risk-adjusted returns

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Trading Risk: Enhanced Profitability Through Risk Control by Kenneth L. Grant

backtesting, business cycle, buy and hold, commodity trading advisor, correlation coefficient, correlation does not imply causation, delta neutral, diversification, diversified portfolio, fixed income, frictionless, frictionless market, George Santayana, implied volatility, interest rate swap, invisible hand, Isaac Newton, John Meriwether, Long Term Capital Management, market design, Myron Scholes, performance metric, price mechanism, price stability, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, two-sided market, value at risk, volatility arbitrage, yield curve, zero-coupon bond

One rather elegant outcome of this is that an individual who takes capital and invests it in Treasuries earns exactly the risk-free rate and, therefore, generates a Sharpe Ratio of precisely 0, while portfolios that fail to earn even this modest level of return actually have negative Sharpes. Therefore, it is only for performance above the minimum benchmark of government coupons—and as such is deemed to be associated with risk-bearing market activities—that the Sharpe Ratio begins to recognize a positive, risk-adjusted return. • Standard Deviation of Return. This is our old friend/nemesis, which we thought was beaten to death already, resurrected to apply as the risk component in a risk-adjusted return calculation. Note that here it is extremely important to express this statistic in the appropriate time spans—ideally, as indicated earlier, one year. Due to the specifics of the calculation (under which the figure varies directly with the square root of the number of data points), this requires either multiplication or division of the square root of the number of observations.

As I have argued, the ideal selection criteria for any given investment opportunity is its incremental impact on prospective risk-adjusted return for the overall portfolio. Stated more simply, it is imperative that you make your decisions on the basis of which opportunities will provide the highest level of incremental return for the amount of incremental exposure assumption they entail. In order to use 142 TRADING RISK this selection process as effectively as possible, the decision maker must have a grasp of the volatility characteristics of his or her prospective trades, but he or she also must be aware of the upside opportunities that are implicit in the trade. Indeed, if the true criterion for investment inclusion is risk-adjusted return, then it is as incorrect to make decisions on the basis of volatility characteristics alone as it is solely on the basis of return opportunity.

For example, you may want to compare such factors as your correlations to market benchmarks, or trading winning percentages across these interludes of diverging fortune, in order to see if they are markedly different. As we will discuss in great detail, this won’t necessarily give you an answer key as to what will work in the future, but it will certainly offer insights that may help you apply your capital and other scarce resources more efficiently. Try New Things. Markets, by their very nature, are in a constant state of flux. One reason for this is that any strategy yielding above-average risk-adjusted return (which we will define in painful detail later) is, by the unshakable laws of human nature, under a sustained threat by other market participants seeking to correct this “inefficiency.” Indeed, as any close observer will tell you, the rate of change is increasing at an increasing rate. This means that there is a limited shelf life for nearly any highly successful market approach. Ultimately, I believe that almost any strategy either will require significant tweaking or will be periodically subject to The Risk Management Investment 11 diminished returns over what constitutes a very real but little understood business cycle that exists for market participation of all kinds.


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

Asian financial crisis, asset allocation, backtesting, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, 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

Average Monthly Gain Average Monthly Loss Standard Deviation Gain Standard Deviation Loss Standard Deviation Semideviation Skewness Kurtosis Coskewness Sharpe ratio Calmar ratio Maximum Drawdown 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. These measures can be classified into six groups: 1. Absolute return measures 2. Absolute risk measures 3. Absolute risk-adjusted return measures Gain/Loss Ratio Beta Annualized Alpha Treynor Ratio Jensen Alpha Information Ratio Up Capture Down Capture Up Number Ratio Down Number Ratio Up Percentage Ratio Down Percentage Ratio. 206 RISK AND MANAGED FUTURES INVESTING 4. Relative return measures 5. Relative risk measures 6. Relative risk-adjusted return measures DATA The data for this study came from the Center for International Securities and Derivatives Markets (CISDM) database. We selected a sample of 200 CTA managers who had complete return data for the period from January 1998 to July 2003.

In addition, CTA QU index possesses a higher Sharpe ratio than equity indices, indicating that CTAs offer superior risk-adjusted returns. These estimates may understate true risk, so monthly modified Sharpe ratios (using VaR instead of standard deviation) is also presented and confirms the advantage of the CTA QU index. Using VaR and modified VaR to measure risk, the CTAs are still less risky than equity indices. For instance, a one percent VaR of −5.3 percent for CTA QU index means that there is a 1 percent chance that the loss will be greater that 5.3 percent next month (or a 99 percent chance that it will be less than 5.3 percent). Besides very attractive risk adjusted return characteristics, one of the most important features of CTAs is their favorable correlation structure to traditional assets classes (see Table 20.2).

Ali PART FOUR Program Evaluation, Selection, and Returns 275 CHAPTER 15 How to Design a Commodity Futures Trading Program 277 Hilary Till and Joseph Eagleeye CHAPTER 16 Choosing the Right CTA: A Contingent Claim Approach 294 Zsolt Berenyi CHAPTER 17 CTAs and Portfolio Diversification: A Study through Time 307 Nicolas Laporte CHAPTER 18 Random Walk Behavior of CTA Returns 326 Greg N. Gregoriou and Fabrice Rouah CHAPTER 19 CTA Strategies for Returns-Enhancing Diversification 336 David Kuo Chuen Lee, Francis Koh, and Kok Fai Phoon CHAPTER 20 Incorporating CTAs into the Asset Allocation Process: A Mean-Modified Value at Risk Framework Maher Kooli 358 viii CONTENTS CHAPTER 21 ARMA Modeling of CTA Returns 367 Vassilios N. Karavas and L. Joe Moffitt CHAPTER 22 Risk-Adjusted Returns of CTAs: Using the Modified Sharpe Ratio 377 Robert Christopherson and Greg N. Gregoriou CHAPTER 23 Time Diversification: The Case of Managed Futures 385 François-Serge Lhabitant and Andrew Green REFERENCES 399 INDEX 417 Preface he idea for this book came about when we realized that a collection of managed futures articles dealing with quantitative and qualitative analyses of commodity trading advisors (CTAs) could be a useful and welcomed addition to existing books on the subject.


pages: 363 words: 28,546

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

asset allocation, Bretton Woods, business cycle, 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, stocks for the long run, superstar cities, survivorship bias, transaction costs, Vanguard fund

It reflects the total variability of an asset return. Beta measures only the variability that is systematically related to the market benchmark. P1: OTA/XYZ P2: ABC c01 JWBT412-Marston December 20, 2010 16:58 16 Printer: Courier Westford PORTFOLIO DESIGN Third, the advisor needs measures of risk-adjusted returns. The Sharpe ratio measures the excess return on an asset (above the risk-free return) relative to the standard deviation. So the Sharpe ratio is appropriate for measuring the risk-adjusted return if the total risk of the asset is being considered. Alpha is a risk-adjusted return based on only the systematic risk of the asset. That is, alpha is the excess return earned on an asset above that explained by the beta of that asset. Alpha is often used to measure the risk-adjusted contribution of a mutual fund manager. It can also be used to measure the marginal contribution of an individual asset to a market-based portfolio.

In Figure 1.3, the return on portfolio P, rP , is compared with a market benchmark, rM .15 Most portfolios have less risk than the market as a whole, so it’s important to compare returns at a common level of risk. Alpha∗ brings the risk level of the market down to that of the portfolio to be evaluated.16 The expression for alpha∗ (α ∗ ) shows how this is done: α ∗ = rP − [rF + (σP /σM )(rM − rF )] Thus the portfolio return is compared with the risk-adjusted return on the market where risk is adjusted downward by the ratio of σ P to σ M . Alpha∗ doesn’t give any more information about risk-adjusted returns than that which is provided by Sharpe ratios, but alpha∗ translates differences in Sharpe ratios into excess returns.17 And excess returns can be understood easily by all investors. Alpha∗ will be used repeatedly in the book to show how well one portfolio is doing relative to another. P1: OTA/XYZ P2: ABC c01 JWBT412-Marston Asset Allocation December 20, 2010 16:58 Printer: Courier Westford 19 NOTES 1.

Investment-grade bonds are highly correlated with one another. The correlation between Barclays Aggregate index and the Treasury return is 0.92. Shifting from the Treasury bond to the Barclays Aggregate raises risk-adjusted return, or alpha∗ , by only 0.3 percent. Similarly, the correlation between the S&P 500 and Russell 3000 all-cap index is very high at 0.99. The returns on these two indexes, moreover, are almost identical over the sample period since the Russell series was introduced in 1979. Diversifying beyond the S&P 500 into small- and mid-cap stocks has a negligible effect on the risk-adjusted return. What these experiments suggest is that the investor is going to have to look beyond U.S. stocks and bonds for diversification gains.3 But note how much the investor has already accomplished. By spreading out stock market investments across the entire S&P 500, the investor has already achieved substantial diversification.


pages: 1,088 words: 228,743

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

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

The dollar’s downtrend lowers U.S. assets’ performance for many non-U.S. investors, while the returns of many unhedged non-U.S. assets have been augmented by currency appreciation. 4.5 RISK-ADJUSTED RETURNS Performance evaluation of any asset, strategy, or fund is increasingly done on returns that are somehow adjusted for the average risk taken. Adjustment for volatility is the most common risk adjustment, based on the logic that it would be “unfair” to compare, say, returns of 4% on two different assets with very different volatility levels. Specifically, the Sharpe ratio (SR) is the average excess return over the riskless rate of return (proxied, in practice, by a near-riskless money market asset) divided by the volatility of this excess return. The SR is a measure of volatility-adjusted or risk-adjusted return. In the CAPM context, the optimal risky asset portfolio can be levered or de-levered through borrowing or lending of cash (i.e., short or long positions in cash).

According to classic portfolio theory, systematic risk such as equity market beta is more dangerous than idiosyncratic risk that can be diversified away. More modern critics point out that volatility does not distinguish between losses that occur in good or bad times or even between upside and downside surprises. Another approach to risk-adjusted returns focuses on an asset’s—or a strategy’s or a fund’s—contribution to portfolio risk as opposed to its standalone risk (a part of which is typically diversifiable). The classic Jensen’s alpha is the intercept when regressing asset returns on equity market returns. More generally, alpha is the intercept of any risk factor model. Thus, alpha is risk-adjusted return where “risk” is some measure of the asset’s contribution to portfolio risk. In the past 10 to 15 years, the Fama–French three-factor model—which contains size and value factors besides the market beta factor—has become increasingly popular among equity managers.

Meanwhile, the way that S&P GSCI Commodity index owners synchronously roll into new futures contracts each month causes a predictable temporary supply–demand imbalance that hurts these index investors. 28.2 RISK This book does not cover the important topics of risk management and portfolio construction except for their mechanical impact on expected returns. Risk reduction through diversification is famously the one free lunch that markets offer, but it improves risk-adjusted returns rather than raw returns. However, Sections 28.2.1 and 28.2.2 show how diversification can boost portfolio returns, almost magically, through leverage or rebalancing. The more typical approach to boosting expected returns is to take more risk, but I remind the reader that not all risks are rewarded and certainly they are not all rewarded equally well. Section 28.2.3 stresses that markets do not treat all volatility as equal, a theme closely related to the shortcomings of the Sharpe ratio, the most common measure of risk-adjusted returns. Section 28.2.4 briefly reviews smart diversification or portfolio construction methods for efficient risk reduction. 28.2.1 Monetized risk reduction via leverage Many retail investor portfolios are insufficiently diversified, perhaps consisting of only a few stocks and having plenty of diversifiable, unrewarded risk.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

Thus even though Manager C ends up ahead of Manager D, many investors will never survive the ride to see the eventual successful outcome (and even those who do may have initiated their investment on an upside excursion, reducing or even eliminating their net return). The greater the volatility, the larger the percentage of investors who will close out their investments at a loss. Clearly, there is a need to use risk-adjusted returns rather than returns alone to make valid performance comparisons. In the next section we consider some alternative risk-adjusted return measures. Risk-Adjusted Return Measures The formulas for the performance measures in this section can be found in Appendix B. Sharpe Ratio The Sharpe ratio is the most widely used risk-adjusted return measure. The Sharpe ratio is defined as the average excess return divided by the standard deviation. Excess return is the return above the risk-free return (e.g., Treasury bill rate). For example, if the average return is 8 percent per year and the T-bill rate is 3 percent, the excess return would be 5 percent.

Most of Manager B’s volatility, however, was on the upside—a characteristic most investors would consider an attribute, not a fault. Although Manager A had lower volatility overall, the downside volatility was significantly greater than Manager B’s—a characteristic that is consistent with most investors’ intuitive sense of greater risk. The Sharpe ratio does not distinguish between downside and upside volatility, while the other risk-adjusted return measures do. Table 8.4 A Comparison of Risk-Adjusted Return Measures Although all the risk-adjusted return measures besides the Sharpe ratio penalize only downside volatility, they do so in different ways that have different implications: Sortino ratio and SDR Sharpe ratio. These ratios penalize returns below a specified level (e.g., zero) with the weight assigned to downside deviations increasing more than proportionately as their magnitude increases.

The average maximum retracement is the average of all these monthly maximum retracements. The return retracement ratio is statistically far more meaningful than the MAR and Calmar ratios because it is based on multiple data points (one for each month) as opposed to a single statistic (the maximum drawdown in the entire record). Comparing the Risk-Adjusted Return Performance Measures Table 8.4 compares Managers A and B shown in Figure 8.3 in terms of each of the risk-adjusted return performance measures we discussed. Interestingly, the Sharpe ratio, which is by far the most widely used return/risk measure, leads to exactly the opposite conclusion indicated by all the other measures. Whereas the Sharpe ratio implies that Manager A is significantly superior in return/risk terms, all the other performance measures rank Manager B higher—many by wide margins.


pages: 504 words: 139,137

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

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

., Rb = 0), so they report the IR simply as which is always higher than the SR, since it does not subtract the risk-free rate. Even if many hedge funds report this number, I view it as an unreasonable number as it gives the hedge fund credit for earning the risk-free rate (and depends on the level of interest rates). The IR is almost always reported as an annualized number, as discussed further below. Both the SR and the IR are ways of calculating risk-adjusted returns, but some traders and investors say, You can’t eat risk-adjusted returns. Suppose, for instance, that a hedge fund beats the risk-free rate by 3% at a tiny risk of 2%, realizing an excellent SR of 1.5. Some investors might say, “Well, it’s still just 3%. I was hoping for more return.” Whether this is a fair criticism or not depends on several things. First, it depends on whether the strategy’s risk really is that low on a long-term basis or this period was just lucky not to have a blowup (e.g., if the hedge fund is selling out-the-money options collecting small premiums until a big market move blows it up).

See also performance measures return drivers of investment styles, ix returns of hedge funds, 22–24. See also performance measures reversal strategies, 152–53; Asness on, 158, 159 RI (residual income), 92–93 Ricardian equivalence, 7t Ricardo, David, 208 risk: measurement of, 57–59; Soros on, 204, 206–7. See also liquidity risk; market exposure (beta risk); value-at-risk (VaR); volatility risk-adjusted alpha, 30 risk-adjusted return, 29–31. See also Sharpe ratio (SR) risk-adjusted return on capital (RAROC), 31–32 risk arbitrage, 14, 314. See also merger arbitrage risk aversion coefficient, 56, 171 risk-based asset allocation, 170–71 risk-free interest rate: bond prices and, 241; bond yields and, 248–49; return of a trading strategy and, 27–28 risk limits, 59–60 risk management, 54; drawdown control in, 54, 59, 60–62, 225; in line with trends, 212; in managed futures investing, 225; versus predatory trading, 84; prospective, 59–60; trader’s emotions and, 61 risk neutral probability, 238 risk parity investing, 16, 45, 171 risk premium: Asness on successful strategies and, 164; bond yield and, 248–49; carry trading and, ix; corporate credit and, 168, 260; inflation and, 196; leverage and, ix; liquidity-adjusted CAPM and, 43; option value and, 238; strategic asset allocation and, 168; value investing and, ix.

., if prices do not change). For instance, global macro investors are known to pursue the currency carry trade where they invest in currencies with high interest rates, bond traders often prefer high-yielding bonds, equity investors like stocks with high dividend yields, and commodity traders like commodity futures with positive “roll return.” Low-risk investing is the style of exploiting the high risk-adjusted returns of safe securities. This investment style is done in several different ways across various markets. Low-risk investing can be done as a long–short equity strategy, buying safe stocks with leverage while shorting risky ones, also called “betting against beta.” Low-risk investing can also be done as a long-only equity strategy, buying a portfolio of relatively safe stocks, also called defensive equity.


pages: 162 words: 50,108

The Little Book of Hedge Funds by Anthony Scaramucci

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

Another Theory, Another Definition Just three short years after A.W. Jones began using a metric he called velocity to measure how closely a stock’s movement tracks the broader market, a young graduate student named Harry Markowitz was busy at work developing the Modern Portfolio Theory. Discussed in a paper entitled “Portfolio Selection,” this theory postulated that it was not enough to simply maximize returns but one must maximize risk-adjusted returns, whereby returns would be based upon a given level of inherent risk. The key to his theory was that the risk of a portfolio is dependent upon the relationship among its securities. In other words, if you picked the right securities or had the right asset allocation you could get out on the efficient frontier and actually find a scenario where you earned more reward yet took less risk. Back in the 1950s, the problem with this approach was that it was not easy to implement—there simply wasn’t enough time or resources to calculate the correlations between thousands of stocks—or (at that time) just 25!

