conversion convertibles (co-cos) 257 contingent payment convertibles (co-pays) 257 Continental Illinois 34 ‘convergence’ trading 170 convertible bonds 250–60 correlations 163–6, 294–5; see also default correlations corruption 303 CORVUS 297 Cox, John 196–7 credit cycle 291 credit default swaps (CDSs) 271–84, 293, 299 credit derivatives 129, 150, 265–72, 282, 295, 299–300 Credit Derivatives Market Practices Committee 273, 275, 280–1 credit models 294, 296 credit ratings 256–7, 270, 287–8, 297–8, 304 credit reserves 140 credit risk 158, 265–74, 281–95, 299 327 credit spreads 114, 172–5, 296 Credit Suisse 70, 106, 167 credit trading 293–5 CRH Capital 309 critical events 164–6 Croesus 137 cross-ruffing 142 cubic splines 189 currency options 98, 218, 319 custom repackaged asset vehicles (CRAVEs) 233 daily earning at risk (DEAR) concept 160 Daiwa Bank 142 Daiwa Europe 277 Danish Oil and Natural Gas 296 data scrubbing 142 dealers, work of 87–8, 124–8, 133, 167, 206, 229–37, 262, 295–6; see also traders ‘death swap’ strategy 110 decentralization 72 decision-making, scientific 182 default correlations 270–1 defaults 277–9, 287, 291, 293, 296, 299 DEFCON scale 156–7 ‘Delta 1’ options 243 delta hedging 42, 200 Deming, W.E. 98, 101 Denmark 38 deregulation, financial 34 derivatives trading 5–6, 12–14, 18–72, 79, 88–9, 99–115, 123–31, 139–41, 150, 153, 155, 175, 184–9, 206–8, 211–14, 217–19, 230, 233, 257, 262–3, 307, 316, 319–20; see also equity derivatives Derman, Emmanuel 185, 198–9 Deutsche Bank 70, 104, 150, 247–8, 274, 277 devaluations 80–1, 89, 203–4, 319 13_INDEX.QXD 17/2/06 4:44 pm Page 328 328 Index dilution of share capital 241 DINKs 313 Disney Corporation 91–8 diversification 72, 110–11, 166, 299 dividend yield 243 ‘Dr Evil’ trade 135 dollar premium 35 downsizing 73 Drexel Burnham Lambert (DBL) 282 dual currency bonds 220–3; see also reverse dual currency bonds earthquakes, bonds linked to 212 efficient markets hypothesis 22, 31, 111, 203 electronic trading 126–30, 134 ‘embeddos’ 218 emerging markets 3–4, 44, 115, 132–3, 142, 212, 226, 297 Enron 54, 142, 250, 298 enterprise risk management (ERM) 176 equity capital management 249 equity collateralized obligations (ECOs) 292 equity derivatives 241–2, 246–9, 257–62 equity index 137–8 equity investment, retail market in 258–9 equity investors’ risk 286–8 equity options 253–4 equity swaps 247–8 euro currency 171, 206, 237 European Bank for Reconstruction and Development 297 European currency units 93 European Union 247–8 Exchange Rate Mechanism, European 204 exchangeable bonds 260 expatriate postings 81–2 expert witnesses 310–12 extrapolation 189, 205 extreme value theory 166 fads of management science 72–4 ‘fairway bonds’ 225 Fama, Eugene 22, 111, 194 ‘fat tail’ events 163–4 Federal Accounting Standards Board 266 Federal Home Loans Bank 213 Federal National Mortgage Association 213 Federal Reserve Bank 20, 173 Federal Reserve Board 132 ‘Ferraris’ 232 financial engineering 228, 230, 233, 249–50, 262, 269 Financial Services Authority (FSA), Japan 106, 238 Financial Services Authority (FSA), UK 15, 135 firewalls 235–6 firing of staff 84–5 First Interstate Ltd 34–5 ‘flat’ organizations 72 ‘flat’ positions 159 floaters 231–2; see also inverse floaters ‘flow’ trading 60–1, 129 Ford Motors 282, 296 forecasting 135–6, 190 forward contracts 24–33, 90, 97, 124, 131, 188 fugu fish 239 fund management 109–17, 286, 300 futures see forward contracts Galbraith, John Kenneth 121 gamma risk 200–2, 294 Gauss, Carl Friedrich 160–2 General Motors 279, 296 General Reinsurance 20 geometric Brownian motion (GBM) 161 Ghana 98 Gibson Greeting Cards 44 Glass-Steagall Act 34 gold borrowings 132 13_INDEX.QXD 17/2/06 4:44 pm Page 329 Index gold sales 97, 137 Goldman Sachs 34, 71, 93, 150, 173, 185 ‘golfing holiday bonds’ 224 Greenspan, Alan 6, 9, 19–21, 29, 43, 47, 50, 53, 62, 132, 159, 170, 215, 223, 308 Greenwich NatWest 298 Gross, Bill 19 Guangdong International Trust and Investment Corporation (GITIC) 276–7 guaranteed annuity option (GAO) contracts 204–5 Gutenfreund, John 168–9 gyosei shido 106 Haghani, Victor 168 Hamanaka, Yasuo 142 Hamburgische Landesbank 297 Hammersmith and Fulham, London Borough of 66–7 ‘hara-kiri’ swaps 39 Hartley, L.P. 163 Hawkins, Greg 168 ‘heaven and hell’ bonds 218 hedge funds 44, 88–9, 113–14, 167, 170–5, 200–2, 206, 253–4, 262–3, 282, 292, 296, 300, 308–9 hedge ratio 264 hedging 24–8, 31, 38–42, 60, 87–100, 184, 195–200, 205–7, 214, 221, 229, 252, 269, 281, 293–4, 310 Heisenberg, Werner 122 ‘hell bonds’ 218 Herman, Clement (‘Crem’) 45–9, 77, 84, 309 Herodotus 137, 178 high net worth individuals (HNWIs) 237–8, 286 Hilibrand, Lawrence 168 Hill Samuel 231–2 329 The Hitchhiker’s Guide to the Galaxy 189 Homer, Sidney 184 Hong Kong 9, 303–4 ‘hot tubbing’ 311–12 HSBC Bank 148 HSH Nordbank 297–8 Hudson, Kevin 102 Hufschmid, Hans 77–8 IBM 36, 218, 260 ICI 34 Iguchi, Toshihude 142 incubators 309 independent valuation 142 indexed currency option notes (ICONs) 218 India 302 Indonesia 5, 9, 19, 26, 55, 80–2, 105, 146, 219–20, 252, 305 initial public offerings 33, 64, 261 inside information and insider trading 133, 241, 248–9 insurance companies 107–10, 117, 119, 150, 192–3, 204–5, 221, 223, 282, 286, 300; see also reinsurance companies