Key capital providers including Fannie Mae, Freddie Mac and Wall Street trading desks have either exited the market or are dramatically reducing their involvement. We think this will increase the size and breadth of the opportunities in the mortgage market for many years to come. We also believe that hedge fund investors will continue to commit capital to mortgage strategies to seek out uncorrelated return streams, improve portfolio diversification, and achieve high risk-adjusted returns. Chapter Six Ironing Out Inefficiencies Exploiting the Efficient Market Theory If the efficient markets hypothesis was a publicly traded security, its price would be enormously volatile. —Andrei Shleifer and Lawrence H. Summers, The Noise Trader Approach to Finance In 1990, Andrei Shleifer and Larry Summers mockingly made the comment that begins this chapter, adding that the “stock in the efficient markets hypothesis—at least as it has been traditionally formulated—crashed along with the rest of the market on October 19, 1987 . . . and its recovery has been less dramatic than that of the rest of the market.”1 Pretty fun for a pair of economists from Harvard, especially for one who would serve as President Clinton’s Secretary of the Treasury and President Obama’s Director of the White House National Economic Council.

And if you don’t have the first, the other two will kill you. —Warren Buffett Throughout this book I have been stressing the key differences between hedge funds and other asset classes. In an effort to generate absolute returns and produce alpha, a hedge fund manager must possess the uncanny ability to fundamentally select the best stocks and systematically diversify his portfolio so that he can produce risk-adjusted returns. But for every stock-picking guru like David Einhorn or Dan Loeb there are dozens of other nameless hedge fund managers who are not quite as successful. Although the purpose of this book is not to uncover the secret formula for achieving alpha-like return, nor is it to explain in painstaking detail how to invest in hedge funds, this chapter will spend a bit of time showing you how investors and fund of hedge fund managers screen the over 9,000 hedge funds that are currently in operation.


pages: 490 words: 117,629

Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen

asset allocation, asset-backed security, buy and hold, capital controls, cognitive dissonance, corporate governance, diversification, diversified portfolio, fixed income, index fund, law of one price, Long Term Capital Management, market bubble, market clearing, market fundamentalism, money market fund, passive investing, Paul Samuelson, pez dispenser, price mechanism, profit maximization, profit motive, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Silicon Valley, Steve Ballmer, stocks for the long run, survivorship bias, technology bubble, the market place, transaction costs, Vanguard fund, yield curve, zero-sum game

In aggregate, venture investors fare about as well as their marketable equity counterparts. After adjustment for risk, the overwhelming majority of venture capital fails to produce acceptable risk-adjusted returns. The new entrant to the world of private entrepreneurial finance faces an obstacle quite apart from the barriers hampering investment success in other asset classes. The top-tier venture partnerships, essentially closed to new money, enjoy superior access to deals, entrepreneurs, and capital markets. Exclusion from the venture capital elite disadvantages all but the most long-standing, most successful limited partners. Suppliers of funds to the venture capital industry generally realize poor risk-adjusted returns. Sensible individual investors look elsewhere for investment performance. CHAPTER SUMMARY Non-core asset classes provide investors with a broad range of superficially appealing but ultimately performance-damaging investment alternatives.

While default-free, noncallable, full-faith-and-credit obligations of the U.S. government play a basic, valuable, differentiable role in investor portfolios, investment-grade corporate bonds, high-yield bonds, foreign bonds, and asset-backed securities contain unattractive characteristics that argue against inclusion in well-constructed portfolios. Understanding the shortcomings of particular fixed-income investment alternatives, particularly in regard to how those alternatives relate to the objectives of the fixed-income asset class, helps investors in making well-informed portfolio decisions. Those asset classes that require superior active management results to produce acceptable risk-adjusted returns belong only in the portfolios of the handful of investors with the resources and fortitude to pursue and maintain a high-quality active investment management program. Understanding the difficulty of identifying superior hedgefund, venture-capital, and leveraged-buyout investments leads to the conclusion that hurdles for casual investors stand insurmountably high. Even many well-equipped investors fail to clear the hurdles necessary to achieve consistent success in producing market-beating active management results.

Strikingly, a comparable-maturity U.S. Treasury note produced a holding period return of 45.8 percent, as the noncallable nature of the government issue allowed investors to benefit fully from the bond market rally. The 3.4 percent holding period increment, realized by PCA bondholders over the three and one-half years, represents scant compensation for accepting a high degree of credit risk. U.S. Treasuries produced risk-adjusted returns significantly higher than those realized by holders of the PCA9.625s. Holders of PCA stock faced a tough set of circumstances. In contrast to the strong market enjoyed by bondholders, equity owners faced a dismal market environment. From the date of PCA’s IPO, which took place near the peak of one of the greatest stock market bubbles ever, to the bond-tender offer date, the S&P 500 declined a cumulative 24.3 percent.


Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Berlin Wall, Bernie Madoff, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, 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

However, the key is that the performance of passive indices is entirely a function of the market beta, or risk premium of a given asset class or other systematic strategy, rather than the active security selection of a manager, while superior performance by many alternative strategies is proving difficult to maintain over time. Reward: Vast Revenue Growth and Wealth Creation for Investment Managers Efficient market theory implies that no manager should be able to achieve outsized risk-adjusted returns consistently over time without a fundamental informational advantage, and much of the academic work regarding the performance of index funds, ETFs, mutual funds, and other investment vehicles discussed in this book seems to validate this thesis. However, this body of work seemingly fails to explain the phenomenon of a limited number of real-life, individual, independent money managers consistently beating the market or posting superior risk-adjusted returns year after year, at least for an extended period of their careers. It is worth turning our attention for a moment to the “ultrasuccessful” investment managers who have accumulated vast wealth and influence.

Put options increase in value when the index declines in price, so that serves to protect the portfolio against falling asset values. The sale of call options means that Madoff’s firm would exchange some of the upside (if stock prices increased) for a fixed amount of money. This strategy, of course, should involve reasonably low volatility (as it is buffered on both sides by the options trades). The problem? Such an approach could not possibly generate the risk-adjusted returns (that is, the return per given unit of 152 Investment: A History volatility) Bernie Madoff had claimed.15 Analyzing Madoff’s returns through one of the feeder funds that gave Madoff money to manage (Fairfield Sentry) reveals that Madoff claimed average annual returns of 10.59 percent with a volatility of just 2.45 percent (and a worst month of just—.64 percent) from December 1990 to October 2008.

In 2003, there were 170 REITs (134 of which traded on the New York Stock Exchange) valued at a combined $310 billion.46 Although liquid real estate vehicles, such as REITs, are extremely popular among investors, there are clear preferences among types of products. In one industry survey, some 68 percent of advisers responding said they had invested in such vehicles, but only 33 percent of them used nontraded REITs in their clients’ portfolios.47 This shows that liquidity preference, transparency, accountability, and ability to hold to a strict regulatory standard while still being able to achieve high risk-adjusted returns are important characteristics for investors in their alternative investment patterns. In many ways, though, REITs and their publicly traded shares are not much different than more traditional public companies—they just happen to be in the business of financing real estate and have a different wrapper and management structure around the assets. They do have some favorable tax advantages, including avoiding taxation at the trust level if more than 90 percent of the income is distributed to unitholders.


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

asset allocation, backtesting, Bernie Madoff, Black Swan, business cycle, buy and hold, 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: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, statistical model, stocks for the long run, 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

For example, recent studies (INGARM, 2009) have shown that through the use of alternative investments one can get access to investment opportunities and factor exposures that are not available through traditional asset classes. Alternative investments such as private equity, real estate, commodities, hedge funds, and CTAs offer a variety of return and risk characteristics: ■ ■ Positive alpha: A risk-adjusted return that exceeds the risk-adjusted return of traditional asset classes. Research has suggested that alternatives often obtain an excess return as providers of liquidity to new investors. Higher rate of return relative to other asset classes: Certain alternative asset (private equity, real estate) returns are derived from less liquid 62 ■ ■ THE NEW SCIENCE OF ASSET ALLOCATION investments. These alternative strategies have a higher expected rate of return as a function of the underlying risk of the investment (e.g., late stage private equity investment has a beta of about 1.25 to 1.5).

Use of the production data has the drawback of underweighting commodities like precious metals that are storable over a longer period and overweighting commodities like agricultural products that must be used over a shorter period. Bache Commodity Index (BCI): The primary objective of the BCI is to provide broad-based exposure to global commodity markets, with low turnover and strong risk-adjusted returns resulting from multiple return factors. The BCI employs a dynamic asset allocation strategy based on the price momentum of individual commodity markets. This approach to index construction may help reduce transaction costs and turnover, and may increase the risk-adjusted return. This index also incorporates a relative roll strategy that is similar to a synthetic spread trade, which will be profitable if the price of the contract closest to expiration falls in price relative to the longer maturity contracts. With the addition of Gasoil in February 2008, the BCI comprises 19 commodities that are traded on 7 major futures exchanges located in the U.S. and the UK.

Notes CHAPTER 5 Strategic, Tactical, and Dynamic Asset Allocation Asset Allocation Optimization Models Strategic Asset Allocation Tactical Asset Allocation Dynamic Asset Allocation Notes CHAPTER 6 Core and Satellite Investment: Market/Manager Based Alternatives Determining the Appropriate Benchmarks and Groupings Sample Allocations Core Allocation Satellite Investment Algorithmic and Discretionary Aspects of Core/Satellite Exposure Replication Based Indices Peer Group Creation—Style Purity Notes 58 59 61 66 70 71 74 82 84 88 91 92 99 101 107 109 110 111 117 119 120 120 122 126 132 Contents CHAPTER 7 Sources of Risk and Return in Alternative Investments Asset Class Performance Hedge Funds Managed Futures (Commodity Trading Advisors) Private Equity Real Estate Commodities Notes CHAPTER 8 Return and Risk Differences among Similar Asset Class Benchmarks Making Sense Out of Traditional Stock and Bond Indices Private Equity Real Estate Alternative REIT Investments Indices Commodity Investment Hedge Funds Investable Manager Based Hedge Fund Indices CTA Investment Index versus Fund Investment: A Hedge Fund Example Notes CHAPTER 9 Risk Budgeting and Asset Allocation Process of Risk Management: Multi-Factor Approach Process of Risk Management: Volatility Target Risk Decomposition of Portfolio Risk Management Using Futures Risk Management Using Options Covered Call Long Collar Notes CHAPTER 10 Myths of Asset Allocation Investor Attitudes, Not Economic Information, Drive Asset Values Diversification Across Domestic or International Equity Securities Is Sufficient vii 134 135 139 143 148 153 160 166 167 168 170 173 179 179 185 185 189 189 194 195 195 200 202 203 206 206 208 210 212 213 214 viii CONTENTS Historical Security and Index Performance Provides a Simple Means to Forecast Future Excess Risk-Adjusted Returns Recent Manager Fund Return Performance Provides the Best Forecast of Future Return Superior Managers or Superior Investment Ideas Do Not Exist Performance Analytics Provide a Complete Means to Determine Better Performing Managers Traditional Assets Reflect “Actual Values” Better Than Alternative Investments Stock and Bond Investment Means Investors Have No Derivatives Exposure Stock and Bond Investment Removes Investor Concerns as to Leverage Given the Efficiency of the Stock and Bond Markets, Managers Provide No Useful Service Investors Can Rely on Academics and Investment Professionals to Provide Current Investment Models and Theories Alternative Assets Are Riskier Than Equity and Fixed Income Securities Alternative Assets Such as Hedge Funds Are Absolute Return Vehicles Alternative Investments Such as Hedge Funds Are Unique in Their Investment Strategies Hedge Funds Are Black Box Trading Systems Unintelligible to Investors Hedge Funds Are Traders, Not Investment Managers Alternative Investment Strategies Are So Unique That They Cannot Be Replicated It Makes Little Difference Which Traditional or Alternative Indices Are Used in an Asset Allocation Model Modern Portfolio Theory Is Too Simplistic to Deal with Private Equity, Real Estate, and Hedge Funds Notes CHAPTER 11 The Importance of Discretion in Asset Allocation Decisions The Why and Wherefore of Asset Allocation Models Value of Manager Discretion 215 215 216 216 217 217 218 218 218 219 220 221 222 222 223 223 223 225 226 226 230 Contents Manager Evaluation and Review: The Due Diligence Process Madoff: Due Diligence Gone Wrong or Never Conducted Notes CHAPTER 12 Asset Allocation: Where Is It Headed?


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

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

Using the volatility of annual returns as the measure of risk, the lowest-cost funds carried significantly less risk (average volatility of 16.2 percent) than their highest-cost peers (17.4 percent). When we take that reduction in risk into account, the risk-adjusted annual return for the lowest-cost quartile comes to 8.9 percent, fully 1.5 percentage points higher than the 7.4 percent risk-adjusted return of the highest-cost quartile. The magic of compounding, again. That 1.5 percent annual advantage in risk-adjusted return may not seem like much. But when we compound those annual returns over time, the cumulative difference reaches staggering proportions. The compound return for the period is 855 percent for the lowest-cost funds and 632 percent for the highest-cost funds, an increase of more than 35 percent, a superiority arising almost entirely from the cost differential.

Taking into account both costs, we find that all-in annual costs of actively managed equity funds range from 0.9 percent of assets in the lowest-cost quartile to 2.3 percent in the highest-cost quartile, as shown in Exhibit 5.1. (This exercise ignores sales charges and therefore overstates the net returns earned by the funds in each quartile.) Costs matter. A lot. EXHIBIT 5.1 Equity Mutual Funds: Returns versus Costs, 1991–2016 Annual Rate Costs Cost Quartile Gross Return Expense Ratio Turnover (est.) Total Costs Net Return* Cumulative Return Risk** Risk- Adjusted Return One (lowest cost) 10.3% 0.71% 0.21% 0.91% 9.4% 855% 16.2% 8.9% Two 10.6 0.99 0.31 1.30 9.3 818 17.0 8.4 Three 10.5 1.01 0.61 1.62 8.9 740 17.5 7.8 Four (highest cost) 10.6 1.44 0.90 2.34 8.3 632 17.4 7.4 500 Index Fund 9.2% 0.04% 0.04% 0.08% 9.1% 783% 15.3% 9.1% *This analysis includes only funds that survived the full 25-year period. Thus, these data significantly overstate the results achieved by equity funds due to survivorship bias.

The gross return of the S&P 500 Index fund was 9.2 percent per year; the net return, 9.1 percent. Carrying a lower risk than any of the four cost quartiles (volatility 15.3 percent), its risk-adjusted annual return was also 9.1 percent, a cumulative gain that ranked the index fund ahead of even the lowest-cost quartile funds by 0.2 percent per year. If the managers take nothing, the investors receive everything: the market’s return. Caution: The index fund’s annual risk-adjusted return of 9.1 percent over the past 25 years is all the more impressive since the returns of the active equity funds are overstated (as always) by the fact that only the funds that were good enough to survive the decade are included in the data. Adjusted for this “survivorship bias,” the return of the average equity fund would fall from 9.0 percent to an estimated 7.5 percent. What’s more, selecting the index fund eliminated the need to search for those rare needles in the market haystack represented by the very few active funds that have performed better than that haystack, in the often-vain hope that their winning ways will continue over decades yet to come.


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

accounting loophole / creative accounting, airline deregulation, Andrei Shleifer, asset allocation, Bretton Woods, business cycle, buy and hold, 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, low cost airline, 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, stocks for the long run, survivorship bias, the market place, transaction costs, Y2K, yield curve, zero-sum game

International Allocation Chapter 7 Taking It to the Tilt 125 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1975 1.00 1.06 1.12 1.18 1.24 1.31 1.37 1.43 1.48 1.53 1.56 1976 –0.28 –0.14 0.00 0.16 0.33 0.50 0.66 0.83 0.98 1.12 1.25 1977 1.30 1.08 0.79 0.45 0.09 –0.25 –0.56 –0.81 –1.01 –1.16 –1.28 1978 1.24 1.25 1.24 1.19 1.09 0.94 0.74 0.53 0.34 0.18 0.04 1979 –0.68 –0.54 –0.40 –0.26 –0.11 0.02 0.16 0.28 0.40 0.51 0.60 1980 0.46 0.54 0.63 0.71 0.79 0.86 0.92 0.96 1.00 1.02 1.04 1981 –0.97 –1.04 –1.12 –1.19 –1.26 –1.33 –1.39 –1.43 –1.45 –1.44 –1.42 1982 –0.74 –0.63 –0.51 –0.37 –0.22 –0.07 0.09 0.23 0.36 0.48 0.58 1983 1.34 1.40 1.45 1.48 1.49 1.47 1.44 1.40 1.35 1.29 1.23 1984 –0.15 –0.15 –0.16 –0.17 –0.17 –0.17 –0.18 –0.18 –0.18 –0.18 –0.18 1985 4.06 4.29 4.45 4.45 4.25 3.88 3.42 2.93 2.48 2.09 1.76 1986 2.28 2.29 2.29 2.26 2.17 2.03 1.82 1.56 1.27 0.98 0.71 1987 0.76 0.71 0.65 0.58 0.52 0.45 0.38 0.32 0.26 0.20 0.14 1988 1.19 1.24 1.29 1.34 1.38 1.41 1.40 1.35 1.25 1.11 0.96 1989 0.12 0.24 0.38 0.53 0.69 0.87 1.05 1.23 1.39 1.53 1.64 continues 126 Table 7.4 continued International Allocation UNDERSTANDING ASSET ALLOCATION 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1990 –1.05 –1.05 –1.05 –1.04 –1.02 –0.99 –0.93 –0.86 –0.75 –0.63 –0.50 1991 0.32 0.43 0.54 0.66 0.79 0.91 1.04 1.15 1.26 1.35 1.43 1992 –1.19 –1.15 –1.10 –1.02 –0.92 –0.78 –0.58 –0.32 –0.02 0.29 0.56 1993 1.43 1.46 1.49 1.53 1.57 1.60 1.64 1.64 1.59 1.43 1.14 1994 0.23 0.20 0.16 0.13 0.09 0.04 0.00 –0.05 –0.10 –0.14 –0.19 1995 0.34 0.56 0.82 1.14 1.52 1.97 2.52 3.15 3.86 4.56 5.14 1996 –0.07 0.16 0.38 0.58 0.77 0.94 1.08 1.21 1.32 1.41 1.49 1997 –0.23 –0.05 0.14 0.34 0.54 0.75 0.94 1.13 1.30 1.45 1.58 1998 0.71 0.76 0.81 0.85 0.89 0.93 0.97 1.00 1.02 1.04 1.06 1999 1.50 1.52 1.52 1.51 1.50 1.47 1.42 1.37 1.31 1.25 1.18 2000 –1.56 –1.52 –1.46 –1.39 –1.31 –1.23 –1.14 –1.06 –0.97 –0.89 –0.81 2001 –1.61 –1.52 –1.43 –1.34 –1.25 –1.16 –1.07 –0.98 –0.90 –0.81 –0.73 2002 –1.01 –1.04 –1.07 –1.10 –1.12 –1.14 –1.15 –1.16 –1.17 –1.18 –1.18 2003 2.10 2.13 2.15 2.17 2.18 2.20 2.21 2.21 2.21 2.21 2.20 2004 1.63 1.61 1.59 1.57 1.55 1.52 1.49 1.45 1.40 1.36 1.30 Based on the sample period, these results suggest an all-or-nothing strategy that would have maximized risk-adjusted returns 80 percent of the time. The problem, however, with all-or-nothing is when you miss, you miss big (on a relative basis). Risk-averse investors may not be able to live with such a bipolar strategy, and might instead find it desirable to minimize their long-run volatilities relative to their long-run average returns. The SAA does just this: It allocates funds in proportion to asset-class market weights. But, if we take the long-run result seriously, a bipolar strategy should generate an average holding closer to each asset class’s market weight. This carries the promise of higher risk-adjusted returns. In simple terms, an optimal active strategy tilts around long-run values, giving us a time-consistent active strategy.

This suggests smallcap stocks, although delivering higher returns than large-caps during the sample period, were no more risky than large-caps. At the other end of the spectrum, we find growth stocks have a beta in excess of one while posting a negative alpha. The higher beta here suggests growth stocks had a higher systematic risk than the market. To make matters worse, this higher risk did not lead to superior risk-adjusted returns as measured by Jensen’s alpha. In fact, the large-cap growth portfolio for this period had a negative statistically significant coefficient; not only were growth stocks riskier, they also delivered a lower return. Given these 30-year statistics, one would be hard-pressed to make a case for the inclusion of growth stocks in a portfolio. Table 2.2 Risk measurements: 1975–2004. Risk-Adjusted Beta Annual Returns Jensen’s Alpha T-Statistics Sharpe Ratio Small-Cap 13.79% 1 5.64% 2.07 Large-Cap 8.13% 1 0.00% Growth 7.17% 1.06 –1.46% 1.87 0.43 Value 8.91% .93 0.81% 1.67 0.60 International 4.91% .62 –1.27% 0.04 0.29 0.65 0.53 Source: Research Insight, Morgan Stanley Capital Management, and Ibbotson Associates As for value stocks, they produced a somewhat lower beta than the market during the sample period, suggesting they have a lower systematic risk than the market.

Risk-Adjusted Beta Annual Returns Jensen’s Alpha T-Statistics Sharpe Ratio Small-Cap 13.79% 1 5.64% 2.07 Large-Cap 8.13% 1 0.00% Growth 7.17% 1.06 –1.46% 1.87 0.43 Value 8.91% .93 0.81% 1.67 0.60 International 4.91% .62 –1.27% 0.04 0.29 0.65 0.53 Source: Research Insight, Morgan Stanley Capital Management, and Ibbotson Associates As for value stocks, they produced a somewhat lower beta than the market during the sample period, suggesting they have a lower systematic risk than the market. Value stocks also appear to have shown a positive alpha, but only enough to be considered marginally significant at best. Once again, taken at face value, the results suggest value stocks have a lower systematic risk than the market but quite possibly offer higher risk-adjusted returns. International stocks exhibited what appears to be significantly lower beta and alpha coefficients for the period, with the alpha coming in at just about zero. This means that although international stocks offered a much lower systematic risk for the period, they did not produce additional excess return. One conclusion is this: The only possible contribution international stocks can make to a portfolio is as risk-reducing or diversification mechanisms. 20 UNDERSTANDING ASSET ALLOCATION Based on the statistics presented in Table 2.2, the CAPM investment implications are fairly straightforward: Avoid growth stocks in your portfolio, include some international stocks as a risk-reduction measure, take some value stocks also as a risk-reduction measure as well as an excess-return-producing measure, and add in small-cap stocks to generate some risk-adjusted excess returns (alpha).


pages: 537 words: 144,318

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

Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, 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, Kickstarter, 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, stocks for the long run, stocks for the long term, 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

While that may or may not be true, illiquidity needs to be reconsidered on a risk-adjusted basis, which includes the analysis of stressed scenarios and the impact on the overall portfolio in light of annual cash liabilities. Although the Endowment Model is not dead, the flaws and shortcomings exposed in 2008 need to be considered and adjusted for when building real money portfolios. Whether the performance of endowment style portfolios snap back quickly or not doesn’t matter; we have learned that risk-adjusted returns and drawdowns are important. If large drawdowns force action beyond the portfolio level (i.e., if the underlying institutions must take action because of portfolio losses), then it makes sense to do whatever is necessary to cut off that risk. Public Pension Goes Endowment In the fall of 2006, I was invited to attend an offsite meeting for a state pension fund that had just been given clearance, through a November 2006 ballot vote, to invest outside of the United States for the first time.

Ironically, conventional wisdom in the investment world holds that global macro hedge funds are risky while real money funds are prudent and safe. It is now clear that real money managers need to reorient their thought process and approach towards improving the portfolio construction process, especially if they have annual cash needs. Specifically, a more forward looking risk-based approach should be at the foundation of real money portfolios. Real money managers should:1. Replace return targets with risk-adjusted return targets. Big drawdowns and volatility matter. Focusing on return targets misses the damage to performance caused by large drawdowns and high volatility. Portfolios should be constructed such that extreme worst-case scenarios are accounted for and dealt with in the investment process, either through the use of overlays, hedges to cut off tail risk, or less aggressive asset allocation with truly diversifying exposures. 2.

This suggests the optimization exercise should incorporate an additional constraint of avoiding losses and drawdowns so large that they imperil the existence of an economic entity (e.g., banks and insurance companies) or cause a very severe dislocation in spending for entities with fixed spending commitments (pension, endowments, foundations). That implies a solution to the optimization exercise where risk-adjusted returns and volatility play a role. It is a second-best solution, but one that helps reduce the potential for a catastrophic outcome. This was neatly illustrated in a recent discussion I had with Larry Bacow, president of my alma mater, Tufts University. He mentioned that the universities with large endowments suffered disproportionately in the crash and now face significant cutbacks in spending as a result.


Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fixed income, implied volatility, index fund, interest rate swap, Long Term Capital Management, margin call, merger arbitrage, Nick Leeson, paper trading, performance metric, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Y Combinator, yield curve

Technical systems are easier to build and run, but in another example of barriers to entry the additional effort required for including fundamental rules is usually rewarded with higher returns. The examples in this book are all technical, but only because they are simpler to explain. Portfolio size There are successful traders who only ever trade one futures contract. At the other extreme large equity index funds could have thousands of holdings. Remember that the law of active management shows that diversification is the best source of additional risk adjusted returns. Both traders and investors should hold more positions when they can; ideally across several asset classes to get the greatest possible benefit. With larger portfolios you’re also less exposed to instrument specific problems such as bad data or temporary liquidity issues. However smaller portfolios make sense for semi-automatic traders or for those running entirely manual systems. As I’ll discuss in chapter twelve, ‘Speed and Size’, those with relatively small accounts also have to limit the number of positions they take.

Saving optimisation from itself Some insights from an alternative technique, bootstrapping, which can help us understand what is going wrong. Making weights by hand How to use a simple method called handcrafting to get portfolio weights. Incorporating Sharpe ratios Using additional information about expected performance to improve handcrafted weights. Optimising gone bad Introducing optimisation Portfolio optimisation will find the set of asset weights which give the best expected risk adjusted returns, usually measured by Sharpe ratio. The inputs to this are the expected average returns, standard deviation of returns, and their correlation. The standard method for doing this was first introduced by Harry Markowitz in the 1950s. It was a neat and elegant solution to a complex problem. Unfortunately it’s all too easy to be distracted by elegance, and forget the important assumptions underlying the maths.

A rule with a significantly negative Sharpe ratio either has very high trading costs and should be omitted, or it is consistently wrong and so should be inverted with longs and shorts reversed before incorporating it into the portfolio (although you’ll probably also want to consider the logic of your original idea before proceeding). 57. If you read the previous chapter you should recognise this as an out of sample expanding window. 71 Systematic Trading The calculation is done using the classic Markowitz optimisation; I find the maximum risk adjusted return (e.g. Sharpe ratio) using the estimated means and correlations, and standard deviations (which are all identical because I’ve used volatility standardisation). I also don’t allow weights to be negative and they have to sum up to exactly 100%. Figure 14 shows the weights calculated for each year.58 In the last throes of the late 1990s tech boom I naturally put all my money into the fast rising NASDAQ.


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Financial Market Meltdown: Everything You Need to Know to Understand and Survive the Global Credit Crisis by Kevin Mellyn

asset-backed security, bank run, banking crisis, Bernie Madoff, bonus culture, Bretton Woods, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, disintermediation, diversification, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Francis Fukuyama: the end of history, George Santayana, global reserve currency, Home mortgage interest deduction, Isaac Newton, joint-stock company, Kickstarter, liquidity trap, London Interbank Offered Rate, long peace, margin call, market clearing, mass immigration, money market fund, moral hazard, mortgage tax deduction, Northern Rock, offshore financial centre, paradox of thrift, pattern recognition, pension reform, pets.com, plutocrats, Plutocrats, Ponzi scheme, profit maximization, pushing on a string, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Ronald Reagan, shareholder value, Silicon Valley, South Sea Bubble, statistical model, The Great Moderation, the new new thing, the payments system, too big to fail, value at risk, very high income, War on Poverty, Y2K, yield curve

A new ‘‘science’’ of capital management grew up, again aided and abetted by management consultants and the statistical tools we have already seen. The big idea was something called risk adjusted return on capital or RAROC. This was basically a way of measuring what every dollar of capital used by a bank to support its businesses returned to the shareholders after adjusting for risk, that is, the probable losses. Other tools and concepts like shareholder value added or SVA also got traction. In theory, if a bank took capital out of a business with low-risk adjusted returns and put it into businesses with high-risk adjusted returns, its overall return on shareholder funds should be higher. So would its position on the banking food chain. It seemed like a good idea at the time. In fact, RAROC was riddled with the same problems as VAR in terms of reliance on risk models.

Banks became seized with a superstitious belief that complex mathematical models could better manage financial risk and return than human judgment. This thinking went well beyond the FICO score or the models used by the rating agencies to ‘‘stress test’’ default probabilities. Banks came to believe that they could design and implement data-driven ‘‘scientific’’ risk systems. The key concepts were ‘‘value at risk’’ or VAR and ‘‘risk adjusted return on capital’’ or RAROC. The basic idea was simple. Every loan, trading position, or operating exposure such as fraud or computer systems failure involved risks that Financial Innovation Made Easy could be identified and quantified with some precision across the whole institution. Risks were quantified by measuring the potential gap between the expected income from a loan or investment and the income actually received if things went wrong.

Again, fancy math gives the illusion of precision when it really depends on piles of assumptions and rules of thumb. Anyone in science or engineering who tries to model complex systems knows this, though few admit it out loud. The wonder of banking since the 1980s is that a simple business was made into a very complex system in the hope that it could be managed ‘‘by the numbers.’’ THE SECURITIZATION IMPERATIVE RAROC calculations made one thing very clear to the bank managements: If you want a high-risk adjusted return on capital, don’t do stuff that needs a lot of capital. Since bank balance sheet intermediation demands big capital buffers, lending money was by definition less capital efficient than ‘‘originating’’ loans for the structured finance sausage machine. Fee income did not eat up capital, so growing feebased businesses like payments and asset management were good things. Overall, anything that transferred what are called ‘‘risk-assets’’ off the bank balance sheet was a good thing.


All About Asset Allocation, Second Edition by Richard Ferri

activist fund / activist shareholder / activist investor, asset allocation, asset-backed security, barriers to entry, Bernie Madoff, buy and hold, capital controls, commoditize, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, equity premium, estate planning, financial independence, fixed income, full employment, high net worth, Home mortgage interest deduction, implied volatility, index fund, intangible asset, Long Term Capital Management, Mason jar, money market fund, mortgage tax deduction, passive income, pattern recognition, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, stocks for the long run, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve

Accordingly, double-digit interest rates in 1980 have dropped to less than 3 percent today. Stock performance going forward will not be as high as it was in the past 30 years because inflation is a large factor in long-term market returns. High starting inflation means higher nominal returns, and low starting inflation means lower nominal returns. A realistic and conservative return for stocks over inflation is 5 percent annually. MODEL 1: RISK-ADJUSTED RETURNS The risk-adjusted return model relies on historical market volatilities to forecast the relative future performance for various asset classes. Market returns can vary considerably over different periods of time, although the volatility of those returns is more consistent. In the long term, the volatility of a market can be used to forecast its returns relative to those of other markets with different risks.

., European, and Pacific Rim Stocks S&P 500 European Index Pacific Rim Index 1985 32.2 75.1 30.3 1986 18.5 42.7 74.1 1987 5.2 13.4 23.7 1988 16.8 15.4 33.0 1989 31.5 26.9 8.9 1990 –3.2 –1.0 –23.1 1991 30.6 14.7 22.9 1992 7.7 –3.6 –7.1 1993 10.0 29.5 53.1 1994 1.3 2.4 3.8 1995 37.4 22.6 7.1 1996 23.1 23.9 2.8 1997 33.4 21.8 –27.2 1998 28.6 24.5 –0.5 1999 21.0 19.2 52.5 2000 –9.1 –10.1 –21.6 2001 –11.9 –18.8 –19.4 2002 –22.1 –17.2 –7.9 2003 28.7 39.0 41.6 2004 10.9 21.7 22.8 2005 4.9 10.4 20.2 2006 15.8 34.6 19.7 2007 5.5 12.3 13.8 2008 –37.0 –47.3 –39.6 2009 26.5 35.8 24.2 CHAPTER 4 70 U.S. dollars from the three global areas: the United States, Europe, and the Pacific Rim. The shaded cell represents the geographical region that had the highest-returning index for that year. The region of the world with the highest return varies over time. There is no pattern recognition or formula that can be used to predict the region that will outperform next. Allocating assets across these three regions has been a good strategy. It created a better risk-adjusted return than having only U.S. equities. Figure 4-2 shows a risk-and-return diagram for U.S. stocks and international stocks using 10 percent increments, starting with no international stocks on the left side and ending with only international stocks on the right side. The international index I used is split evenly between the Pacific Rim and Europe, rebalanced annually. The international composite index dates back to 1973.

Most analysts and economists agree to disagree on every element of market forecasting ranging from the methodology used to the modeling techniques to the input into those equations. Interestingly, despite differences of opinion and techniques, most long-term forecasts tend to fall within a narrow range of returns. At the end of this chapter is my own forecast of long-term market risk and return. FORECASTING MARKET RETURNS There are two basic market forecasting methodologies discussed in this chapter. The first method is a risk-adjusted return model that relies on historical price volatility in part to forecast the future performance of various asset classes relative to one another. The second method is an economic top-down model that relies on a long-term forecast of gross domestic product (GDP) to forecast various asset-class returns. Forecasting a market’s future return always involves the analysis of historical risk and return.


pages: 302 words: 84,428

Mastering the Market Cycle: Getting the Odds on Your Side by Howard Marks

activist fund / activist shareholder / activist investor, Albert Einstein, business cycle, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, financial innovation, fixed income, if you build it, they will come, income inequality, Isaac Newton, job automation, Long Term Capital Management, margin call, money market fund, moral hazard, new economy, profit motive, quantitative easing, race to the bottom, Richard Feynman, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, secular stagnation, short selling, South Sea Bubble, stocks for the long run, superstar cities, The Chicago School, The Great Moderation, transaction costs, VA Linux, Y2K, yield curve

Thus there won’t be particular points on the continuum where risk-bearing is rewarded much more or much less than at others (that is, investments whose promised risk-adjusted return is obviously superior to the rest). In a rational world, any violations of these provisions would cause capital to move such that the prices of mispriced assets are bid up or pushed down. As a result: the violations would be corrected, all investments would offer risk-adjusted returns that are fair relative to each other, and investors could increase their returns only by increasing the amount of risk they bear. If investors always behaved that way, their actions would cause the world to be marked by “efficient markets” where no investment offers a better risk-adjusted return than any other. Of course markets don’t always operate as they’re supposed to—things certainly aren’t always priced right—but the general suggestion of efficiency is too logical to be disregarded.

Investors without a view on the prospects for a given stock were willing to buy bonds convertible into it, as long as they were able to short the underlying shares in an appropriate “hedge ratio” (see my memo “A Case in Point,” June 2005). Convertible arbitrageurs reported outstanding risk-adjusted returns in all kinds of market environments . . . until so much money and so many competitors were attracted to the strategy that no one could find positions as attractively priced as those of the past. The important lesson is that—especially in an interconnected, informed world—everything that produces unusual profitability will attract incremental capital until it becomes overcrowded and fully institutionalized, at which point its prospective risk-adjusted return will move toward the mean (or worse). And, correspondingly, things that perform poorly for a while eventually will become so cheap—due to their relative depreciation and the lack of investor interest—that they’ll be primed to outperform.