insurance law 272 Intel 260 intellectual property in financial products 226 Intercontinental Hotels Group (IHG) 285–6 International Accounting Standards 33 International Securities Market Association 106 International Swap Dealers Association (ISDA) 273, 275, 279, 281 Internet stock and the Internet boom 64, 112, 259, 261, 310, 319 interpolation of interest rates 141–2, 189 inverse floaters 46–51, 213–16, 225, 232–3 13_INDEX.QXD 17/2/06 4:44 pm Page 330 330 Index investment banks 34–8, 62, 64, 67, 71, 127–8, 172, 198, 206, 216–17, 234, 265–7, 298, 309 investment managers 43–4 investment styles 111–14 irrational decisions 136 Italy 106–7 Ito’s Lemma 194 Japan 39, 43, 82–3, 92, 94, 98–9, 101, 106, 132, 142, 145–6, 157, 212, 217–25, 228, 269–70 Jensen, Michael 117 Jett, Joseph 143 JP Morgan (company) 72, 150, 152, 160, 162, 249–50, 268–9, 284–5, 299; see also Morgan Guaranty junk bonds 231, 279, 282, 291, 296–7 JWM Associates 175 Kahneman, Daniel 136 Kaplanis, Costas 174 Kassouf, Sheen 253 Kaufman, Henry 62 Kerkorian, Kirk 296 Keynes, J.M. 167, 175, 198 Keynesianism 5 Kidder Peabody 143 Kleinwort Benson 40 Korea 9, 226, 278 Kozeny, Viktor 121 Krasker, William 168 Kreiger, Andy 319 Kyoto Protocol 320 Lavin, Jack 102 law of large numbers 192 Leeson, Nick 51, 131, 143, 151 legal opinions 47, 219–20, 235, 273–4 Leibowitz, Martin 184 Leland, Hayne 42, 202 Lend Lease Corporation 261–2 leptokurtic conditions 163 leverage 31–2, 48–50, 54, 99, 102–3, 114, 131–2, 171–5, 213–14, 247, 270–3, 291, 295, 305, 308 Lewis, Kenneth 303 Lewis, Michael 77–8 life insurance 204–5 Lintner, John 111 liquidity options 175 liquidity risk 158, 173 litigation 297–8 Ljunggren, Bernt 38–40 London Inter-Bank Offered Rate (LIBOR) 6, 37 ‘long first coupon’ strategy 39 Long Term Capital Management (LTCM) 44, 51, 62, 77–8, 84, 114, 166–75, 187, 206, 210, 215–18, 263–4, 309–10 Long Term Credit Bank of Japan 94 LOR (company) 202 Louisiana Purchase 319 low exercise price options (LEPOs) 261 Maastricht Treaty and criteria 106–7 McLuhan, Marshall 134 McNamara, Robert 182 macro-economic indicators, derivatives linked to 319 Mahathir Mohammed 31 Malaysia 9 management consultants 72–3 Manchester United 152 mandatory convertibles 255 Marakanond, Rerngchai 302 margin calls 97–8, 175 ‘market neutral’ investment strategy 114 market risk 158, 173, 265 marketable eurodollar collateralized securities (MECS) 232 Markowitz, Harry 110 mark-to-market accounting 10, 100, 139–41, 145, 150, 174, 215–16, 228, 244, 266, 292, 295, 298 Marx, Groucho 24, 57, 67, 117, 308 13_INDEX.QXD 17/2/06 4:44 pm Page 331 Index mathematics applied to financial instruments 209–10; see also ‘quants’ matrix structures 72 Meckling, Herbert 117 Melamed, Leo 34, 211 merchant banks 38 Meriwether, John 167–9, 172–5 Merrill Lynch 124, 150, 217, 232 Merton, Robert 22, 42, 168–70, 175, 185, 189–90, 193–7, 210 Messier, Marie 247 Metallgesellschaft 95–7 Mexico 44 mezzanine finance 285–8, 291–7 MG Refining and Marketing 95–8, 114 Microsoft 53 Mill, Stuart 130 Miller, Merton 22, 101, 194 Milliken, Michael 282 Ministry of Finance, Japan 222 misogyny 75–7 mis-selling 238, 297–8 Mitchell, Edison 70 Mitchell & Butler 275–6 models financial 42–3, 141–2, 163–4, 173–5, 181–4, 189, 198–9, 205–10 of business processes 73–5 see also credit models Modest, David 168 momentum investment 111 monetization 260–1 monopolies in financial trading 124 moral hazard 151, 280, 291 Morgan Guaranty 37–8, 221, 232 Morgan Stanley 76, 150 mortgage-backed securities (MBSs) 282–3 Moscow, City of 277 moves of staff between firms 150, 244 Mozer, Paul 169 Mullins, David 168–70 multi-skilling 73 331 Mumbai 3 Murdoch, Rupert 247 Nabisco 220 Napoleon 113 NASDAQ index 64, 112 Nash, Ogden 306 National Australia Bank 144, 178 National Rifle Association 29 NatWest Bank 144–5, 198 Niederhoffer, Victor 130 ‘Nero’ 7, 31, 45–9, 60, 77, 82–3, 88–9, 110, 118–19, 125, 128, 292 NERVA 297 New Zealand 319 Newman, Frank 104 news, financial 133–4 News Corporation 247 Newton, Isaac 162, 210 Nippon Credit Bank 106, 271 Nixon, Richard 33 Nomura Securities 218 normal distribution 160–3, 193, 199 Northern Electric 248 O’Brien, John 202 Occam, William 188 off-balance sheet transactions 32–3, 99, 234, 273, 282 ‘offsites’ 74–5 oil prices 30, 33, 89–90, 95–7 ‘omitted variable’ bias 209–10 operational risk 158, 176 opinion shopping 47 options 9, 21–2, 25–6, 32, 42, 90, 98, 124, 197, 229 pricing 185, 189–98, 202 Orange County 16, 44, 50, 124–57, 212–17, 232–3 orphan subsidiaries 234 over-the-counter (OTC) market 26, 34, 53, 95, 124, 126 overvaluation 64 13_INDEX.QXD 17/2/06 4:44 pm Page 332 332 Index ‘overwhelming force’ strategy 134–5 Owen, Martin 145 ownership, ‘legal’ and ‘economic’ 247 parallel loans 35 pari-mutuel auction system 319 Parkinson’s Law 136 Parmalat 250, 298–9 Partnoy, Frank 87 pension funds 43, 108–10, 115, 204–5, 255 People’s Bank of China (PBOC) 276–7 Peters’ Principle 71 petrodollars 71 Pétrus (restaurant) 121 Philippines, the 9 phobophobia 177 Piga, Gustavo 106 PIMCO 19 Plaza Accord 38, 94, 99, 220 plutophobia 177 pollution quotas 320 ‘portable alpha’ strategy 115 portfolio insurance 112, 202–3, 294 power reverse dual currency (PRDC) bonds 226–30 PowerPoint 75 preferred exchangeable resettable listed shares (PERLS) 255 presentations of business models 75 to clients 57, 185 prime brokerage 309 Prince, Charles 238 privatization 205 privity of contract 273 Proctor & Gamble (P&G) 44, 101–4, 155, 298, 301 product disclosure statements (PDSs) 48–9 profit smoothing 140 ‘programme’ issuers 234–5 proprietary (‘prop’) trading 60, 62, 64, 130, 174, 254 publicly available information (PAI) 277 ‘puff’ effect 148 purchasing power parity theory 92 ‘put’ options 90, 131, 256 ‘quants’ 183–9, 198, 208, 294 Raabe, Matthew 217 Ramsay, Gordon 121 range notes 225 real estate 91, 219 regulatory arbitrage 33 reinsurance companies 288–9 ‘relative value’ trading 131, 170–1, 310 Reliance Insurance 91–2 repackaging (‘repack’) business 230–6, 282, 290 replication in option pricing 195–9, 202 dynamic 200 research provided to clients 58, 62–4, 184 reserves, use of 140 reset preference shares 254–7 restructuring of loans 279–81 retail equity products 258–9 reverse convertibles 258–9 reverse dual currency bonds 223–30 ‘revolver’ loans 284–5 risk, financial, types of 158 risk adjusted return on capital (RAROC) 268, 290 risk conservation principle 229–30 risk management 65, 153–79, 184, 187, 201, 267 risk models 163–4, 173–5 riskless portfolios 196–7 RJ Reynolds (company) 220–1 rogue traders 176, 313–16 Rosenfield, Eric 168 Ross, Stephen 196–7, 202 Roth, Don 38 Rothschild, Mayer Amshel 267 Royal Bank of Scotland 298 Rubinstein, Mark 42, 196–7 13_INDEX.QXD 17/2/06 4:44 pm Page 333 Index Rumsfeld, Donald 12, 134, 306 Rusnak, John 143 Russia 45, 80, 166, 172–3, 274, 302 sales staff 55–60, 64–5, 125, 129, 217 Salomon Brothers 20, 36, 54, 62, 167–9, 174, 184 Sandor, Richard 34 Sanford, Charles 72, 269 Sanford, Eugene 269 Schieffelin, Allison 76 Scholes, Myron 22, 42, 168–71, 175, 185, 189–90, 193–7, 263–4 Seagram Group 247 Securities and Exchange Commission, US 64, 304 Securities and Futures Authority, UK 249 securitization 282–90 ‘security design’ 254–7 self-regulation 155 sex discrimination 76 share options 250–1 Sharpe, William 111 short selling 30–1, 114 Singapore 9 single-tranche CDOs 293–4, 299 ‘Sisters of Perpetual Ecstasy’ 234 SITCOMs 313 Six Continents (6C) 275–6 ‘smile’ effect 145 ‘snake’ currency system 203 ‘softing’ arrangements 117 Solon 137 Soros, George 44, 130, 253, 318–19 South Sea Bubble 210 special purpose asset repackaging companies (SPARCs) 233 special purpose vehicles (SPVs) 231–4, 282–6, 290, 293 speculation 29–31, 42, 67, 87, 108, 130 ‘spinning’ 64 333 Spitzer, Eliot 64 spread 41, 103; see also credit spreads stack hedges 96 Stamenson, Michael 124–5 standard deviation 161, 193, 195, 199 Steinberg, Sol 91 stock market booms 258, 260 stock market crashes 42–3, 168, 203, 257, 259, 319 straddles or strangles 131 strategy in banking 70 stress testing 164–6 stripping of convertible bonds 253–4 structured investment products 44, 112, 115, 118, 128, 211–39, 298 structured note asset packages (SNAPs) 233 Stuart SC 18, 307, 316–18 Styblo Bleder, Tanya 153 Suharto, Thojib 81–2 Sumitomo Corporation 100, 142 Sun Tzu 61 Svensk Exportkredit (SEK) 38–9 swaps 5–10, 26, 35–40, 107, 188, 211; see also equity swaps ‘swaptions’ 205–6 Swiss Bank Corporation (SBC) 248–9 Swiss banks 108, 305 ‘Swiss cheese theory’ 176 synthetic securitization 284–5, 288–90 systemic risk 151 Takeover Panel 248–9 Taleb, Nassim 130, 136, 167 target redemption notes 225–6 tax and tax credits 171, 242–7, 260–3 Taylor, Frederick 98, 101 team-building exercises 76 team moves 149 technical analysis 60–1, 135 television programmes about money 53, 62–3 Thailand 9, 80, 302–5 13_INDEX.QXD 17/2/06 4:44 pm Page 334 334 Index Thatcher, Margaret 205 Thorp, Edward 253 tobashi trades 105–7 Tokyo Disneyland 92, 212 top managers 72–3 total return swaps 246–8, 269 tracking error 138 traders in financial products 59–65, 129–31, 135–6, 140, 148, 151, 168, 185–6, 198; see also dealers trading limits 42, 157, 201 trading rooms 53–4, 64, 68, 75–7, 184–7, 208 Trafalgar House 248 tranching 286–9, 292, 296 transparency 26, 117, 126, 129–30, 310 Treynor, Jack 111 trust investment enhanced return securities (TIERS) 216, 233 trust obligation participating securities (TOPS) 232 TXU Europe 279 UBS Global Asset Management 110, 150, 263–4, 274 uncertainty principle 122–3 unique selling propositions 118 unit trusts 109 university education 187 unspecified fund obligations (UFOs) 292 ‘upfronting’ of income 139, 151 Valéry, Paul 163 valuation 64, 142–6 value at risk (VAR) concept 160–7, 173 value investing 111 Vanguard 116 vanity bonds 230 variance 161 Vietnam War 182, 195 Virgin Islands 233–4 Vivendi 247–8 volatility of bond prices 197 of interest rates 144–5 of share prices 161–8, 172–5, 192–3, 199 Volcker, Paul 20, 33 ‘warehouses’ 40–2, 139 warrants arbitrage 99–101 weather, bonds linked to 212, 320 Weatherstone, Dennis 72, 268 Weil, Gotscal & Manges 298 Weill, Sandy 174 Westdeutsche Genosenschafts Zentralbank 143 Westminster Group 34–5 Westpac 261–2 Wheat, Allen 70, 72, 106, 167 Wojniflower, Albert 62 World Bank 4, 36, 38 World Food Programme 320 Worldcom 250, 298 Wriston, Walter 71 WTI (West Texas Intermediate) contracts 28–30 yield curves 103, 188–9, 213, 215 yield enhancement 112, 213, 269 ‘yield hogs’ 43 zaiteku 98–101, 104–5 zero coupon bonds 221–2, 257–8