(Jeff Saut, “Being Wrong and Still Making Money,” Seeking Alpha, March 13, 2017, emphasis added) XVI The Cycle in Success * * * * * * The important lesson is that—especially in an interconnected, informed world—everything that produces unusual profitability will attract incremental capital until it becomes overcrowded and fully institutionalized, at which point its prospective risk-adjusted return will move toward the mean (or worse). And, correspondingly, things that perform poorly for a while eventually will become so cheap—due to their relative depreciation and the lack of investor interest—that they’ll be primed to outperform. Cycles like these hold the key to success in investing, not trees that everyone is assuming will grow to the sky. Hopefully you’re now equipped for the task of recognizing, assessing and responding to cycles.


pages: 263 words: 75,455

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

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, 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

“Value” represents the portfolio formed from highest ranked decile, “MF (5)” represents the portfolio formed from the middle decile, and “Glamour” is the portfolio formed from the lowest ranked decile. Figure 2.2 and Table 2.2 demonstrate that the Magic Formula does quite well ranking the stocks. The first and best decile according to the Magic Formula outperforms the worst and last decile. The better deciles also tend to outperform with lower volatility, measured by standard and downside deviation, which leads to better risk-adjusted returns, represented by higher Sharpe and Sortino ratios. TABLE 2.2 Glamour, Middle, and Value Decile Performance Statistics: Magic Formula Strategy (1964 to 2011) As the figures and tables demonstrate, Greenblatt's Magic Formula has consistently outperformed the market, and with lower relative risk than the market. The nature of compounding means that, over long periods of time, small edges can result in big differences in returns.

An edge, some patience, and the magic of compounding translate into serious profits. Our study of the Magic Formula shows that analyzing stocks according to some proxy for price (e.g., a “bargain” or a “fair” price), and some proxy for quality (e.g., a “good” business or a “wonderful” company) can help us to identify value, and provide us with an edge, that can lead to outperformance and excellent risk-adjusted returns. Naturally, we wondered if we could improve on the outstanding performance delivered by the Magic Formula. Are there other simple, logical strategies that can do better? IT's ALL ACADEMIC: IMPROVING QUALITY AND PRICE We have created a generic, academic alternative to the Magic Formula that we call “Quality and Price.” Quality and Price is the academic alternative to the Magic Formula because it draws its inspiration from academic research papers.

He wanted to find some measure that would adjust the return for the risk taken to generate it. He created the Sharpe ratio, which does this by examining the historical relationship between excess return—the return in excess of the risk-free rate—and volatility, which stands in for risk. The higher the Sharpe ratio, the more return is generated for each additional unit of volatility, and the better the price metric. The Sortino ratio, like the Sharpe ratio, measures risk-adjusted return. The difference is that the Sortino ratio only measures downside volatility, while the Sharpe ratio measures both upside and downside volatility. The Sortino ratio doesn't adjust return for upside volatility, only for downside volatility, which we wish to avoid. The Sortino ratio also measures excess returns in excess of a minimum acceptable return. We use 5 percent per year as the minimum acceptable return in our analysis.


pages: 130 words: 32,279

Beyond the 4% Rule: The Science of Retirement Portfolios That Last a Lifetime by Abraham Okusanya

asset allocation, diversification, diversified portfolio, high net worth, longitudinal study, market design, mental accounting, Paul Samuelson, quantitative easing, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, transaction costs

Impact of alpha on SWR One common question about the SWR framework is what’s the impact of superior investment return, also known as alpha, on sustainable withdrawals? The logic goes that sustainable withdrawal rates are calculated using market returns (ie beta), so a retiree should be able to improve SWR by achieving higher risk-adjusted returns (ie alpha). Alpha has the opposite effect that fees and charges have on sustainable withdrawal rate. A 1% improvement in risk-adjusted return over and above an index-based portfolio, results in a 0.5% improvement in withdrawal rate. Bengen (2006) framed this as the impact of the ‘super investor’ who generates portfolio alpha. In practice, alpha is rare. If it does exist, it’s a very shy animal. It’s not like investment costs, which we know in advance and where we can see the impact.

A reason value and small-cap equities improved the withdrawal rate is simply because they deliver higher returns than the overall equity market. Fig. 60 below summarises the annualised return, standard deviation, maximum gain and loss between 1900-2016. Fig. 60: Annualised return, standard deviation, maximum gain and loss for large, small and value equities (1900-2015) This dataset shows us that value and small equities do outperform, but with slightly higher volatility. They do seem to deliver higher risk-adjusted return though. I’ll leave the debate about exactly why value and small premium exists to financial academics. But there’s extensive empirical research to show that value and small-cap premium does exist. Dimson, Marsh and Staunton48 addressed the question by noting that, ‘The key question is whether the size premium will continue in the future. This is unanswerable since we cannot travel forward in time.


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

Albert Einstein, anti-communist, asset allocation, beat the dealer, Benoit Mandelbrot, Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, capital asset pricing model, Claude Shannon: information theory, computer age, correlation coefficient, diversified portfolio, Edward Thorp, en.wikipedia.org, Eugene Fama: efficient market hypothesis, 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

The theorists were not even saying, necessarily, that all the market-beaters are simply lucky. There are ways to boost return by accepting greater risk. One is to use leverage. A very aggressive investor might borrow money to buy more stock than he could otherwise. This multiplies the expected return—and also multiplies the risk. For these reasons, the notion of a superior investor needs to be carefully qualified. The hallmark has to be a market-beating risk-adjusted return, achieved not through luck but through some logical system. It was concrete evidence of this that the economists failed to find. A name that occurs to many people today is Warren Buffett. “I’d be a bum in the street with a tin cup if the markets were efficient,” Buffett once said. Buffett had already made a name for himself with a successful hedge fund and had founded Omaha-based Berkshire Hathaway when Samuelson wrote that “a loose version of the ‘efficient market’ or ‘random walk’ hypothesis accords with the facts of life.”

Shannon’s Demon The chart’s upper line shows the value of a 50–50 stock and cash portfolio that is rebalanced each time unit. This line trends upward. The dollar scale on this chart is logarithmic, so the straight trend line actually means exponentially growing wealth. The rebalanced portfolio is also less volatile than the stock. The scale of the jitters is relatively less for the rebalanced portfolio than for the stock itself. Shannon’s rebalancer is not only achieving a superior return, but a superior risk-adjusted return. How does Shannon’s stock system work? Does it work? Shannon’s system bears a telling similarity to a great puzzle of physics. In his 1871 book Theory of Heat, British physicist James Clerk Maxwell semiseriously described a perpetual motion machine. The machine could be as simple as a container of air divided into two chambers by a partition. There is a tiny trapdoor in the partition.

It is an important idea that has been studied by such economists as Mark Rubinstein and Eugene Fama (who were apparently unaware of Shannon’s unpublished work). Rubinstein demonstrated that given certain assumptions, the optimal portfolio is always a constant-proportion rebalanced portfolio. This is one reason why it makes sense for ordinary investors to periodically rebalance their holdings in stocks, bonds, and cash. You get a slightly higher risk-adjusted return than you would otherwise. Commissions and capital gains taxes cut into this benefit, though. In recent years, Stanford information theorist Thomas Cover has built ingeniously on Shannon’s idea of the constant-proportion rebalanced portfolio. Cover believes that new algorithms can render the concept profitable, even after trading costs. Shannon’s main point in his talk, however, may have been to refute the then-common belief that the random walk of stock prices is an absolute barrier to making greater-than-market returns.


pages: 369 words: 128,349

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

3Com Palm IPO, Andrei Shleifer, asset allocation, buy and hold, 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

The sample includes both pure cash and pure stock bids, and bids that seek less than 100 percent of the target as long as the acquisition will result in the acquirer holding the entire 100 percent. For example, a company holding 75 percent of the target may seek to acquire the remaining 25 percent through a merger or a tender offer. For this sample of merger arbitrage deals, the risk-adjusted return is 0.7 percent per month if transaction costs and other slippages are not considered. The risk-adjusted return falls to 0.3 percent per month if all impediments and costs of transacting are considered. Further, the risk of the arbitrage portfolio is close to zero in stable or appreciating markets, but the systematic risk as measured by beta jumps to 0.50 in downtrending markets. Some other interesting information relating to the success of merger arbitrage is given in Table 9.3.4 Table 9.3 Success of Merger Arbitrage Total number of first bids Arbitrageur loses money First bid fails Target not bought out Targets taken over Hostile Deals Cash Deals Stock Deals 3,434 778 (23%) 606 (18%) 478 (14%) 86% 1,815 356 (20%) 295 (16%) 272 (15%) 85% 475 143 (30%) 173 (36%) 131 (28%) 72% It can be observed that the failure rate is high in the case of hostile deals.

Does it imply that the next stock deleted from the index is likely to appreciate? Yes. But will it appreciate? Maybe not. Similarly, the results do not imply that the next twenty stocks deleted from the index will necessarily appreciate, though they are likely to. But the results do imply that if you follow this strategy for the next two to three years and no significant changes take place in how the market reacts to these deletions, then you will earn risk-adjusted returns that are larger than the normal return. However, an unsuccessful run of any mispricing can cost the investor a significant loss of capital. POSITIVE ABNORMAL RETURNS DO NOT MEAN POSITIVE RETURNS The anomalous evidence presented generally focuses on abnormal returns. Since an abnormal return is the actual return minus the normal return, the actual return could be negative even though the abnormal return is positive.

Accordingly, all future analysis is presented based on the simple strategy of holding sectors above the mean return. Until now, the risk level of the positions was ignored. However, it is important to take risk into account because industry-momentum-based trading strategies are clearly riskier than holding a 91 92 Beyond the Random Walk broader market portfolio. The Sharpe ratio is used to compare the risk-adjusted returns.3 Sharpe ratios are reported below based on annual returns for the best-case scenarios, an average return of 4 percent for short-term Treasury bills during 1997–2001, and standard deviations (reported in parentheses) in Table 5.4. 25-week estimation period and 5-week holding period 5-week estimation period and 5-week holding period 5-week estimation period and 1-week holding period S&P 500 holding return 0.91 0.76 1.05 0.49 Since higher Sharpe ratios indicate superior investment, the best results are for the one-week holding period, with a ratio of 1.05.


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

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

Month 5 Stock 1 Stock 2 Month 4 Stock 3 Month 3 Month 2 Stock n Time Month 1 Predictor Variables FIGURE 7.7 Cross-sectional time series study. Predictability Studies Contradicting Semistrong EMH. The semistrong form of EMH is the boldest testable version of EMH.44 It asserts that no information in the public domain, fundamental or technical, can be used to generate risk-adjusted returns in excess of the market index. The bottom line of numerous well-conducted cross-sectional time series studies is this: Price movements are predictable to some degree with 352 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS stale public information, and excess risk-adjusted returns are possible. Here, I summarize some of these key findings: • Small capitalization effect: A stock’s total market capitalization, defined as the number of shares outstanding multiplied by its price per share, is predictive of future returns.45 Stocks in a portfolio composed of the lowest decile portfolio of market capitalization earned about 9 percent per year more than stocks in the highest decile portfolio.46 This effect is most pronounced in the month of January.

For example, geology’s dominant theory, plate tectonics, may predict that specific geologic formations that were created millions of years ago would be observed if an investigation of some specific location were to be carried out tomorrow. In finance, the efficient market’s hypothesis predicts that if a TA rule were to be back tested, its profits, after adjustment for risk, would not exceed the risk adjusted return of the market index. Once the prediction has been deduced from the hypothesis, the operations necessary to produce the new observations are carried out. They may involve a visit to the location of the predicted geologic formation or the back test of the TA rule. Then it becomes a matter of comparing prediction with observation. Measuring the degree of agreement between observation and prediction and making the decision about whether the hypothesis is left intact or falsified is what statistical analysis is all about.

Perhaps this explains the defensive, unscientific response of those who support the efficient markets hypothesis. When observations collided with their favorite theory, they tried to save it from falsification by reducing its information content. As mentioned earlier, its least informative version, EMH weak, predicts 142 METHODOLOGICAL, PSYCHOLOGICAL, PHILOSOPHICAL, STATISTICAL FOUNDATIONS that investment strategies based on TA32 will not be able to earn risk adjusted returns that beat the market index. When EMH supporters were faced with studies showing that TA-based strategies were able to earn excess returns,33 they responded by trying to immunize their theory from falsification. They did so by inventing new risk factors and claimed that the excess returns earned by TA were merely compensation for risks inherent in pursuing such a strategy. In other words, EMH defenders claimed that investors who followed the TA strategy were exposing themselves to a risk that was specific to that strategy.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

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

Multiprocessing 20.4 Atoms and Molecules 20.5 Multiprocessing Engines 20.6 Multiprocessing Example Exercises Reference Bibliography Notes Chapter 21 Brute Force and Quantum Computers 21.1 Motivation 21.2 Combinatorial Optimization 21.3 The Objective Function 21.4 The Problem 21.5 An Integer Optimization Approach 21.6 A Numerical Example Exercises References Chapter 22 High-Performance Computational Intelligence and Forecasting Technologies 22.1 Motivation 22.2 Regulatory Response to the Flash Crash of 2010 22.3 Background 22.4 HPC Hardware 22.5 HPC Software 22.6 Use Cases 22.7 Summary and Call for Participation 22.8 Acknowledgments References Notes Index EULA List of Tables Chapter 1 Table 1.1 Table 1.2 Chapter 2 Table 2.1 Chapter 5 Table 5.1 Chapter 13 Table 13.1 Chapter 14 Table 14.1 Chapter 16 Table 16.1 Chapter 17 Table 17.1 List of Illustrations Chapter 2 Figure 2.1 Figure 2.2 Figure 2.3 Chapter 3 Figure 3.1 Figure 3.2 Chapter 4 Figure 4.1 Figure 4.2 Figure 4.3 Chapter 5 Figure 5.1 Figure 5.2 Figure 5.3 Figure 5.4 Figure 5.5 Chapter 6 Figure 6.1 Figure 6.2 Figure 6.3 Chapter 7 Figure 7.1 Figure 7.2 Figure 7.3 Chapter 8 Figure 8.1 Figure 8.2 Figure 8.3 Figure 8.4 Chapter 9 Figure 9.1 Figure 9.2 Chapter 10 Figure 10.1 Figure 10.2 Figure 10.3 Chapter 11 Figure 11.1 Figure 11.2 Chapter 12 Figure 12.1 Figure 12.2 Chapter 13 Figure 13.1 Figure 13.2 Figure 13.3 Figure 13.4 Figure 13.5 Figure 13.6 Figure 13.7 Figure 13.8 Figure 13.9 Figure 13.10 Figure 13.11 Figure 13.12 Figure 13.13 Figure 13.14 Figure 13.15 Figure 13.16 Figure 13.17 Figure 13.18 Figure 13.19 Figure 13.20 Figure 13.21 Figure 13.22 Figure 13.23 Figure 13.24 Figure 13.25 Chapter 14 Figure 14.1 Figure 14.2 Figure 14.3 Chapter 15 Figure 15.1 Figure 15.2 Figure 15.3 Chapter 16 Figure 16.1 Figure 16.2 Figure 16.3 Figure 16.4 Figure 16.5 Figure 16.6 Figure 16.7 Figure 16.8 Chapter 17 Figure 17.1 Figure 17.2 Figure 17.3 Chapter 18 Figure 18.1 Figure 18.2 Chapter 19 Figure 19.1 Figure 19.2 Figure 19.3 Chapter 20 Figure 20.1 Figure 20.2 Chapter 21 Figure 21.1 Chapter 22 Figure 22.1 Figure 22.2 Figure 22.3 Figure 22.4 Figure 22.5 Figure 22.6 Figure 22.7 Figure 22.8 Figure 22.9 Figure 22.10 About the Author Dr. Marcos López de Prado manages several multibillion-dollar funds using machine learning (ML) and supercomputing technologies. He founded Guggenheim Partners’ Quantitative Investment Strategies (QIS) business, where he developed high-capacity strategies that consistently delivered superior risk-adjusted returns. After managing up to $13 billion in assets, Marcos acquired QIS and spun-out that business from Guggenheim in 2018. Since 2010, Marcos has been a research fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy, Office of Science). One of the top-10 most read authors in finance (SSRN's rankings), he has published dozens of scientific articles on ML and supercomputing in the leading academic journals, and he holds multiple international patent applications on algorithmic trading.

A practical application of HRP is to determine allocations across multiple machine learning (ML) strategies. 16.2 The Problem with Convex Portfolio Optimization Portfolio construction is perhaps the most recurrent financial problem. On a daily basis, investment managers must build portfolios that incorporate their views and forecasts on risks and returns. This is the primordial question that 24-year-old Harry Markowitz attempted to answer more than six decades ago. His monumental insight was to recognize that various levels of risk are associated with different optimal portfolios in terms of risk-adjusted returns, hence the notion of “efficient frontier” (Markowitz [1952]). One implication is that it is rarely optimal to allocate all assets to the investments with highest expected returns. Instead, we should take into account the correlations across alternative investments in order to build a diversified portfolio. Before earning his PhD in 1954, Markowitz left academia to work for the RAND Corporation, where he developed the Critical Line Algorithm.

Figure 16.8 Correlation matrix before and after clustering The methodology described in this chapter can be applied to problems beyond optimization. For example, a PCA analysis of a large fixed income universe suffers the same drawbacks we described for CLA. Small-data techniques developed decades and centuries ago (factor models, regression analysis, econometrics) fail to recognize the hierarchical nature of financial big data. Kolanovic et al. [2017] conducted a lengthy study of HRP, concluding that “HRP delivers superior risk-adjusted returns. Whilst both the HRP and the MV portfolios deliver the highest returns, the HRP portfolios match with volatility targets much better than MV portfolios. We also run simulation studies to confirm the robustness of our findings, in which HRP consistently deliver a superior performance over MV and other risk-based strategies […] HRP portfolios are truly diversified with a higher number of uncorrelated exposures, and less extreme weights and risk allocations.”