pages: 442 words: 39,064

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

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

A simple, economically plausible mathematical approach to market modeling is needed which captures the essence of reality. The existing approaches to financial market modeling are quite diverse, and the literature is rather extensive. Significant progress in our understanding of financial markets was acquired, for instance, by Markowitz with the mean-variance portfolio theory [288], the capital asset pricing model of Sharpe [370] and its elaboration by Lintner, Merton’s [293] and Black and Scholes’s option pricing and hedging theory [41], Ross’s arbitrage pricing theory [353], and Cox, Ingersoll, and Ross’s theory of interest rates [95], to cite a few of the major advances. Economic models differ from models in the physical sciences in that economic agents are supposed to anticipate the future.

Herding and Delegated Portfolio Management: The Impact of Relative Performance Evaluation on Asset Allocation, Working paper, Duke University, Durham, NC. 291. McCarty, P. A. (1986). Effects of feedback on the self-confidence of men and women, Academy of Management Journal 29, 840–847. 292. Meakin, P. (1998). Fractals, Scaling, and Growth Far from Equilibrium (Cambridge University Press, Cambridge, U.K. and New York). 293. Merton, R. (1973). An intertemporal capital asset pricing model, Econometrica 41, 867–888. 294. Merton, R. C. (1990). Continuous-Time Finance (Blackwell, Cambridge, U.K.). 295. Meyer, F. (1947). L’accélération évolutive. Essai sur le rythme évolutif et son interprétation quantique (Librairie des Sciences et des Arts, Paris). 296. Meyer, F. (1954). Problématique de l’évolution (Presses Universitaires de France, Paris). 297. Milgram, S. (1967). The small world problem, Psychology Today 2, 60–67. 298.


pages: 444 words: 151,136

Endless Money: The Moral Hazards of Socialism by William Baker, Addison Wiggin

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Andy Kessler, asset allocation, backtesting, bank run, banking crisis, Berlin Wall, Bernie Madoff, Black Swan, Branko Milanovic, break the buck, Bretton Woods, BRICs, business climate, capital asset pricing model, commoditize, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, crony capitalism, cuban missile crisis, currency manipulation / currency intervention, debt deflation, Elliott wave, en.wikipedia.org, Fall of the Berlin Wall, feminist movement, fiat currency, fixed income, floating exchange rates, Fractional reserve banking, full employment, German hyperinflation, housing crisis, income inequality, index fund, inflation targeting, Joseph Schumpeter, laissez-faire capitalism, land reform, liquidity trap, Long Term Capital Management, McMansion, mega-rich, money market fund, moral hazard, mortgage tax deduction, naked short selling, negative equity, offshore financial centre, Ponzi scheme, price stability, pushing on a string, quantitative easing, RAND corporation, rent control, reserve currency, riskless arbitrage, Ronald Reagan, school vouchers, seigniorage, short selling, Silicon Valley, six sigma, statistical arbitrage, statistical model, Steve Jobs, The Great Moderation, the scientific method, time value of money, too big to fail, upwardly mobile, War on Poverty, Yogi Berra, young professional

This work also finds that a system, such as gold, that limits monetary growth would result in deflation, instability, and sub-optimal economic growth. We have a centrally managed fiat currency by choice. It ensures a small, manageable amount of inflation, and isolates control of the quantity of money in the hands of a benevolent few who possess the highest degree of understanding of its operation. But there is a side effect: It presents an enticing one-way bet to profit from the use of debt. Known to all students of finance is the Capital Asset Pricing Model (CAPM), which states there is an optimal mixture of equity and debt in investment decisions. Although as a theory it holds appeal, in practice the lure of leverage, which exists even in monetary systems that are not established by fiat or lack central control, is powerful. The record shows that neither is there long-term correlation between interest rates and Flat-Earth Economics 73 inflation, nor between interest rates and money supply growth.

See Hard money Bank of America, 142, 211, 212 Bank of United States, 56 BankOne, 212 Barca, Hamilcar, 246 Barnes, Lou, 9 BB&T, 124 Bear Stearns, 122, 152, 155, 329 Belfer, Lauren, 307 Berkshire Hathaway, 171 Bernanke, Ben, 71, 72, 94, 117–119, 121, 128, 129, 133–134 409 410 Beverly, Louis, 148 Bhirud, Suresh, 12–13 Bipartisan Campaign Reform Act of 2002, 182 Birnbaum, Jeff, 184 Blackstone group, 336 The Black Swan (Taleb), 15, 28, 280 Blueprint for Change, 226 Bodenhorn, Howard, 47, 49 Bonner, Bill, 235–236 Booms and Depression (Fisher), 131 Bordo, Michael, 112 “Born Rich,” 174 Boyd, Joe, 303 Brady, Robert, 178 Bretton Woods Agreement of 1944, 67, 89, 106, 112, 348, 349, 353 Brin, Sergey, 171 Bryan, William Jennings, 54 Buffett, Warren, 171, 174, 198, 336, 338–339, 350, 351 Burnham, James, 291 Bush, George H. W., 201 Bush, George W., 202, 339–340 Business as a System of Power (Brady), 178 Butler, Robert, 335 Butler, Smedley, 86–88 Byrne, Patrick, 140 Caesar, Julius, 248 Caligula, 258–259 Calomiris, Charles, 212 Campos, Roel, 329 Capital Asset Pricing Model (CAPM), 72 Capitalism: democracy as threat: and economic stress, 233–235 overview, 232–233 Roman Empire as example, 235–236 evolution of, 241–243 INDEX Roman Empire as example building wealth, 243–245 collectivism, 252–256 money supply, 245–249 squandering wealth, 249–252 See also Roman Empire, decline of Carnegie Foundation, 178 Carter, Jimmy, 221, 230–231 The Case Against the Fed (Rothbard), 354 Cassidy, John, 118, 119 Cato Institute, 336 Catsoulis, Jeannette, 335 Center for a Responsible Budget (CFRB), 226–227 Center for Responsible Politics (CRP), 184–185, 186 Chavez, Hugo, 365 Chemical Bank, 55, 56 Choudhri, Ehsan, 94–95, 96, 98, 99 Chrysler, 235 Citibank, 124 Citicorp, 211 Citigroup, 57 Clifford Chance LLP, 318 Clinton, Bill, 186, 201, 330, 340 Clinton, Hilary, 202 Coleman, Thomas, 311 Community Reinvestment Act (CRA), 127, 147, 149, 212 Conant, Charles, 62, 280 Conda, Cesar, 160 Considius, Q., 248 Conspiracy of Fools (Eichenwald), 223 Council on Foundations, 176 Counterfeiting, 38, 53 Countrywide Financial, 212, 214 Cramer, Jim, 141–142, 143, 145–146, 289, 303 Crawford, Michael, 246 Credit Crisis of 2008–2009: forced lending, 128–133 Index overview, 116–121 “too big to fail” policy, 121–128 See also Housing Crisis Crispus, Gaius Sallustius, 248 Culture of irresponsibility: chilling of inquiry, 293–296 moral vacuum, 296–299 morality, 290–292 overview, 277–281 rationality, 285–290 secular society, 281–285 See also Moral hazard; Self-indulgence Cuneo, Jonathan W., 326–327 “Curveball,” 363 Damn Yankees, 303 Dawes Plan, 62 The Debt Deflation Theory of Great Depressions (Fisher), 131 Deflation: Making Sure “It” Doesn’t Happen Here (Bernanke), 117 Democracy Alliance, 185 Derivatives, 22.


pages: 537 words: 144,318

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

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Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business process, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

The problem with buy and hold is that you may have to wait a long time for your opportunity. A long only, “see what happens” type of strategy is probably best addressed by buying an index. You can run this strategy with a lean, low-cost staff. You basically resign yourself to the fact that you do not have market timing skill and opt instead for cheap beta through an index. A real money manager thinks about the world through the Capital Asset Pricing Model, which is a diversified efficient frontier model of managing money. In this model, you want noncorrelated assets with decent returns, and I would say there are three types of return streams. The first is beta. Everyone knows what beta is—it is S&P500 or something equivalent. Then there is what I will call “beta-plus,” or “hard-to-access beta.” It is hard-to-access private equity, distressed real estate, mines in Ghana, and other types of illiquid assets.