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Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market by Steven Drobny

Albert Einstein, asset allocation, Berlin Wall, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, buy low sell high, capital controls, central bank independence, commoditize, commodity trading advisor, corporate governance, correlation coefficient, Credit Default Swap, diversification, diversified portfolio, family office, fixed income, glass ceiling, high batting average, implied volatility, index fund, inflation targeting, interest rate derivative, inventory management, John Meriwether, Long Term Capital Management, margin call, market bubble, Maui Hawaii, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shale / tar sands, oil shock, out of africa, paper trading, Paul Samuelson, Peter Thiel, price anchoring, purchasing power parity, reserve currency, risk tolerance, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, The Wisdom of Crowds, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond, zero-sum game

See also Siva-Jothy, Christian Protectionism, 44 Putin,Vladimir, 237–238 Put options, 63–64, 77, 85, 236, 331, 334 Quantitative analysis, 346 Quantum Endowment, 28 Quantum Fund, 28, 30, 184, 217, 271, 273, 277–278 RAROC (risk-adjusted return on capital), 39 Raw material boom, 239 Reading recommendations, 92, 158–159 INDEX Real estate, 62–63, 242. See also Home builders; Housing bubble Real estate investment trusts (REITs), 63, 290 Real money, 53, 62, 70 Recession, 14, 115, 264 Redemptions, 58, 69 Relative value (RV), 25, 68, 126–127, 141–142, 147–148, 156, 173, 310–311, 330, 336, 337, 346 Reminiscences of a Stock Operator (Lefevre), 92, 244 Researcher. See Drobny,Andres, Dr. Return(s), 55, 99, 254, 344–346. See also Absolute returns Reverse repurchase agreements, 51 Reward/risk ratio, 174 Reward-to-volatility ratio, 342 Risk-adjusted returns, 254, 344–346 Risk arbitrage, 33, 80–81. See also Arbitrage Risk aversion, 107, 115 Risk capital, 24 Risk curve, 328 Risk-free rate, 196, 342 Risk management strategies, 7–8, 25, 32, 50, 53–54, 61–62, 131, 137–138, 172, 192, 204, 206, 213–214, 285, 293–294, 333–334 Risk premium, 55 Risk/reward analysis, 98, 110–111, 126, 296 Risk-to-return ratio, 62 Robertson, Julian, xi, 8, 10, 21, 23, 27–28, 245–247, 277 Roditi, Nick, 269, 278 Rogers, Jim, 8, 210, 217–221, 223–239, 269–271, 278 Rogers International Commodity Index, 218 Rubin, Robert, 32, 245 Rumors, 249, 268 Russia/Russian rubles, 22–23, 49–50, 64–65, 203–204, 211, 237–238, 277, 280, 283, 286, 289, 290 Russian crisis 1998, 10, 21–23, 26, 54, 64, 80, 292–294, 299–300, 310 Russian Equity Index, 65 Russian stock market index (RTSI$), 22 INDEX S&P 500 index, 27–28, 193, 212–213, 217, 273–274, 282 Safe harbor, 205 Salomon Brothers, 24 Samuelson, Paul, 9 Scholes, Myron, 24, 207 Secular trends, 234 Sell-offs, 20, 118, 175, 295, 302 SemperMacro, 71, 72 Seykota, Ed, 9 Sharpe ratio, 62, 342–343 Short-dated volatility, 55 Short positions, xii, 58.

Whereas significant assets under management can prove an issue for some more focused investing styles, it is not a particular hindrance to global macro hedge funds given their flexibility and the depth and liquidity in the markets they trade. Although macro traders are often considered risky speculators due to the large swings in gains and losses that can occur from their leveraged directional bets, when viewed as a group, global macro hedge fund managers have produced superior risk-adjusted returns over time. From January 1990 to December 2005, global macro hedge funds have posted an average annualized return of 15.62 percent, with an annualized standard deviation of 8.25 percent. Macro funds returned over HFRI Macro Index Growth of $1,000 $10,000 $9,000 $8,000 $7,000 $6,000 $5,000 $4,000 $3,000 $2,000 $1,000 $0 Initial 1990 1991 1992 1993 1994 1995 1996 HFRI Macro Index FIGURE 1.1 Source: HFR. 1997 1998 1999 2000 2001 S&P 500 w/ dividends Comparison of HFRI Macro Index with S&P 500 2002 2003 2004 2005 4 INSIDE THE HOUSE OF MONEY 500 basis points more than the return generated by the S&P 500 index for the same period with more than 600 basis points less volatility.

If you’re a pattern guy, you’re looking at the pattern and can see it developing, which is when you’re loading up. At the end, when everybody’s on it and volatility has been cranked up because it’s already been recognized, that’s when you are selling.” Tell me about your matrix. Global macro is the matrix. It’s my mental picture of how money flows around the world, the flow of funds on a global basis seeking out the highest risk-adjusted return. Money is always moving somewhere, whether it’s from oil to currencies to metals or to stocks. I follow it. The only time when global money doesn’t flow is when it’s afraid, like during 1998 or after 9/11, and then it heads to Switzerland. Markets are based on two emotions: fear and greed.The greed part is nice when you’re a part of it and are invested, but when fear takes over, it’s an unbelievable thing.


pages: 321

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

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

Alpha, of course, is the first letter of the Greek alphabet – as in “the alpha and the omega,” the beginning and the end – and it lurks inside the word “alphabet.” Over the centuries, it has attached itself to a variety of scientific terms. The financial use of the word “alpha” goes back to 1968, when Michael Jensen, then a young PhD economics candidate at the University of Chicago, coined the phrase “Jensen’s alpha” in a paper he published in The Journal of Finance. Jensen’s alpha measured the risk-adjusted returns of a portfolio and determined whether it was performing better or worse than the expected market. Eventually, Jensen’s alpha evolved into a measure of investment performance known simply as alpha, and it is most commonly used to describe returns that exceed the market or a benchmark index. Since then, the term “alpha” has been widely adopted throughout the investing world, particularly by hedge funds, to refer to the unique “edge” that they claim can generate returns that beat the market.

Investing via modeling and smart data processing doesn’t seem like much of a stretch 40 years after the launch of the first systematic hedge funds. Still, the path toward quantitative investing becoming an established strategy was not a straight line. The quant meltdown of 2007 dealt a particular blow to investor and participant confidence in the ability of quantitative investing to produce credible long-term risk-adjusted returns: in August of that year, a market panic prompted a large number of quant funds to liquidate their positions in a short period of time, creating an unprecedented drawdown and causing some participants and investors to head for the exits in what amounted to a stampede. That period was followed the next year by the global financial crisis, which again saw significant investment return volatility.

According to the illiquidity premium principle, because portfolio performance is measured after costs, investors require excess returns from illiquid assets to cover their losses from buying higher (at the ask price) and selling lower (at the bid price). The theory was first confirmed by Amihud and Mendelson (1986), showing that, on average, a 1% increase in the spread is associated with a 0.211% increase in monthly risk-adjusted returns. Hence, a strategy applied to assets with high spreads yields increased returns in exchange for a fixed cost: in line with the aforementioned results, a 1% extra fixed cost as a result of increased spread is counterbalanced by the elevated return over a excess spread 1 , or roughly five months. In period of monthly excess return 0.211 other words, buy-and-hold investors with at least a five-month-long investment horizon, or investors trading daily alphas with turnover less monthly excess return 0.211 number of days in a month 21 , or roughly 1%, seek a profit than excess spreadd 1 by investing in assets with a higher spread.


pages: 345 words: 87,745

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

asset allocation, backtesting, Bernie Madoff, buy and hold, 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

Here is how the Treynor Ratio works: Take any portfolio’s return and subtract a risk-free rate of return (usually the Treasury bill yield); then divide the result by the portfolio’s beta. The result is the ratio of a portfolio’s excess return to market risk as measured by the portfolio’s beta. The Treynor Ratio can be used to compare many portfolios to one another and sort the results by the best risk-adjusted returns. The funds with the highest ratios have the highest returns per unit of market risk; this is an indication of the managers who may have skill. The ratio is used to weed out bad managers from potential good ones, thus making an investment consultant’s job much easier. William Sharpe developed a similar formula for evaluating risk-adjusted returns of portfolios. Ironically, rather than using his own beta formula as the denominator in the equation, Sharpe used a portfolio’s standard deviation of return. Perhaps this was because Treynor beat him to the punch. Sharpe’s formula became known as the Sharpe Ratio.

Net of fees and risk-adjusted, 39 surviving funds beat the market while 76 funds underperformed. That’s a 1-to-2 win-loss ratio in the funds before the risk adjustment and a 1-to-2 win-loss ratio after risk adjustment. The median winning fund outperformed the market on a risk-adjusted basis by 0.6 percent while the median losing fund underperformed the market by 1.6 percent. It is interesting to note in Jensen’s study that fund managers fared no better or worse in their risk-adjusted returns than their unadjusted performance. Jensen summed up his findings in his study’s abstract: The evidence on mutual fund performance indicates not only that these 115 mutual funds were on average not able to predict security prices well enough to outperform a buy-the-market-and-hold policy, but also that there is very little evidence that any individual fund was able to do significantly better than that which we expected from mere random chance.13 Alpha is an indication of a manager’s potential skill.

These funds can experience higher share-price volatility than diversified funds because sector funds are subject to issues specific to a given sector. Securities and Exchange Commission (SEC) The federal government agency that regulates mutual funds, registered investment advisors, the stock and bond markets, and broker-dealers. The SEC was established by the Securities Exchange Act of 1934. Sharpe ratio A measure of risk-adjusted return. To calculate a Sharpe ratio, an asset’s excess return (its return in excess of the return generated by risk-free assets such as Treasury bills) is divided by the asset’s standard deviation. It can be calculated compared to a benchmark or an index. short sale The sale of a security or option contract that is not owned by the seller, usually to take advantage of an expected drop in the price of the security or option.


pages: 299 words: 92,782

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

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

So unlike sports, where there are some observable measures of skill (such as hitting a baseball), all we can really say today is that we cannot explain results by luck alone and that it appears that skill plays a role when companies earn a high return on their assets. The authors caution, though, that it is very easy to confuse superior performance with the results you would expect from luck.21 Investing is another endeavor where we could benefit from teasing apart skill and luck. We can define skill as the ability to take actions that will predictably generate a risk-adjusted return in excess of an appropriate benchmark, such as the S&P 500, over time. It is impossible for investment managers to generate returns in excess of the benchmark in the aggregate. The reason is that the market's return must simply be the sum total of the results of the managers (or close to it). Since managers charge fees for their services, the return to investors is less than that of the market.

So the coefficient of correlation provides practical information about the relationship between scoring runs and winning games.4 The process of determining which statistics are useful begins with a definition of your objective: What do you want to use the statistics for? In sports, the object is to win the game. In investing, it is making money; or to put it more technically, generating risk-adjusted returns in excess of some benchmark over time. Knowing your objective is important because it's hard to chart a course without knowing the destination. Next, you have to determine what factors contribute to achieving your objective. To do so, you have to translate a theory of cause and effect into quantities that you can observe and measure. This allows you to assess how skill, measured as high persistence, translates into your objective, measured as high predictive value.5 It is now easy to see why a shareholder may have some concern about the CEO's emphasis on high quality.

, which rates mutual funds on a five-star scale. Researchers who have studied initial ratings and changes in those ratings report that investors put more money than normal into funds that receive positive ratings or upgrades of their ratings, and withdraw money from funds with low ratings or downgrades of ratings.23 The star rating system is a forced normal distribution based on prior, risk-adjusted returns. For example, the top 10 percent of funds earn a rating of five stars, the next 22.5 percent four stars, and the middle 35 percent three stars, the following 22.5 percent two stars, and the bottom 10 percent one star. Morningstar weights the results according to the longevity of the fund, so the full track record of a fund with a long history has a greater weight than its recent performance.


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

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

The Great Strategy Debate: From the 1990s to Today In parallel with the development of the derivatives market in the early 1990s, many traditional commercial banks were faced with a fundamental strategic issue: After watching the corporate loan market become commoditized due to intense competition and disintermediation in the capital markets, many commercial banks seriously considered changing their strategic focus. Some leaders such as Bankers Trust and JP Morgan underwent a fundamental transformation from a commercial bank to trading institutions. As with any transformation of this magnitude, the process was difficult and required strong commitment by senior management. Raroc models, used to make the risk-adjusted return from different businesses or loans directly comparable, were developed by these leading institutions in an effort to sharpen their strategies and build the commitment of senior management. In retrospect, the development of Raroc and its strategic impact have been self-reinforcing, leading the industry to look differently at the economics of credit businesses and the measurement of value creation within a financial services company.

As I developed my quantitative skills, I discovered more opportunities to apply them and came to know many others in the field who shared my appreciation of quantitative methods. 251 JWPR007-Lindsey 252 May 7, 2007 17:15 h ow i b e cam e a quant A Brief Chronology Upon graduation from college with a bachelor of science degree in economics and little exposure to quantitative methods aside from some introductory calculus and statistics, I accepted a position in the pension department at Equitable Life Assurance Society, then the third largest insurance company in the United States. My initial responsibilities involved performance calculations for pension funds—at this time, the state of the art in performance measurement was time-weighted rates of return. The notion of risk-adjusted returns had not yet gained traction at Equitable. Reasonably soon after my arrival at Equitable, I managed to land a job in the Investment Advisory Department, which managed the asset allocation of Equitable’s pension fund clients. This group determined how to allocate the funds across the various separate accounts that were invested in money market instruments, publicly traded bonds, direct placement bonds, large stocks, small stocks, and real estate.

Miller and Evan Schulman, “Money Illusion Revisited: Linking Inflation to Asset Return Correlations,” Journal of Portfolio Management (Spring 1999). 14. Ray A. LeClair and Evan Schulman, “Revenue Recognition Certificates: A New Security,” Financial Analysts Journal (July–August 2006). 15. F. Black, Exploring General Equilibrium (Cambridge, MA: MIT Press, 1995), p. 35. Chapter 6 1. Raroc is an acronym for risk-adjusted return on capital, a phrase that covers many different models for evaluating ex ante and ex post performance recognizing the risks undertaken. VaR is an acronym for value at risk, often defined as the maximum possible loss within a given confidence interval. For more information, see Wilson (98). JWPR007-Lindsey May 18, 2007 346 11:41 note s Chapter 7 1. The trick was so-called differentiating under the integral sign, here is the quote from Surely You Must be Joking: “. . . when guys at MIT or Princeton had trouble doing a certain integral, it was because they couldn’t do it with standard methods they had learned at school . . .


The Handbook of Personal Wealth Management by Reuvid, Jonathan.

asset allocation, banking crisis, BRICs, business cycle, buy and hold, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, market bubble, merger arbitrage, negative equity, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

A correlation of close to 0 is a sign that the two investments are actually acting independently of one another. This de-correlation should not be confused with a negative correlation; a correlation of up to –1 may arise when price movements behave almost in opposition. With this brief and fairly simplistic explanation, it is important to note that portfolios are generally directed at producing superior risk-adjusted returns when assets are de-correlated. It should therefore be the expectation of investors that their wealth managers put a high level of importance on such a characteristic.1 The utopian vision of portfolio blending does have its blips though. This decorrelated blend tends to hold in the longer term but not necessarily at shorter-term ឣ 18 PORTFOLIO INVESTMENT _________________________________________________ 1 Year Rolling Correlation Fixed Income to Equities 8 08 l-0 Ju 7 07 l-0 nJa Ju 6 06 l-0 nJa Ju 5 05 l-0 nJa Ju 4 04 l-0 nJa Ju 3 03 l-0 nJa Ju 2 02 l-0 nJa Ju 1 l-0 nJa Ju n- l-0 0 01 Average Correlation: 0.06 Ja Ja n- 00 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 -0.10 -0.20 -0.30 -0.40 -0.50 -0.60 -0.70 -0.80 -0.90 -1.00 Ju Correlation intervals.

Frequent review re-emphasizes the criticality of ongoing communication and information exchange, ensures that the portfolio does not steer too far off course, and checks that the investor’s goals for the portfolio have not changed. The second is the ability of the adviser to integrate portfolio solutions that can stabilize risk and return dynamics of the portfolio. ____________________________________________ PORTFOLIO RETURN BEHAVIOUR 19 ឣ Note that reviewing portfolio performance is not a substitute for the investment adviser who identifies solutions that will improve portfolio risk-adjusted returns. Despite the dynamism and inherent difficulties with identifying de-correlated strategies, a portfolio adviser should constantly be on the look out for new investment types or asset classes that may serve to improve the risk and return dynamic of the portfolio. Put simply, investors should want to ensure that they and their advisers have considered all options for improving their portfolios’ risk and return – both in the short term and over the length of the investment time horizon.

Perhaps this may create significant opportunities for some multi-strategy operations going forward. 2008 hedge fund review Hedge funds are often thought to be absolute return vehicles; however, the underlying assets in which they trade are often the same equities, bonds and commodities found in many long-only manager portfolios. Of course, the structure of the trades are often far removed from the more traditional fundamental buy-and-hold techniques found in their long-only peers’ portfolios. Given this characteristic, perhaps hedge funds should be viewed as ‘better risk adjusted returns’ rather than absolute returns; of course their stated mandate implicitly remains the production of positive returns in all market conditions. The year 2008 will go down in history as being one of the most important in the evolution of the hedge fund industry. The financial crisis has proved to be worse than many had feared and the financial and economic imbalances that have taken years to build up may take years to unwind.


pages: 654 words: 120,154

The Firm by Duff McDonald

"Robert Solow", Asian financial crisis, borderless world, collective bargaining, commoditize, conceptual framework, corporate governance, creative destruction, credit crunch, family office, financial independence, Frederick Winslow Taylor, income inequality, invisible hand, Jeff Bezos, Joseph Schumpeter, Kickstarter, laissez-faire capitalism, Mahatma Gandhi, Nelson Mandela, new economy, pets.com, Ponzi scheme, Ralph Nader, risk tolerance, risk-adjusted returns, shareholder value, Silicon Valley, Steve Jobs, supply-chain management, The Nature of the Firm, young professional

No wonder that when something like the credit crunch comes along, huge numbers of highly skilled people in compartmentalized worlds are unable to respond to it.”30 What’s more, McKinsey and others wholly endorsed bankers’ move farther out on the risk curve in search of higher returns. Specifically, they were pushing the concept of risk-adjusted return on capital, or RAROC, as well as a notion called “shareholder value added,” or SVA. Both ideas were based on a simple premise. “In theory, if a bank took capital out of a business with low-risk adjusted returns and put it into a business with high-risk adjusted returns, its overall return on shareholder funds should be higher. So would its position in the banking food chain,” explained Kevin Mellyn in Financial Market Meltdown. “It seemed like a good idea at the time.”31 It wasn’t. McKinsey wasn’t alone in pushing RAROC, of course.

See also deregulation Reilly, Ewing “Zip,” 38, 130 Renault, 79 Republic Steel, 28 retirement plan, McKinsey, 119, 199, 291 Reuter, Edzard, 157 revenue/fees, McKinsey: for Bank One, 185–86 banking industry and, 204, 286, 292, 293 Bower-Kearney split and, 56 Bower tenure and, 95–96 Bower views about, 73 for China clients, 283 client dissatisfaction with, 186 and client-McKinsey long-term relationships, 185 and client reactions to Gupta and Kumar cases, 322 and compensation of employees, 87 competition and, 137, 190–91, 203–4 contingency, 266 culture/values and, 266 and decline in billing rate, 236 decline in, 267–70 dot-com bubble and, 267–68 and effect of McKinsey on clients, 173, 174 Enron and, 239 European expansion and, 75, 77 and feigned ignorance of financials, 296 and Fortune-Huey story about McKinsey, 206 FSI and, 234 and government as clients, 72–73 In Search of Excellence and, 153 from IT, 200 justification for premium, 204 and Kumar-Rajaratnam investigation, 310 and McKinsey arrogance, 206 and McKinsey as brand, 93 and McKinsey in the future, 329 in 1930s and 1940s, 25, 37, 52 in 1960s and 1970s, 95–96, 102, 108, 126, 134, 138, 164 in 1980s, 125, 164, 203, 222 in 1990s, 164, 197, 203, 222, 235, 236 premium, 190–91 from public sector clients, 283 as secondary to serving clients, 44 sources of, 329 and strategy work, 142 for Tanzania clients, 79 between 2001 and 2010, 137, 235–36, 266, 267–70, 294, 296–97, 322 value billing and, 57. See also specific managing director or client Rhône-Poulenc, 79 risk-adjusted return on capital (RAROC), 289 RiskMetrics, 311 RJR Nabisco, 41, 81 Robert Heller & Associates, 55 Rockefeller, David, 19, 91 Rockefeller, Nelson, 68 Roddick, Harrison, 47, 50, 56 Roeder, Ulrich, 228 Rolls-Royce, 78 Romney, Mitt, 1, 111, 232, 284 Rosenthal, Jim, 145 Rotten Corps, 84 Royal Dutch Shell, 74–75, 76 Rude Awakening: The Rise, Fall and Struggle for Recovery of General Motors (Keller), 183 “The Rules of Three and Four” (Henderson), 115–16 Russia: McKinsey office in, 160, 228 Saatchi & Saatchi, 200 Safran, 323 Salinas, Carlos, 176 Salomon Brothers, 111 Samsung, 309 San Francisco, California: McKinsey office in, 42, 52, 64, 127, 147, 265 Sandberg, Sheryl, 327 Sandoz, 79 Sanson, Norman, 176, 223 Santa Fe Institute, 217 Sanwa Bank, 115 Sapient, 265 Sara Lee, 134, 224 Sarbanes-Oxley Act, 252 Savoy Plaza Hotel (New York City): as Bower client, 34–35 Sawhill, John, 156, 239–40 Saxena, Parag, 312, 313 Say It with Charts (Zelazny), 123, 156 SBC Communication, 179 SBC Warburg, 227, 232 Scale and Scope (Chandler), 16, 78 Scandinavian Airlines Systems (SAS), 255 Schiefer, Friedrich, 158 Schumpeter, Joseph, 247 Schwab, Klaus, 324 Schwartz, Mark, 312, 313 Science magazine, 78 Scient, 265 Scott, H.


Concentrated Investing by Allen C. Benello

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

Table 4.5â•… Performance Statistics for Concentrated Combination Value Portfolios, Rolling Annual Rebalance (1963 to 2015) Number of Stocks 300 200 100 50 40 30 25 20 15 10 5 1 Return (%) 17.2 17.9 18.6 19.5 19.8 20.1 20.4 20.4 20.7 20.6 21.6 22.8 Sharpe ratio 0.73 0.78 0.81 0.84 0.84 0.84 0.85 0.84 0.83 0.80 0.78 0.63 104 Concentrated Investing Figure 4.3â•… Concentrated Combination Value Portfolios, Rolling Annual Rebalance (1963 to 2015) Source: O’Shaughnessy Asset Management, LLC. O’Shaughnessy found the one-stock portfolios generated the best raw returns at 22.8 percent per year compound. As the number of portfolio holdings swelled from one, the raw returns fell off in rank order. The 25-stock portfolios generated the best Sharpe ratio—a measure of risk-adjusted returns—at 0.85. As the portfolio holdings increased beyond 25 positions, the risk-adjusted returns diminished in rank order. O’Shaughnessy’s research shows that portfolios perform better as they become more concentrated on the most undervalued stocks. The findings bear out Buffett’s advocacy for concentrated value portfolios. Buffett earlier argued that if he “were running $50, $100, $200 million, [he] would have 80 percent in five positions, with 25 percent for the largest.”20 These analyses assume that we equally weight the positions in each portfolio.

Graham’s recommendations approximately coincide with the academic research, which holds that the optimal number of positions in a portfolio is somewhere between 10 and 30. Klarman, Buffett, and Munger recommend fewer positions—5 for Buffett and Munger, 10 to 15 for Klarman—all of which broadly agree with the research that best returns for value investors can be had at very concentrated portfolios, along with O’Shaughnessy’s finding that 25 positions offered the best risk-adjusted return. In the following chapters, we examine the philosophies and returns of several concentrated value investors. Whether they explicitly calculate positions using the Kelly Formula, or simply concentrate into positions intuitively without making an explicit calculation, they all have exceptional long-term track records. First, we examine the track record and philosophy of Buffett’s business partner and friend Charlie Munger, vice chairman of Berkshire Hathaway.


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, p-value, paper trading, performance metric, prediction markets, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

This phase will provide the statistics that you or your company consider important, such as the following: Profit and loss (P and L): The money made by the strategy without transaction fees. Net profit and loss (net P and L): The money made by the strategy with transaction fees. Exposure: The capital invested. Number of trades: The number of trades placed during a trading session. Annualized return: This is the return for a year of trading. Sharpe ratio: The risk-adjusted return. This date is important because it compares the return of the strategy with a risk-free strategy. While this part will be described in detail later, for this section, we will be interested in testing our strategy with an initial capital over a given period of time. For the purpose of backtesting, we will have a portfolio (grouping of financial assets such as bonds and stocks) composed of only one type of stock: Google (GOOG).

Volatility adjusted mean reversion trading strategies We explored mean reversion trading strategies in great detail in Chapter 4, Classical Trading Strategies Driven by Human Intuition. For the purposes of this chapter, we will first create a very simple variant of a mean reversion strategy and then show how one would apply volatility adjustment to the strategy to optimize and stabilize its risk-adjusted returns. Mean reversion strategy using the absolute price oscillator trading signal Let's explain and implement a mean reversion strategy that relies on the Absolute Price Oscillator (APO) trading signal indicator we explored in Chapter 2, Deciphering the Markets with Technical Analysis. It will use a static constant of 10 days for the Fast EMA and a static constant of 40 days for the Slow EMA.

We will use a week as the time horizon for our trading strategy: last_week = 0 weekly_pnls = [] weekly_losses = [] for i in range(0, num_days): if i - last_week >= 5: pnl_change = pnl[i] - pnl[last_week] weekly_pnls.append(pnl_change) if pnl_change < 0: weekly_losses.append(pnl_change) last_week = i from statistics import stdev, mean sharpe_ratio = mean(weekly_pnls) / stdev(weekly_pnls) sortino_ratio = mean(weekly_pnls) / stdev(weekly_losses) print('Sharpe ratio:', sharpe_ratio) print('Sortino ratio:', sortino_ratio) The preceding code will return the following output: Sharpe ratio: 0.09494748065583607 Sortino ratio: 0.11925614548156238 Here, we can see that the Sharpe ratio and the Sortino ratio are close to each other, which is what we expect since both are risk-adjusted return metrics. The Sortino ratio is slightly higher than the Sharpe ratio, which also makes sense since, by definition, the Sortino ratio does not consider large increases in PnLs as being contributions to the drawdown/risk for the trading strategy, indicating that the Sharpe ratio was, in fact, penalizing some large +ve jumps in PnLs. Maximum executions per period This risk measure is an interval-based risk check.


pages: 584 words: 187,436

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

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

In a rough-and-ready way, his techniques anticipated the breakthroughs in financial academia of the 1950s and 1960s. IN 1952, THREE YEARS AFTER JONES HAD LAUNCHED HIS fund, modern portfolio theory was born with the publication of a short paper titled “Portfolio Selection.” The author was a twenty-five-year-old graduate student named Harry Markowitz, and his chief insights were twofold: The art of investment is not merely to maximize return but to maximize risk-adjusted return, and the amount of risk that an investor takes depends not just on the stocks he owns but on the correlations among them. Jones’s investment method crudely anticipated these points. By paying attention to the velocity of his stocks, Jones was effectively controlling risk, just as Markowitz advocated. Moreover, by balancing the volatility of his long and short positions, Jones was anticipating Markowitz’s insight that the risk of a portfolio depends on the relationship among its components.36 Jones’s approach was more practical than that of Markowitz.

They could work wonders for a large portfolio. And so, in the aftermath of the bond-market crisis of 1994, there were two verdicts on hedge funds. Regulators were forced to confront worrisome questions about the industry; but lacking a good theory of how to tame it, they ultimately chose to look the other way. Meanwhile, institutional investors reached a critical verdict: Notwithstanding the turmoil of 1994, hedge funds promised risk-adjusted returns that were simply irresistible. In a sense, the two verdicts were one. Because markets are not perfectly efficient, hedge funds and other creatures of the markets raise difficult issues: They are part of an unstable game that can wreak havoc on the world economy. But by the same token, the inefficiency of the markets allowed hedge funds to do well. Investors would line up to get into them. 9 SOROS VERSUS SOROS To create the mining city of Noril’sk, Stalin resorted to slavery.

But even if funds make every effort to report their results honestly, they cannot help but “smooth” them. A hedge fund may estimate the value of an illiquid asset every few weeks; if it rises 5 percent and then falls back within that period, it will be recorded simply as flat—with the result that some sharp volatility along the way is not acknowledged. As a result, hedge funds with illiquid assets are not as stable as their numbers suggest. Their risk-adjusted returns look wonderful because some of the risk goes unreported. But the biggest danger for buyers of illiquid assets is that, in a crisis, these assets will collapse the hardest. In moments of panic, investors crave securities that can be easily sold, and the rest are shunned ruthlessly. Long-Term Capital’s apparently diverse portfolio concealed a single bet that the world would be stable: When this proved wrong, apparently unrelated positions collapsed simultaneously because many of them boiled down to an attempt to harvest a premium for holding illiquid assets.


pages: 436 words: 98,538

The Upside of Inequality by Edward Conard

affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, assortative mating, bank run, Berlin Wall, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Climatic Research Unit, cloud computing, corporate governance, creative destruction, Credit Default Swap, crony capitalism, disruptive innovation, diversified portfolio, Donald Trump, en.wikipedia.org, Erik Brynjolfsson, Fall of the Berlin Wall, full employment, future of work, Gini coefficient, illegal immigration, immigration reform, income inequality, informal economy, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), invention of the telephone, invisible hand, Isaac Newton, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, Kodak vs Instagram, labor-force participation, liquidity trap, longitudinal study, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, mass immigration, means of production, meta analysis, meta-analysis, new economy, offshore financial centre, paradox of thrift, Paul Samuelson, pushing on a string, quantitative easing, randomized controlled trial, risk-adjusted returns, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, selection bias, Silicon Valley, Simon Kuznets, Snapchat, Steve Jobs, survivorship bias, The Rise and Fall of American Growth, total factor productivity, twin studies, Tyler Cowen: Great Stagnation, University of East Anglia, upwardly mobile, War on Poverty, winner-take-all economy, women in the workforce, working poor, working-age population, zero-sum game

Successful innovators need not share their success with investors. Successful individuals like Google’s Larry Page and Facebook’s Mark Zuckerberg look like corporations of a bygone capital-intensive era. Without much need for capital, start-ups become all-or-nothing lotteries. The chance for enormous payoffs attracts a larger number of more talented gamblers. More gamblers produce more outsized winners, and more innovation, too—whether the risk-adjusted returns are good, on average, or not. Their success has compounding benefits. It provides American workers with more valuable on-the-job training, at companies like Google and Facebook, than they can get in other high-wage, slower-growing manufacturing-based economies. It creates synergistic communities of experts, like Silicon Valley. And it puts equity into the hands of successful risk-takers who use their equity and expertise to underwrite further risk-taking that produces more innovation, faster growth, and compounding benefits.

As the U.S. economy devotes more resources to these lottery-like investments, income inequality will grow at the highest end of the wage scale. Increased Risk-Taking Increases Inequality Even If the Returns Are Subpar Even though a handful of fortunate innovators are making outsized returns, it does not mean that on average innovation’s profitability has increased and that entrepreneurial risk-takers, investors, and properly trained talent are merely benefiting from outsized risk-adjusted returns. Nor is it necessary for average returns to increase for inequality to rise. As more resources are devoted to finding and commercializing innovation, overall return on investment is likely to decline.33 Even if returns are declining in general, the shift toward innovation’s more widely distributed lottery-like returns—and away from traditional investments—can increase outsized success. Scrutinizing only the successful 1 percent (or 0.1 percent, or 0.01 percent) ignores the true cost of success, namely the cost of failure.

But without the benefits of exogenous growth, given the near certainty of widespread failure, and with competition from the growing amount of investments in intangibles like research and development, it would not be surprising to find below-average returns even though outsized success is rising. Income inequality may nevertheless rise as the dispersion of returns widens even though the increased risk necessary to produce a handful of outsized successes and the high failure rates needed to produce those returns may not represent the walk in the park they appear to be. Loss of Status Drives Irrational Risk-Taking As poor as the risk-adjusted returns on start-ups may be for investors who can diversify their risk by investing in many start-ups, they are surely much worse for individual entrepreneurs. Unlike investors who enjoy average returns by investing in many projects, founders and their teams risk everything on a single start-up. As such, they bear undiversified project-specific risks that investors avoid through diversification.


pages: 264 words: 115,489

Take the Money and Run: Sovereign Wealth Funds and the Demise of American Prosperity by Eric C. Anderson

asset allocation, banking crisis, Bretton Woods, business continuity plan, business process, buy and hold, collective bargaining, corporate governance, credit crunch, currency manipulation / currency intervention, currency peg, diversified portfolio, fixed income, floating exchange rates, housing crisis, index fund, Kenneth Rogoff, open economy, passive investing, profit maximization, profit motive, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, sovereign wealth fund, the market place, The Wealth of Nations by Adam Smith, too big to fail, Vanguard fund

As with domestic equity markets, Yale seeks to make the most of the situation by hiring managers with “bottom-up fundamental research capabilities” but also by allocating funds to these managers based on confidence in the individual and “the appropriate size for a particular strategy.”32 This is tailored investing at its best. Yale’s “private equity” asset class is marketed as “extremely attractive longterm risk-adjusted return.” This asset class is said to include participation with venture capitalists and “leveraged buy-out partnerships.” Given this build-up, it’s not surprising to find that the Endowment believes this asset class can be expected to generate real returns of 11.2%, but with a high risk of 27.7%. Nonetheless, Yale is clearly proud of its track record in private equity, reporting Investing Like a Sovereign Wealth Fund 73 that since inception this area of the portfolio has provided a 31.4% annualized return.33 The last asset class, “real assets,” is described as providing “attractive return prospects, excellent portfolio diversification, and a hedge against unanticipated inflation.”

On 12 March 2008, the government of Abu Dhabi—operator of the world’s largest sovereign wealth fund—sent a letter to Western financial officials spelling out the United Arab Emirates’ investment guidelines. According to the letter, “the Abu Dhabi Government has never and will never use its investments as a foreign policy tool.” Rather, Abu Dhabi’s “investment organizations have always sought solely to maximize risk-adjusted returns.”11 As evidence supporting this claim, Abu Dhabi declared 80% of the Abu Dhabi Investment Authority’s (ADIA) funds are managed by “outside firms” who provide access to “international financial expertise.” In addition to these broad statements clearly intended to assuage Western skeptics, the letter outlined nine principles said to guide Abu Dhabi’s capital investment organizations. As listed in the letter, these principles are: • To operate for the public good, generating long-term, attractive returns for the prosperity of the people of Abu Dhabi • To operate as strictly individual entities, making independent, commercially driven investment decisions • To follow meticulously all of the laws, regulations, and rules of the countries and exchanges in which investments are made • To meet all disclosure requirements of relevant government and regulatory bodies in countries in which they invest • To maximize risk-adjusted returns, relative to well-established market indices • To recruit and retain world-class financial professionals either as in-house or external managers • To invest with a long-term perspective • To invest in a well-diversified portfolio across asset classes, geographies, and sectors • To maintain appropriate standards of governance and accountability12 The letter closed by noting Abu Dhabi realized its investments “are good for the global economy—providing increased liquidity, injecting capital for growth, expanding market access, creating jobs, and encouraging a common interest and commitment to mutual prosperity.”13 Quite clearly, the authors had been reading through U.S.