See Credit bubble creation future Bond Trader prediction Professor prediction neutrality Buffett, Warren diversification Reader’s Digest franchise S&P index performance prediction California Public Employees’ Retirement System (CalPERS) contribution levels, increase equities allocation Equity Trader operation flexibility, impact fund, formula hedge funds, in-house operation investment performance, improvement target, increase methodology 1% effect peak-to-trough drawdown Pensioner control pension problems portfolio construction, assumption Predator operation unlevered pension fund CalPERS Model Cambridge Endowment, asset management Capital accumulation adequacy ratios allocation, determination availability compounding inflows, attraction loss, avoidance management tactical asset allocation models, usage pools preservation raising total destruction unlevered pool Capital Asset Pricing Model (CAPM) confusion Capitalism, stability Cash balance holding importance leverage, relationship obligations, meeting (difficulty) valuation quality Catastrophe risk options CDX generic credit spreads Central banker talent, Bond Trader perspective Central banks, alpha source Charitable foundation, running (example) China commodity market manipulation Commodity Super Cycle, importance Commodity Trader perspective decoupling examination FDI fiscal stimulus foreign exchange reserves future G7 demand reliance GDP GDP (2000-2008) global reserves acquisition growth rates, achievement importance, Commodity Investor perspective investment-to-GDP ratios Plasticine Macro Trader perspective problems renminbi (2005-2009) superpower Church of England, pension fund assets Client risk Closet dollar exposure Cocoa (1970-2009) Cohen, Abbey Joseph Collateralized debt obligations (CDOs) Commodities bearish view collapse, Commodity Hedger anticipation Commodity Investor focus curves pricing susceptibility equities Commodity Investor perspective exposure definition, application indices, usage impact investor participation long/short, ease meltdown options markets, liquidity price-induced inflation prices collapse financial flows, impact risk, equity risk (contrast) space, active manager search Commodities and oil (2008) Commodity Hedger, The big moves Cargill employment commodities collapse exposure, indices (usage) discipline endowment process full-risk positions, risk collars (requirement) globalization, meaning human bias, impact information arbitrage information flow, absence interview investment process investor focus lessons leverage, usage liquidity management valuation process market entry mistakes passive commodity indices, avoidance peak oil belief portfolio construction price dislocation identification real asset perspective real money fund operation redemptions, absence risk collars function impact risk management tactical approach tail hedging, impact time horizon, shortening trader development trades examination, events/news (impact) example execution hurdles/shortage ideas, origination one-year time horizon problems trading history volatility, dampening Commodity Investor, The active/tactical pension fund manager annual returns China, importance commodities/commodity equities focus cyclical/secular macro/micro thought process deferred oil trade downside risk, mitigation export land model false confidence hedge fund interaction hedge fund manager skill hyperinflation, worry ideas, trading interview investment process lessons liquidity, examination liquid net worth long-term investment horizon macro/micro domination macro theme mines, purchase oil fields, purchase pension fund, base currency philosophy positions, scaling process real money manager scenario resource nationalism risk-taking ability sovereign wealth fund, control speculative flows spot shortages/outages state pension fund, control tactical approach trade, problem trends triangulated conviction uncertainty, risk Commodity markets China manipulation Commodity Trader approach factors pricing structure stress test Commodity Super Cycle importance initiation trade selection Commodity Trader, The career trades China perspective commodities long/short, ease perspective trades global book, running global energy positions hedge fund money management inflation perspective interview liquidity, absence market coverage entry options, usage prop trader, hedge fund manager (contrast) prop trading, customer flow (impact) risk management failure second order effects short side Commodity trading advisers (CTAs), impact Constraints Consuelo Mack WealthTrack (Swensen) Consumer Price Index (CPI) Consumer price inflation (CPI) number, investment Contango Conundrum Speech (Greenspan) Convenience yield Copper (1989-2009) Core inflation, headline inflation (contrast) Core positions trading, indices/options (usage) Corn, yield expectations (increase) Corner solution Corn futures (2006-2009) Corn futures (2007) Corporate bonds, risky assets Corporate pensions funds, PBGC guarantees NLRB ruling Correlations analysis movement risk, increase Corruption Perceptions Index (CPI) Counterparty risk importance Country-related Eurobonds, usage Coxe, Don Crash (2008) banks, problems foresight CRB (2004-2009) CRB Commodity Index (2001) CRB Index (2009) Credit bubble future recognition trades Credit default swaps (CDSs) levels, examination payment usage Credit indices, tranches Credit pricing, example Credit spreads, tightness Crop yields, pollution (impact) Cross-correlation misunderstanding risk management Cross-sectional data sets Crowded positions, identification Crowding factor issue pervasiveness Crude oil inventory Cumulative returns (1990-2009) Currency hedge Currency valuation Cyclical analysis Data mining techniques, contrast Datastream, usage Debt.


pages: 624 words: 127,987

The Personal MBA: A World-Class Business Education in a Single Volume by Josh Kaufman

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Albert Einstein, Atul Gawande, Black Swan, business process, buy low sell high, capital asset pricing model, Checklist Manifesto, cognitive bias, correlation does not imply causation, Credit Default Swap, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, David Ricardo: comparative advantage, Dean Kamen, delayed gratification, discounted cash flows, Donald Knuth, double entry bookkeeping, Douglas Hofstadter, en.wikipedia.org, Frederick Winslow Taylor, George Santayana, Gödel, Escher, Bach, high net worth, hindsight bias, index card, inventory management, iterative process, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Lao Tzu, loose coupling, loss aversion, Marc Andreessen, market bubble, Network effects, Parkinson's law, Paul Buchheit, Paul Graham, place-making, premature optimization, Ralph Waldo Emerson, rent control, side project, statistical model, stealth mode startup, Steve Jobs, Steve Wozniak, subscription business, telemarketer, the scientific method, time value of money, Toyota Production System, tulip mania, Upton Sinclair, Vilfredo Pareto, Walter Mischel, Y Combinator, Yogi Berra

The complexity of financial transactions and the statistical models those transactions relied upon continued to grow until few practitioners fully understood how they worked or respected their limits. As Wired revealed in a February 2009 article, “Recipe for Disaster: The Formula That Killed Wall Street,” the inherent limitations of deified financial formulas such as the Black-Scholes option pricing model, the Gaussian copula function, and the capital asset pricing model (CAPM) played a major role in the tech bubble of 2000 and the housing market and derivatives shenanigans behind the 2008 recession. Learning how to use complicated financial formulas isn’t the same as learning how to run a business. Understanding what businesses actually do to create and deliver value is essential knowledge, but many business programs have de-emphasized value creation and operations in favor of finance and quantitative analysis.

You can’t know in advance if (or which) black swan events will occur: all you can do is be flexible, prepared, and Resilient (discussed later) enough to react appropriately if and when they do. Even the most detailed analysis with reams of historical data can’t save you from Uncertainty. The primary drawback of the financial models taught in most MBA programs is Uncertainty: your pro forma, (Net Present Value NPV ), or (Capital Asset Pricing Model CAPM) model is only as good as the quality of your predictions. Many a business has been ruined by financial predictions that turned out to be wrong. How likely is it that your ten-year financial projection predicts absolutely everything that will happen with 100 percent accuracy? Who says tomorrow is going to be anything like today? Many people make a business of selling certainty, which doesn’t exist.

Analysis of Financial Time Series by Ruey S. Tsay

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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

Empirical analysis of asset returns is then to estimate the unknown parameter θ and to draw statistical inference about behavior of {rit } given some past log returns. The model in Eq. (1.14) is too general to be of practical value. However, it provides a general framework with respect to which an econometric model for asset returns rit can be put in a proper perspective. Some financial theories such as the Capital Asset Pricing Model (CAPM) of Sharpe (1964) focus on the joint distribution of N returns at a single time index t (i.e., the distribution of {r1t , . . . , r N t }). Other theories emphasize the dynamic structure of individual asset returns (i.e., the distribution of {ri1 , . . . , ri T } for a given asset i). In this book, we focus on both. In the univariate analysis of Chapters 2 to 7, our T main concern is the joint distribution of {rit }t=1 for asset i.

In each plot, the two horizontal lines denote two standard-error limits of the sample ACF. and Q(10) = 32.7 for the log returns. The p values of these four test statistics are all less than 0.0003, suggesting that monthly returns of the value-weighted index are serially correlated. Thus, the monthly market index return seems to have stronger serial dependence than individual stock returns. In the finance literature, a version of the Capital Asset Pricing Model (CAPM) theory is that the return {rt } of an asset is not predictable and should have no autocorrelations. Testing for zero autocorrelations has been used as a tool to check the efficient market assumption. However, the way by which stock prices are determined and index returns are calculated might introduce autocorrelations in the observed return series. This is particularly so in analysis of high-frequency financial data.

A Primer for the Mathematics of Financial Engineering by Dan Stefanica

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asset allocation, Black-Scholes formula, capital asset pricing model, constrained optimization, delta neutral, discrete time, Emanuel Derman, implied volatility, law of one price, margin call, quantitative trading / quantitative finance, Sharpe ratio, short selling, time value of money, transaction costs, volatility smile, yield curve, zero-coupon bond

Let Wi be the weight of asset i in the BFinding efficient portfolios is one of the fundamental problems answered by the modern portfolio theory of Markowitz and Sharpe; see Markowitz [17] and Sharpe [25] for seminal ~apers. Of all the efficient portfolio~, the portfolio with the higheflt Sharpe ratio E~l~?, l.e., the expected return above the rIsk free rate r f normalized by the standard deviation 0-( R) of the return, is called the market portfolio (or the tangency portfolio) and plays an important role in the Capital Asset Pricing Model (CAPM). 262 CHAPTER 8. LAGRANGE MULTIPLIERS. NEWTON'S METHOD. portfolio, for i = 1 : 4. From (8.49) and (8.50), it follows that E[R] var(R) = WlJLl + W2JL2 + W3JL3 + W4JL4; The gradient W1 + W2 + W3 + W4 W1JL1 + W2JL2 + W3JL3 + W4JL4 = 1; = JLp. where w = (Wi)i=1:4, and f : }R4 f(w) = g(w) = ----7 }R = f(wo), and 9 : }R4 ----7 (8.53) (8.54) V(x,).) + 2W30"10"3P1,3 + A1 + A2JL1 2W30"3 + 2W10"10"3P1,~ + 2W20"20"3P2,3 + 2W40"4 + Al + A2JL4 WI + w2 + W3 + W4 - 1 W1JL1 + W2JL2 + w3JLa + W4JL4 - Al + A2JL3 20"r 20"10"2P1,2 20"10":3P1,:3 0 1 20"10"2P1,2 20"~ 20"20":3P2,3 0 1 20"10"3P1,3 20"20"aP2,3 0 1 20":1 0 0 0 20"1 1 1 1 1 1 o JL1 JL4 0 are defined as We first check that condition (8.9) is satisfied.


pages: 350 words: 103,270

The Devil's Derivatives: The Untold Story of the Slick Traders and Hapless Regulators Who Almost Blew Up Wall Street . . . And Are Ready to Do It Again by Nicholas Dunbar

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asset-backed security, bank run, banking crisis, Basel III, Black Swan, Black-Scholes formula, bonus culture, break the buck, capital asset pricing model, Carmen Reinhart, Cass Sunstein, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delayed gratification, diversification, Edmond Halley, facts on the ground, financial innovation, fixed income, George Akerlof, implied volatility, index fund, interest rate derivative, interest rate swap, Isaac Newton, John Meriwether, Kenneth Rogoff, Long Term Capital Management, margin call, market bubble, money market fund, Myron Scholes, Nick Leeson, Northern Rock, offshore financial centre, Paul Samuelson, price mechanism, regulatory arbitrage, rent-seeking, Richard Thaler, risk tolerance, risk/return, Ronald Reagan, shareholder value, short selling, statistical model, The Chicago School, Thomas Bayes, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, yield curve, zero-sum game

Quoted in the write-up for Risk’s Lifetime Achievement Award to Bill Winters (Risk, January 2005, 18). 2. See the write-up for Risk’s House of the Year Award to J.P. Morgan (Risk, January 2002, 46). 3. Robert C. Merton, “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates,” Journal of Finance 29, no. 2 (1974): 449–470. 4. Roughly speaking, the correlation parameter is equivalent to beta in Sharpe’s capital asset pricing model (CAPM). 5. David X. Li, “On Default Correlation: A Copula Function Approach” (working paper, RiskMetrics Group, 1999). 6. It was not an unreasonable argument. In 2008, structured products with a money-back guarantee based on a single issuer—Lehman Brothers—defaulted, losing money for consumers in the United States, Germany, and Hong Kong. 7. “Another area where Deutsche has shown innovation...is in structuring bespoke tranches of CDOs . . .