As listed in the letter, these principles are: • To operate for the public good, generating long-term, attractive returns for the prosperity of the people of Abu Dhabi • To operate as strictly individual entities, making independent, commercially driven investment decisions • To follow meticulously all of the laws, regulations, and rules of the countries and exchanges in which investments are made • To meet all disclosure requirements of relevant government and regulatory bodies in countries in which they invest • To maximize risk-adjusted returns, relative to well-established market indices • To recruit and retain world-class financial professionals either as in-house or external managers • To invest with a long-term perspective • To invest in a well-diversified portfolio across asset classes, geographies, and sectors • To maintain appropriate standards of governance and accountability12 The letter closed by noting Abu Dhabi realized its investments “are good for the global economy—providing increased liquidity, injecting capital for growth, expanding market access, creating jobs, and encouraging a common interest and commitment to mutual prosperity.”13 Quite clearly, the authors had been reading through U.S.


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

asset allocation, backtesting, buy and hold, 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, stocks for the long run, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Yogi Berra, zero-coupon bond

It takes years to become comfortable with this strategy; many lose their nerve and never see the thing through. You cannot pilot a modern jet fighter before mastering the trainer; likewise, you should not attempt dynamic asset allocation before mastering fixed asset allocation. In the 1995 version of this book, I provided an example of how changing the stock and bond allocation in the opposite direction of P/B produced a slight improvement in risk-adjusted return. Alas, this is no longer true, as a P/B sensitive investor would have completely exited the stock market by last year. However, for what it’s worth, Figure 7-10 is a graph of P/B versus five-year forward average return. Although there is some scatter, there is obviously a strong tendency for returns to be high with low starting P/Bs, and low with high P/Bs. The most remarkable aspect of this plot is that the lower boundary of the data points forms quite a straight line; this represents the minimum return which can be expected for a given P/B.

Cyclical stock: A security that is particularly sensitive to economic conditions, such as an aircraft or paper company (as opposed to a food or drug manufacturer, whose profits and sales are not sensitive to economic conditions). Discounted dividend model (DDM): A method of estimating the intrinsic value of a company or market by calculating the discounted value of its expected future dividends. The amount by which future 190 The Intelligent Asset Allocator dividends are reduced is called the discount rate ; it typically approximates the risk-adjusted return of the asset. Diversification: Allocating assets among investments with different risks, returns, and correlations in order to minimize nonsystematic risk. Efficient frontier: All of the possible portfolio combinations which maximize return for every possible level of expected risk or which minimize expected risk for every possible level of expected return. The mathematical technique for calculating these portfolios, called mean-variance analysis, was invented by Harry Markowitz.


pages: 322 words: 77,341

I.O.U.: Why Everyone Owes Everyone and No One Can Pay by John Lanchester

asset-backed security, bank run, banking crisis, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black-Scholes formula, Blythe Masters, Celtic Tiger, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, diversified portfolio, double entry bookkeeping, Exxon Valdez, Fall of the Berlin Wall, financial deregulation, financial innovation, fixed income, George Akerlof, greed is good, hedonic treadmill, hindsight bias, housing crisis, Hyman Minsky, intangible asset, interest rate swap, invisible hand, Jane Jacobs, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Kickstarter, laissez-faire capitalism, light touch regulation, liquidity trap, Long Term Capital Management, loss aversion, Martin Wolf, money market fund, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, negative equity, new economy, Nick Leeson, Norman Mailer, Northern Rock, Own Your Own Home, Ponzi scheme, quantitative easing, reserve currency, Right to Buy, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, South Sea Bubble, statistical model, The Great Moderation, the payments system, too big to fail, tulip mania, value at risk

In his view, “successful people understand that risk, properly conceived, is often highly productive rather than something to avoid. They appreciate that risk is an advantage to be used rather than a pitfall to be skirted. Such people understand that taking calculated risks is quite different from being rash.”6 He put this into practice by encouraging Bankers Trust to develop a precisely quantified measure of risk, a system which became known as risk-adjusted return on capital, or RAROC. RAROC offered a numerical analysis of risk and added to it a measure of the impact of that risk on a business’s profitability; just as portfolio management provided a way of assessing and optimizing the risk of a set of share holdings, RAROC did the same for a company’s or bank’s range of businesses. In time, however, the industry came to prefer a newer model of risk, called value at risk, or VAR.

., 181, 184–92, 195, 199–200, 223–24, 227 Reykjavík, 10, 12, 170 risk, risks, 49–58, 66–76, 133–36, 141–67, 211–12, 219 AIG and, 75–76 assessment of, 46, 55–56, 74, 133, 135–36, 141–43, 145–67, 187–88, 191, 202, 205, 212, 216, 226 banks and, 19, 34–37, 41, 133, 135–36, 143, 150–54, 156–57, 160, 165–66, 174, 187–88, 191–95, 202, 204–7, 216, 224, 226, 228, 230 bonds and, 61–63, 103, 118, 144, 154, 208 derivatives and, 46–47, 49–52, 54–55, 57–58, 66–75, 78–80, 114–15, 117–22, 151, 153, 158–60, 163, 166–67, 184–85, 205, 212 desirability of, 144, 146, 150, 206–7 diversification and, 146–48 Greenspan and, 142–43, 164–66, 174, 184 hedging of, 49–50, 52, 58, 115, 205 historical data and, 163, 166 housing and, 88, 94–97, 112–13, 125, 129, 145, 158–60, 163–65 investing and, 5, 68, 70, 88, 103, 144, 146–53, 158, 165, 184, 190 leverage and, 35–36 LTCM and, 55–56 overconcentration of, 72–73 regulation and, 143, 153, 164, 187–88, 191, 195, 202, 204–5, 212, 224, 226 securitization and, 69–70, 163, 165 of stairs, 134–35 VAR and, 151–57, 162–63 risk-adjusted return on capital (RAROC), 150–51 Ritholtz, Barry, 219–20 Robinson, Phillip, 128–31 Rogers, Jim, 221 Royal Bank of Scotland (RBS), 34–36, 120, 227 bailout of, 32, 40, 204 Russia, 3, 15–16, 18, 53 bond default of, 55–56, 162, 164–65 Salomon Brothers, 63 Sanford, Charles, 150 Santander, 40 savings, 28, 86, 107, 177, 179, 187 savings and loan crisis, 73, 185, 220 Scholes, Myron, 45, 47–48, 54–55, 147 Securities and Exchange Commission (SEC), 195 credit ratings and, 209–10 regulation and, 153, 186, 189–92 securitization, 20, 22, 200 derivatives and, 69–70, 74, 113–14, 117–19, 122, 212 risk and, 69–70, 125, 163, 165, 212, 224 selling, sales, 34, 42, 104, 174, 203 of bonds, 59, 61–63, 144 derivatives and, 46–50, 52, 56, 65, 67–68, 73–74, 120 of equity, 58–59 of houses, 28–29, 71, 89–90 risk and, 151–52, 165, 224 Shiller, Robert, 106, 145n, 194 Simon, David, 83–84 Singapore exchange, 54 Skilling, Jeffrey, 106 small numbers, law of, 137 Sociét Générale, 51, 77 solvency, insolvency, 28–29 of banks, 36–38, 40–43, 64, 74–75, 120 Spain, 15, 40, 177, 214 contracting economy of, 222–23 housing in, 92, 110 special purpose vehicles (SPVs), 70, 120 stairs, deaths caused by, 134–35 Standard & Poor’s (S&P), 62, 114, 151, 209 statistics, 160–62 Stefánsdóttir, Rakel, 9–10, 12 stock market, stocks, 22, 54–55, 61, 76, 80, 101–11, 115, 226 bubbles and implosions in, 3, 42, 103–9, 142, 175–76 derivatives and, 50–52, 54 investing in, 59, 73, 101–7, 111, 146–52, 158, 175, 192 new-economy, 103 1929 crash of, 152, 199, 213 October 1987 crash of, 142, 151–52, 161–62, 164–65 prices of, 102, 105–6, 109–10, 147–51, 158, 174 structured investment vehicles (SIVs), 120 Summa de Arithmetica (Pacioli), 26 Summers, Lawrence, 43, 74, 188 Taleb, Nassim, 53, 155–56 Tax Reform Act of 1986 (TRA), 100 technology, 42, 104, 149, 155, 166 terrorism, 2, 12, 18, 107 Tett, Gillian, 121, 193 Thatcher, Margaret, 199, 217, 222 free-market capitalism and, 14, 21, 24 on housing, 87, 91, 98 regulation and, 21, 195–96 torture, end of ban on, 18 tranching, 117–18, 122 Treasury, British, 181–82 Treasury, U.S., 43, 54, 64, 74, 76–78 AIG bailout and, 76, 78 regulation and, 188–90 Treasury bills (T-bills), 29–30, 62, 103, 118, 144, 208 China’s investment in, 109, 176–77 Trichet, Jean-Claude, 92 Trillion Dollar Meltdown, The (Morris), 42 Troubled Assets Relief Program (TARP), 37, 189 Turner, Adair, 181 Tversky, Amos, 136–38, 141 UBS, 36, 120 uncertainty, 96 fair value theory and, 147–48 risk and, 55–56, 153, 163 United Kingdom, 9, 11–12, 18, 28–29, 61, 122–24, 134, 139, 194–202, 216–18 banking in, 5, 11, 32–36, 38–40, 51–54, 76–77, 89, 94, 120, 146, 180, 194–96, 199, 202, 204–6, 211–12, 217, 227–28 bill of, 220–22, 224 and City of London, 21–22, 32, 195–97, 200, 217–18 credit ratings and, 123–24, 209 derivatives and, 72, 200–201 financial vs. industrial interests in, 196–99 free-market capitalism in, 14–15, 21, 230 GDP of, 32, 214, 220 Goodwin’s pension and, 76–77 housing in, 38, 87–98, 110, 122, 177–78 interest rates in, 102, 177–80 personal debt in, 221–22 prosperity of, 214, 216 regulation in, 21–22, 105n, 180–82, 194–96, 199–201, 218 United Nations, 4 United States, 17–22, 34, 62–71, 120–31, 134n, 165, 199–201 AIG bailout and, 76–78 banks of, 36–37, 39–40, 43, 63–71, 73, 75, 77–78, 84, 116, 120–21, 127, 150, 152, 163, 183, 185, 190, 195, 204, 211–12, 219–20, 225, 227–28 bill of, 219–20 China’s investment in, 109, 176–77 credit and, 109, 123–24, 195, 208–9, 211 free-market capitalism in, 14–15, 230 housing in, 37, 82–86, 95, 97–101, 109–10, 114–15, 122, 125–31, 157–58, 163 interest rates in, 102, 107–8, 173–77 regulation in, 181, 184–92, 195, 199–200, 223–24, 227 urban desolation in, 81–86 value, values, 42, 74–75, 78–80, 103–4, 179, 181, 217–18, 220, 227 bonds and, 61, 103 derivatives and, 38, 48–49, 185, 201 housing and, 28–29, 71, 90, 92–95, 111, 176 investing and, 60–61, 104, 198 LTCM and, 55–56 notional, 38, 48–49, 80 value at risk (VAR), 151–57, 162–63 Vietnam War, 18, 220 Viniar, David, 163 volatility, 20, 158 risk and, 47–48, 148–50, 161 Volcker, Paul, 20 Waldrow, Mary, 127 Wall Street, 22, 53, 64, 129, 188 Washington Post, The, 18 wealth, 4, 10, 19–21, 64, 204, 206 financial industry’s ascent and, 20–21 in free-market capitalism, 15, 19, 230 housing and, 87, 90, 121 Keynes’s predictions on, 214–15 in West, 218–19 Weatherstone, Dennis, 152 Wells Fargo, 84, 127 Wessex Water, 105n West, 14–18, 43, 213, 231 conflict between Communist bloc and, 16–18 free-market capitalism in, 14–15, 17, 21, 23 wealth in, 218–19 wheat, 49n, 52 When Genius Failed (Lowenstein), 161 Williams, John Burr, 147 Wilson, Lashawn, 130–31 Wire, The, 83–84 World Bank, 58, 65, 69 * GDP, which will be mentioned quite a few times in this story, sounds complicated but isn’t: it’s nothing more than the value of all the goods and services produced in an economy.


pages: 280 words: 79,029

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

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

As we have seen in the first chapter, the advantages of diversification have long been known—to Chinese merchants thousands of years ago and to Geneva bankers in the eighteenth century. But it was first captured in formal theory in 1952, when a twenty-five-year-old graduate student at the University of Chicago named Harry Markowitz published a paper called “Portfolio Selection.” The gist of Markowitz’s theory was that the return on an investment had to be weighed against the risk of its going awry and that these “risk-­adjusted” returns could be improved by diversifying. Putting all your money into the shares of a single firm might deliver a high return, but it exposes you to disaster if that firm goes broke. Better to spread your money across different bets, be they geographies, industries, or asset classes. Securitization is another take on this idea: by pulling a lot of different loans into a single investable security, the income stream it produces should become more stable.

., 32 Keys, Benjamin, 48 Kharroubi, Enisse, 79 Kickstarter, 172 King, Stephen, 99 Klein, David, 182 Krugman, Paul, xv Lahoud, Sal, 166 Lang, Luke, 153, 161–162 Laplanche, Renaud, 179, 184, 188, 190, 193–194, 196–197 Latency, 53 Law of large numbers, 17 Layering, 57 Left-digit bias, 46 Lehman Brothers, x, 44, 65 Lending direct, 84 marketplace, 184 payday, 200 relationship-based, 11, 151, 206–208 secured, xiv, 76 unsecured, 206 See also Loans; Peer-to-peer lending Lending Club, 172, 179–180, 182–184, 187, 189, 194–195, 197 Leonardo of Pisa (Fibonacci), 19 Lerner, Josh, 59 Lethal pandemic, risk-modeling for demographic profile, 230 exceedance-probability curve, 231–232, 232 figure 3 historical data, 228–229 infectiousness and virulence, 229–230 location of outbreak, 230–231 Leverage, 51, 70–71, 80, 186, 188 Leverage ratio, 76–77 Lewis, Michael, 57 Liber Abaci or Book of Calculation (Fibonacci), 19 LIBOR (London Interbank Offered Rate), 41 Liebman, Jeffrey, 98 Life expectancy government reaction to, 128–129 projections of, 124–127, 126 figure 2 ratio of young to older people, 127–128 Life-insurance policies, 142 Life-settlements industry, 142–143 Life table, 20 Limited liability, 212 Liquidity, 12–14, 39, 185–186 List, John, 109 The Little Book of Behavioral Investing (Montier), 156 Lo, Andrew, 113–115, 117–123 Loans low-documentation, 48–49 secured, 76 small business, 181, 216 student, 164, 166–167, 169–171, 182 syndicated, 41 Victory Loans, 28 See also Lending; Peer-to-Peer lending Logistic regression, 201 London, early fire insurance in, 16–17 London, Great Fire of, 16 London Interbank Offered Rate (LIBOR), 41 Long-Term Capital Management, 123 Longevity, betting on, 143–144 Loss aversion, 136 Lotteries, 212, 213 Low-documentation loans, 48–49 Lumni, 165, 168, 175 Lustgarten, Anders, 111 Lynn, Jeff, 160–161 Mack, John, 180 Mahwah, New Jersey, 52, 53 Marginal borrowers assessment of, 216–217 behavioral finance and, 208–214 industrialization of credit, 206 microfinance and, 203 savings schemes, 209–214 small businesses, 215–219 unsecured lending to, 206 Wonga, 203, 205, 208 Marginal borrowers (continued) ZestFinance, 199, 202, 205–206 Maritime piracy, solutions to, 151–152 Maritime trade, role of in history of finance, 3, 7–8, 14, 17, 23 Market makers, 15–16, 55 MarketInvoice, 195, 207, 217–218 Marketplace lending, 184 Markowitz, Harry, 118 Massachusetts, use of inflation-protected bonds in, 26 Massachusetts, use of social-impact bonds in, 98 Matching engine, 52 Maturity transformation, 12–13, 187–188, 193 McKinsey & Company, ix, 42 Mercator Advisory Group, 203 Merrill, Charles, 28 Merrill, Douglas, 199, 201 Merrill Lynch, 28 Merton, Robert, 31, 113–114, 123–124, 129–132, 142, 145 Mian, Atif, 204 Michigan, University of, financial survey by, 134–135 Microfinance, 203 Micropayment model, 217 Microwave technology, 53 The Million Adventure, 213–214 Minsky, Hyman, 42 Minsky moment, 42 Mississippi scheme, 36 Mitchell, Justin, 166–167 Momentum Ignition, 57 Monaco, modeling risk of earthquake in, 227 Money, history of, 4–5 Money illusion, 73–74 Money laundering, 192 Money-market funds, 43, 44 Monkeys, Yale University study of loss aversion with, 136 Montier, James, 156–157 Moody, John, 24 Moody’s, 24, 235 Moore’s law, 114 Morgan Stanley, 188 Mortgage-backed securities, 49, 233 Mortgage credit by ZIP code, study of, 204 Mortgage debt, role of in 2007–2008 crisis, 69–70 Mortgage products, unsound, 36–37 Mortgage securitization, 47 Multisystemic therapy, 96 Munnell, Alicia, 129 Naked credit-default swaps, 143 Nature Biotechnology, on drug-development megafunds, 118 “Neglected Risks, Financial Innovation and Financial Fragility” (Gennaioli, Shleifer, and Vishny), 42 Network effects, 181 New York, skyscraper craze in, 74–75 New York City, prisoner-rehabilitation program in, 108 New York Stock Exchange (NYSE), 31, 52, 53, 61, 64 New York Times, Merrill Lynch ad in, 28 Noncorrelated assets, 122 Nonprofits, growth of in United States, 105–106 Northern Rock, x NYMEX, 60 NYSE Euronext, 52 NYSE (New York Stock Exchange), 31, 52, 53, 61, 64 OECD (Organization for Economic Co-operation and Development), 128, 147 Oldfield, Sean, 67–68, 80–84 OnDeck, 216–218 One Service, 94–95, 105, 112 Operating expense ratio, 188–189 Options, 15, 124 Order-to-trade ratios, 63 Oregon, interest in income-share agreements, 172, 176 Organization for Economic Co-operation and Development (OECD), 128, 147 Overtrading, 24 Packard, Norman, 60 Pandit, Vikram, 184 Park, Sun Young, 233 Partnership mortgage, 81 Pasion, 11 Pave, 166–168, 173, 175, 182 Payday lending Consumer Financial Protection Bureau, survey on, 200 information on applicants, acquisition of, 202 underwriting of, 201 PayPal, 219 Peak child, 127 Peak risk, 228 Peer-to-peer lending advantages of, 187–189 auction system, 195 big investors in, 183 borrowers, assessment of, 197 in Britain, 181 commercial mortgages, 181 CommonBond, 182, 184, 197 consumer credit, 181 diversification, 196 explained, 180 Funding Circle, 181–182, 189, 197 investors in, 195 Lending Club, 179–180, 182–184, 187, 189, 194–195, 197 network effects, 181 ordinary savers and, 184 Prosper, 181, 187, 195 RateSetter, 181, 187, 196 Relendex, 181 risk management, 195–197 securitization, 183–184, 196 Peer-to-peer lending (continued) small business loans, 181 SoFi, 184 student loans, 182 Zopa, 181, 187, 188, 195 Pensions, cost of, 125–126 Perry, Rick, 142–143 Peterborough, England, social-impact bond pilot in, 90–92, 94–95, 104–105, 112 Petri, Tom, 172 Pharmaceuticals, decline of investment in, 114–115 Piracy Reporting Centre, International Maritime Bureau, 151 Polese, Kim, 210 Poor, Henry Varnum, 24 “Portfolio Selection” (Markowitz), 118 Prediction Company, 60–61 Preferred shares, 25 Prepaid cards, 203 Present value of cash flows, 19 Prime borrowers, 197 Prince, Chuck, 50–51, 62 Principal-agent problem, 8 Prisoner rehabilitation programs, 90–91, 94–95, 98, 108, 112 Private-equity firms, 69, 85, 91, 105, 107 Projection bias, 72–73 Property banking crises and, xiv, 69 banking mistakes involving, 75–80 behavioral biases and, 72–75 dangerous characteristics of, 70–72 fresh thinking, need for, xvii, 80 investors’ systematic errors in, 74–75 perception of as safe investment, 76, 80 Prosper, 181, 187, 195 Provisioning funds, 187 Put options, 9, 82 Quants, 19, 63, 113 QuickBooks, 218 Quote stuffing, 57 Raffray, André-François, 144 Railways, affect of on finance, 23–25 Randomized control trials (RCTs), 101 Raphoen, Christoffel, 15–16 Raphoen, Jan, 15–16 RateSetter, 181, 187, 196 RCTs (randomized control trials), 101 Ready for Zero, 210–211 Rectangularization, 125, 126 figure 2 Regulation NMS, 61 Reinhart, Carmen, 35 Reinsurance, 224 Relendex, 181 Rentes viagères, 20 Repurchase “repo” transactions, 15, 185 Research-backed obligations, 119 Reserve Primary Fund, 44 Retirement, funding for anchoring effect, 137–138 annuities, 139 auto-enrollment in pension schemes, 135 auto-escalation, 135–136 conventional funding, 127–128 decumulation, 138–139 government reaction to increased longevity, 128–129 home equity, 139–140 life expectancy, projections of, 124–127, 126 figure 2 life insurance policies, cash-surrender value of, 142 personal retirement savings, 128–129, 132–133 replacement rate, 125 reverse mortgage, 140–142 savings cues, experiment with, 137 SmartNest, 129–131 Reverse mortgages, 140–142 Risk-adjusted returns, 118 Risk appetite, 116 Risk assessment, 24, 45, 77–78, 208 Risk aversion, 116, 215 Risk-based capital, 77 Risk-based pricing model, 176 Risk management, 55, 117–118, 123, 195–197 Risk Management Solutions, 222 Risk sharing, 8, 82 Risk-transfer instrument, 226 Risk weights, 77–78 Rogoff, Kenneth, 35 “The Role of Government in Education” (Friedman), 165 Roman Empire business corporation in, 7 financial crisis in, 36 forerunners of banks in, 11 maritime insurance in, 8 Rotating Savings and Credit Associations (ROSCAs), 209–210 Roulette wheel, use of in experiment on anchoring, 138 Royal Bank of Scotland, 186 Rubio, Marco, 172 Russia, mortgage market in, 67 S-curve, in diffusion of innovations, 45 Salmon, Felix, 155 Samurai bonds, 27 Satsuma Rebellion (1877), 27 Sauter, George, 58 Save to Win, 214 Savings-and-loan crisis in US (1990s), 30 Savings cues, experiment with, 137 Scared Straight social program, 101 Scholes, Myron, 31, 123–124 Science, Technology, and Industry Scoreboard of OECD, 147 Securities and Exchange Commission (SEC), 54, 56, 57, 58, 64 Securities markets, 14 Securitization, xi, 20, 37–38, 117–122, 183–184, 196, 236 Seedrs, 160–161 Sellaband, 159 Shared equity, 80–84 Shared-equity mortgage, 84 Shepard, Chris, xii–xiii Shiller, Robert, xv–xvi, 242 Shleifer, Andrei, 42, 44 Short termism, 58 SIBs.