pages: 357 words: 110,017

Money: The Unauthorized Biography by Felix Martin

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bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, Bretton Woods, British Empire, call centre, capital asset pricing model, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, creative destruction, credit crunch, David Graeber, en.wikipedia.org, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, fixed income, Fractional reserve banking, full employment, Goldman Sachs: Vampire Squid, Hyman Minsky, inflation targeting, invention of writing, invisible hand, Irish bank strikes, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, mobile money, moral hazard, mortgage debt, new economy, Northern Rock, Occupy movement, Plutocrats, plutocrats, private military company, Republic of Letters, Richard Feynman, Richard Feynman, Robert Shiller, Robert Shiller, Scientific racism, seigniorage, Silicon Valley, smart transportation, South Sea Bubble, supply-chain management, The Wealth of Nations by Adam Smith, too big to fail

Quite the opposite: academic finance elected to concern itself with nothing else. It chose as its exclusive focus of investigation the pricing of financial securities on the private capital markets—the equity shares and bonds that were becoming ever more important as the liberalising policies of the post-war period picked up steam. Its major innovations—the theory of portfolio balance, the Capital Asset Pricing Model, the theory of options pricing—were eagerly adopted by financial practitioners, since investors and their agents were naturally interested in making sense of what they were doing.21 Yet by focusing exclusively on the pricing of securities on private markets, academic finance developed an exact mirror image of the flaw of neoclassical macroeconomics. By ignoring the essential link between the financial securities traded on the capital markets and the monetary system operated by the sovereign and the banks, academic finance built a theory of finance without the macroeconomy just as neoclassical macroeconomics had built a theory of the macroeconomy without finance.


pages: 374 words: 114,600

The Quants by Scott Patterson

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Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Bonfire of the Vanities, Brownian motion, buttonwood tree, 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, 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, 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

He continued to churn out libraries of papers, leveraging the power of computers and a stream of bright young students eager to learn from the guru of efficient markets. In 1992, soon after Asness arrived on the scene, Fama and French published their most important breakthrough yet, a paper that stands as arguably the most important academic finance research of the last two decades. And the ambition behind it was immense: to overturn the bedrock theory of finance itself, the capital asset pricing model, otherwise known as CAPM. Before Fama and French, CAPM was the closest approximation to the Truth in quantitative finance. According to the grandfather of CAPM, William Sharpe, the most important element in determining a stock’s potential future return is its beta, a measure of how volatile the stock is compared with the rest of the market. And according to CAPM, the riskier the stock, the higher the potential reward.


pages: 467 words: 154,960

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

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

There is no greater source of conflict among researchers and practitioners in capital market theory than the validity of technical analysis. The vast majority of academic research condemns technical analysis as theoretically bankrupt and of no practical value…It is certainly understandable why many researchers would oppose technical analysis: the validity of technical analysis calls into question decades of careful theoretical modeling [Capital Asset Pricing Model, Arbitrage Pricing Theory] claiming the markets are efficient and investors are collectively, if not individually, rational.23 242 The biggest cause of trouble in the world today is that the stupid people are so sure about things and the intelligent folks are so full of doubts. Bertrand Russell Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets However, it seems many ignore the data.


pages: 545 words: 137,789

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

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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, Bretton Woods, British Empire, 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, 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, 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

Fama joined another firm that manages index funds, Dimensional Fund Advisors.) The rise of efficient market theory also signaled the beginning of quantitative finance. In addition to the random walk model of stock prices, the period between 1950 and 1970 saw the development of the mean-variance approach to portfolio diversification, which Harry Markowitz, another Chicago economist, pioneered; the capital asset pricing model, which a number of different scholars developed independently of one another; and the Black-Scholes option pricing formula, which Fischer Black, an applied mathematician from Harvard, and Myron Scholes, a finance Ph.D. from Chicago, developed. Some of the mathematics used in these theories is pretty befuddling, which helps explain why there are so many physicists and mathematicians working on Wall Street, but the basic ideas underpinning them aren’t so difficult.


pages: 415 words: 125,089

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

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

In cooperation with William Sharpe-a graduate student who later shared the Nobel Prize with him-Markowitz made it possible to skip over the whole problem of calculating covariances among the individual securities. His solution was to estimate how each security varies in relation to the market as a whole, a far simpler matter. This technique subsequently led to Sharpe's development of what has come to be known as the Capital Asset Pricing Model, which analyzes how financial assets would be valued if all investors religiously fol lowed Markowitz's recommendations for building portfolios. CAPM, as it is known, uses the term "beta" to describe the average volatility of individual stocks or other assets relative to the market as a whole over some specific period of time. The AIM Constellation Fund that we looked at in Chapter 12, for example, had a beta of 1.36 during the years 1983 to 1995, which means that AIM tended to move up or down 1.36% every time the S&P 500 moved up or down 1%; it tended to fall 13.6% every time the market dropped 10%, and so on.


pages: 524 words: 143,993

The Shifts and the Shocks: What We've Learned--And Have Still to Learn--From the Financial Crisis by Martin Wolf

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air freight, anti-communist, Asian financial crisis, asset allocation, asset-backed security, balance sheet recession, bank run, banking crisis, banks create money, Basel III, Ben Bernanke: helicopter money, Berlin Wall, Black Swan, bonus culture, break the buck, Bretton Woods, call centre, capital asset pricing model, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collateralized debt obligation, corporate governance, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency manipulation / currency intervention, currency peg, debt deflation, deglobalization, Deng Xiaoping, diversification, double entry bookkeeping, en.wikipedia.org, Erik Brynjolfsson, Eugene Fama: efficient market hypothesis, eurozone crisis, Fall of the Berlin Wall, fiat currency, financial deregulation, financial innovation, financial repression, floating exchange rates, forward guidance, Fractional reserve banking, full employment, global rebalancing, global reserve currency, Growth in a Time of Debt, Hyman Minsky, income inequality, inflation targeting, information asymmetry, invisible hand, Joseph Schumpeter, Kenneth Rogoff, labour market flexibility, labour mobility, light touch regulation, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandatory minimum, margin call, market bubble, market clearing, market fragmentation, Martin Wolf, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mortgage debt, negative equity, new economy, North Sea oil, Northern Rock, open economy, paradox of thrift, Paul Samuelson, price stability, private sector deleveraging, purchasing power parity, pushing on a string, quantitative easing, Real Time Gross Settlement, regulatory arbitrage, reserve currency, Richard Feynman, Richard Feynman, risk-adjusted returns, risk/return, road to serfdom, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, Second Machine Age, secular stagnation, shareholder value, short selling, sovereign wealth fund, special drawing rights, The Chicago School, 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, too big to fail, Tyler Cowen: Great Stagnation, very high income, winner-take-all economy, zero-sum game

Adair Turner, ‘Monetary and Financial Stability: Lessons from the Crisis and from Classic Economics Texts’, Financial Services Authority, London, 2 November 2012, http://www.fsa.gov.uk/static/pubs/speeches/1102-at.pdf. 11. See Willem Buiter, ‘The Unfortunate Uselessness of Most State of the Art Academic Macroeconomics’, 3 March 2009, http://blogs.ft.com/maverecon/2009/03/the-unfortunate-uselessness-of-most-state-of-the-art-academic-monetary-economics. 12. See, for example, the capital-asset pricing model, http://www.investopedia.com/terms/c/capm.asp. 13. Felix Martin, Money: The Unauthorised Biography (London: Bodley Head, 2013). 14. Michael McLeay, Amar Radia and Ryland Thomas, ‘Money Creation in the Modern Economy’, Bank of England Quarterly Bulletin (2014), Q1, p. 14, http://www.bankofengland.co.uk/publications/Documents/quarterlybulletin/2014/qb14q102.pdf. See also Stuart Berry, Richard Harrison, Ryland Thomas and Iain de Weymarn, ‘Interpreting Movements in Broad Money’, Bank of England Quarterly Bulletin (2007), Q3, p. 377, http://www.bankofengland.co.uk/publications/Documents/quarterlybulletin/qb070302.pdf, and Josh Ryan-Collins, Tony Greenham, Richard Werner and Andrew Jackson, ‘What Do Banks Do?’


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

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

As much as Wall Street had a hand in the current crisis, it began as a broadly held belief that housing prices could not fall—a belief that fueled a full-blown social contagion. Learning how to spot such bubbles and deal with them before they infect entire economies will be a major challenge for the next generation of finance scholars. Equipped with sophisticated nancial ideas ranging from the capital asset pricing model to intricate options-pricing formulas, you are certainly and justi ably interested in building materially rewarding careers. There is no shame in this, and your nancial success will re ect to a large degree your e ectiveness in producing strong results for the rms that employ you. But, however imperceptibly, the rewards for success on Wall Street, and in nance more generally, are changing, just as the de nition of nance must change if is to reclaim its stature in society and the trust of citizens and leaders.


pages: 670 words: 194,502

The Intelligent Investor (Collins Business Essentials) by Benjamin Graham, Jason Zweig

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3Com Palm IPO, accounting loophole / creative accounting, air freight, Andrei Shleifer, asset allocation, buy low sell high, capital asset pricing model, corporate governance, corporate raider, Daniel Kahneman / Amos Tversky, diversified portfolio, Eugene Fama: efficient market hypothesis, George Santayana, hiring and firing, index fund, intangible asset, Isaac Newton, Long Term Capital Management, market bubble, merger arbitrage, money market fund, new economy, passive investing, price stability, Ralph Waldo Emerson, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, sharing economy, short selling, Silicon Valley, South Sea Bubble, Steve Jobs, survivorship bias, the market place, the rule of 72, transaction costs, tulip mania, VA Linux, Vanguard fund, Y2K, Yogi Berra

Incidentally, when businessmen buy businesses—which is just what our Graham & Dodd investors are doing through the medium of marketable stocks—I doubt that many are cranking into their purchase decision the day of the week or the month in which the transaction is going to occur. If it doesn’t make any difference whether all of a business is being bought on a Monday or a Friday, I am baffled why academicians invest extensive time and effort to see whether it makes a difference when buying small pieces of those same businesses. Our Graham & Dodd investors, needless to say, do not discuss beta, the capital asset pricing model, or covariance in returns among securities. These are not subjects of any interest to them. In fact, most of them would have difficulty defining those terms. The investors simply focus on two variables: price and value. I always find it extraordinary that so many studies are made of price and volume behavior, the stuff of chartists. Can you imagine buying an entire business simply because the price of the business had been marked up substantially last week and the week before?


pages: 1,042 words: 266,547

Security Analysis by Benjamin Graham, David Dodd

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activist fund / activist shareholder / activist investor, asset-backed security, backtesting, barriers to entry, capital asset pricing model, carried interest, collateralized debt obligation, collective bargaining, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fear of failure, financial innovation, fixed income, full employment, index fund, intangible asset, invisible hand, Joseph Schumpeter, locking in a profit, Long Term Capital Management, low cost carrier, moral hazard, mortgage debt, Myron Scholes, p-value, Right to Buy, risk-adjusted returns, risk/return, secular stagnation, shareholder value, The Chicago School, the market place, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, zero-coupon bond

Oddly enough, despite 75 years of success achieved by value investors, one group of observers largely ignores or dismisses this discipline: academics. Academics tend to create elegant theories that purport to explain the real world but in fact oversimplify it. One such theory, the Efficient Market Hypothesis (EMH), holds that security prices always and immediately reflect all available information, an idea deeply at odds with Graham and Dodd’s notion that there is great value to fundamental security analysis. The Capital Asset Pricing Model (CAPM) relates risk to return but always mistakes volatility, or beta, for risk. Modern Portfolio Theory (MPT) applauds the benefits of diversification in constructing an optimal portfolio. But by insisting that higher expected return comes only with greater risk, MPT effectively repudiates the entire value-investing philosophy and its long-term record of risk-adjusted investment outperformance.


pages: 1,336 words: 415,037

The Snowball: Warren Buffett and the Business of Life by Alice Schroeder

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affirmative action, Albert Einstein, anti-communist, Ayatollah Khomeini, barriers to entry, Bob Noyce, Bonfire of the Vanities, Brownian motion, capital asset pricing model, card file, centralized clearinghouse, collateralized debt obligation, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, desegregation, Donald Trump, Eugene Fama: efficient market hypothesis, global village, Golden Gate Park, Haight Ashbury, haute cuisine, Honoré de Balzac, If something cannot go on forever, it will stop - Herbert Stein's Law, In Cold Blood by Truman Capote, index fund, indoor plumbing, intangible asset, interest rate swap, invisible hand, Isaac Newton, Jeff Bezos, John Meriwether, joint-stock company, joint-stock limited liability company, Long Term Capital Management, Louis Bachelier, margin call, market bubble, Marshall McLuhan, medical malpractice, merger arbitrage, Mikhail Gorbachev, money market fund, moral hazard, NetJets, new economy, New Journalism, North Sea oil, paper trading, passive investing, Paul Samuelson, pets.com, Plutocrats, plutocrats, Ponzi scheme, Ralph Nader, random walk, Ronald Reagan, Scientific racism, shareholder value, short selling, side project, Silicon Valley, Steve Ballmer, Steve Jobs, supply-chain management, telemarketer, The Predators' Ball, The Wealth of Nations by Adam Smith, Thomas Malthus, too big to fail, transcontinental railway, Upton Sinclair, War on Poverty, Works Progress Administration, Y2K, yellow journalism, zero-coupon bond

But the tendencies of humankind being what they are, EMH became de rigueur in business school classrooms, yet the number of individual investors and professional money managers who assumed that they were smarter than average only grew, the toll-takers kept taking their cut, and the market went on as before. Thus the main effect of “The Superinvestors of Graham-and-Doddsville” was to add to the growing legend, even the cult, that was building around Warren Buffett. Meanwhile, EMH and its underpinning, the capital asset pricing model, drove extraordinary and deep roots into the investing world; it launched a view of the stock market as an efficient statistical machine. In a reliably efficient market, a stock was risky not based on where it was trading versus its intrinsic value, but based on “volatility”—how likely it was to deviate from the market average. Using that information and newly unleashed computing power, economists and mathematicians started going to Wall Street to make more money than they ever could in academia.