pages: 244 words: 79,044

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

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, NetJets, new economy, Ponzi scheme, post-work, risk-adjusted returns, risk/return, shareholder value, Silicon Valley, six sigma, statistical arbitrage, Vanguard fund, zero-coupon bond

As we took the short walk down the street to the aunt’s house, I tried to explain my rediscovered revelation about the multiple-fee structure and how it probably did not make sense for Mr and Mrs Straw, or even pension funds generally, to be invested in hedge funds, but Puk was oddly casual about it. ‘Don’t you think the rest of the world knows that finance guys are not worth what they are getting paid?’ she said with a smile, before continuing, ‘And are you and Holte not a part of the problem rather than the solution?’ I gave Puk the usual song and dance about uncorrelated risk-adjusted returns at Holte Capital, but her mind was already elsewhere. PART FOUR The fast road down 20 * * * Feeling grim Burnt out ‘A stroll after the close? There’s something I want to chat with you about.’ The email from Brian was nothing unusual. We would often go down to Grosvenor Square to have a chat in the fresh air, where the team wouldn’t see us. At that point we had been running Holte Capital together for four years, with me as the portfolio manager and Brian as the CFO.

I kept hammering home the point to our investors that most larger funds that have high return profiles do so with massive market exposures even if their illiquid portfolios allow them to avoid marking down securities in a down market and thus avoid showing their market exposure. At Holte Capital I felt our market exposure was much more limited, and the low correlation to the markets only made our risk-adjusted returns look even better. But again our investors said, ‘That all sounds very good, Lars, but your talk will not fatten my bank account – returns will.’ By 2007 we felt this was our time. We had been frustrated that our low-risk profile hindered our growth to become a mega-fund, but it was not easy to increase risk so dramatically in a short period of time, as our existing investors all had to be fully informed and on board, otherwise they might have felt they had bought a very low-volatility product and feel misled at it changing into a high-volatility one.


pages: 349 words: 134,041

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

accounting loophole / creative accounting, Albert Einstein, Asian financial crisis, asset-backed security, beat the dealer, Black Swan, Black-Scholes formula, Bretton Woods, BRICs, Brownian motion, business process, buy and hold, buy low sell high, call centre, capital asset pricing model, collateralized debt obligation, commoditize, complexity theory, computerized trading, corporate governance, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, currency peg, disintermediation, diversification, diversified portfolio, Edward Thorp, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, financial innovation, fixed income, Haight Ashbury, high net worth, implied volatility, index arbitrage, index card, index fund, interest rate derivative, interest rate swap, Isaac Newton, job satisfaction, John Meriwether, locking in a profit, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, Marshall McLuhan, mass affluent, mega-rich, merger arbitrage, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mutually assured destruction, Myron Scholes, new economy, New Journalism, Nick Leeson, offshore financial centre, oil shock, Parkinson's law, placebo effect, Ponzi scheme, purchasing power parity, quantitative trading / quantitative finance, random walk, regulatory arbitrage, Right to Buy, risk-adjusted returns, risk/return, Satyajit Das, shareholder value, short selling, South Sea Bubble, statistical model, technology bubble, the medium is the message, the new new thing, time value of money, too big to fail, transaction costs, value at risk, Vanguard fund, volatility smile, yield curve, Yogi Berra, zero-coupon bond

Is it in credit risk, market risk, wherever? What return are we getting on each unit of capital? He thought this would be a sensible way to run the bank. It was, but the answers weren’t easy to get; the basic tools and information required were not readily available. The work took time, it spawned revolutionary risk management tools like RiskMetrics and CreditMetrics. BT was also creating similar tools such as RAROC (Risk Adjusted Return On Capital). DAS_C10.QXD 5/3/07 7:59 PM Page 269 9 C re d i t w h e re c re d i t i s d u e – f u n w i t h C D S a n d C D O 269 The answer, when it came back, was disturbing – the vast bulk of Morgan’s capital was tied up in credit risk. It was returning something in single figures. Given its target return was around 20%, there was a big problem. Morgan’s knew it couldn’t stop lending; the loans were the basis of its relationship with clients, the thin end of the wedge used to get more lucrative deals.

L Position title DAS_Z01.QXP 8/11/06 2:10 PM Page 314 Tr a d e r s , G u n s & M o n e y 314 Table E.1 Continued • You will be supported by a world-class risk management team (readily identifiable by their guide dogs) and operational staff and systems (specially chosen for their total ignorance). • There are excellent career prospects for advancement in this progressive organization. (We have sinecures for everybody who has failed to perform.) • Trading with the bank’s capital to achieve targeted risk adjusted returns on capital under the bank’s unique Economic Capital Allocation system. (If you are half as smart as you think you are then you will be able to game the system from day one. Everybody else has.) • Developing innovative trading strategies. (You need to be able to come up with hare-brained trading schemes based on the relationship between the El Nino cycle and market prices.) • Closely managing trading positions.

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: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

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

Ridgeline was closed a large part of the time to new investors, and current partners were often restricted from adding capital. To maintain higher returns, we sometimes even reduced our size by returning capital to partners. Unlike some hedge fund managers who also had a waiting list, we could have increased our fees by raising our share of the profits or adding more capital, thereby driving down the return to limited partners. Such tactics by the general partner to capture nearly all the excess risk-adjusted return, or “alpha,” rather than share it with the other investors are what economic theory predicts. Instead, I preferred to treat limited partners as I would wish to be treated in their place. In August 1998, the hedge fund Long-Term Capital Management (LTCM), a pool of $4 billion, lost nearly all its money. Highly leveraged, it threatened to default on something like $100 billion in contracts.

Unfortunately he was in UCI’s School of Social Sciences and I didn’t really get to know him before he was recruited by Stanford just two years after his arrival. Had he still been at UCI after Princeton Newport Partners was well under way, might we have collaborated? He contributed a key simplification for understanding options, the binary model, and I might have been able to convince him that markets have significant inefficiencies—in other words, opportunities for abnormal risk-adjusted returns. Discussing this in 1975 when I invited him to lecture at UCI, Bill argued that my rewards from PNP didn’t demonstrate market inefficiency, because you could argue that I and my associates were simply getting paid according to our worth. Had we turned our talents to other areas of economic endeavor we could expect the same. Before costs, each passive investor gets the same return as the index.

Stories sell stocks: the wonderful new product that will revolutionize everything, the monopoly that controls a product and sets prices, the politically connected and protected firm that gorges at the public trough, the fabulous mineral discovery, and so forth. The careful investor, when he hears such tales, should ask a key question: At what price is this company a good buy? What price is too high? Suppose, after doing your analysis of the company’s financial statements, management, business model, and prospects, you conclude that it’s worth buying at $40 a share, at which price you expect not only a satisfactory excess risk-adjusted return but have a margin of safety in case your analysis is flawed. Suppose you also conclude that the expected return at $80 is substandard, so the stock is likely overpriced. Typically you’ll avoid investing in stocks when they are trading above your buy price but, if you follow many companies carefully, from time to time some will be attractive purchases. The range between your “buy” price and the “likely overpriced” level, in this case from $40 to $80, is likely to be narrower for better, more experienced investors, enabling them to participate in more situations and with greater confidence.


pages: 302 words: 86,614

The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds by Maneet Ahuja, Myron Scholes, Mohamed El-Erian

activist fund / activist shareholder / activist investor, Asian financial crisis, asset allocation, asset-backed security, backtesting, Bernie Madoff, Bretton Woods, business process, call centre, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, fixed income, high net worth, interest rate derivative, Isaac Newton, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, merger arbitrage, Myron Scholes, NetJets, oil shock, pattern recognition, Ponzi scheme, quantitative easing, quantitative trading / quantitative finance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, systematic trading, zero-sum game

The Avenue team prides itself on its comprehensive approach and broad experience across a large number of distressed opportunities as well as cycles over the past 20-plus years. Understanding the fundamental value of companies in distress as well as the unique risks and opportunities of bankruptcy is a particular strength of Lasry’s investment team. As a result, the firm has captured outsized risk-adjusted returns in the distressed investing space. “You need to understand how a company’s going to operate in the bankruptcy process and how that’s going to affect its ongoing operations,” says Lasry. “You’ve got to mesh many different disciplines into one. That’s our edge. We have the expertise to understand those different disciplines better than others.” “What has made Buffett successful, what has made other people very, very good, is their ability to see things in the available data that others don’t see,” Lasry continues.

Although this is the ultimate goal of all hedge funds, most don’t control risk and take significant factor exposures. Bridgewater realized that by concentrating on alpha strategies, unconstrained management (i.e., no benchmark other than LIBOR), it could enhance investor returns and provide them with lower risk than by combining both alpha and beta strategies. Or, to put it differently, it is always possible to port an alpha-producing strategy to any factor exposure and provide superior risk-adjusted returns. The unconstrained alpha strategy produces abnormal returns; the beta strategies produce systematic returns. Ahuja is an energetic lady. That energy flows throughout the book. She has a great group of Masters who explain their craft, who they are, and how they think, through informative and stimulating interviews. I don’t know how she was able to assemble this amazing group (or even how she got me to write this afterword), but we are fortunate to have so many wonderful insights in one place. 1The classic example of efficiency gain is the farmer transferring the risk of his wheat crop to the miller who stores the crop until the bakers ask for the flour to make the bread and cakes that we consume.


pages: 263 words: 80,594

Stolen: How to Save the World From Financialisation by Grace Blakeley

"Robert Solow", activist fund / activist shareholder / activist investor, asset-backed security, balance sheet recession, bank run, banking crisis, banks create money, Basel III, basic income, battle of ideas, Berlin Wall, Big bang: deregulation of the City of London, bitcoin, Bretton Woods, business cycle, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collective bargaining, corporate governance, corporate raider, credit crunch, Credit Default Swap, cryptocurrency, currency peg, David Graeber, debt deflation, decarbonisation, Donald Trump, eurozone crisis, Fall of the Berlin Wall, falling living standards, financial deregulation, financial innovation, Financial Instability Hypothesis, financial intermediation, fixed income, full employment, G4S, gender pay gap, gig economy, Gini coefficient, global reserve currency, global supply chain, housing crisis, Hyman Minsky, income inequality, inflation targeting, Intergovernmental Panel on Climate Change (IPCC), Kenneth Rogoff, Kickstarter, land value tax, light touch regulation, low skilled workers, market clearing, means of production, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, negative equity, neoliberal agenda, new economy, Northern Rock, offshore financial centre, paradox of thrift, payday loans, pensions crisis, Ponzi scheme, price mechanism, principal–agent problem, profit motive, quantitative easing, race to the bottom, regulatory arbitrage, reserve currency, Right to Buy, rising living standards, risk-adjusted returns, road to serfdom, savings glut, secular stagnation, shareholder value, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, the built environment, The Great Moderation, too big to fail, transfer pricing, universal basic income, Winter of Discontent, working-age population, yield curve, zero-sum game

The CWF should balance its investments between domestic and international assets that support the aims of the Green New Deal with maximising risk-adjusted returns. The PAM would also manage the private assets of domestic savers via public pensions pots, and the mutual and insurance funds that currently send their capital to private asset managers for investment. These funds would be encouraged — either via tax incentives or regulation — to allow the PAM to invest funds on their behalf. The government should also consider providing tax breaks to savers who invest their money in the PAM upon the point of withdrawal. The aim of the private fund would have to be to maximise risk-adjusted returns, with the Green New Deal coming as a secondary consideration, but this could be subject to negotiations between the PAM and the mutual and insurance funds involved and their members.


pages: 467 words: 154,960

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

Albert Einstein, Atul Gawande, backtesting, beat the dealer, Bernie Madoff, Black Swan, buy and hold, 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, 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, William of Occam, zero-sum game

Campbell remained with his fund, which today is the oldest commodity fund still trading.67 However, it is unfair to refer to Campbell and Co. as a “commodity fund” because Campbell trades more than just commodities. Jim Little of Campbell and Co. makes this clear when he describes the widely diverse markets they trade, which include stocks: “We always are looking for non-correlated absolute return strategies that can produce higher quality risk adjusted returns; whether that is more managed futures strategy models or long/short equities or whatever. We have 30 years of experience doing long/short stock indexes, bond futures, and currencies; to do it in individual equities (stocks) isn’t that much different.” But, like John W. Henry, trading diverse markets doesn’t translate into complicated trading strategies. Campbell also believes in keeping it simple: “I’m very uncomfortable with black box trading where I’m dealing with algorithms I don’t understand.

That’s why 80 percent of mutual funds don’t beat the averages. Fear of Volatility and Confusion with Risk There are organizations that rank and track monthly performance numbers. One organization gives a “star ranking” (like Morningstar): “The quantitative rating system employed ranks and rates the performance of all commodity trading advisors (CTA)…Ratings are given in four categories: a) equity, b) performance, c) risk exposure, and d) risk-adjusted returns. In each category, the highest possible rating is five stars and the lowest possible rating is one star. The actual statistics on which the percentiles are based as follows: 1. Performance: Rate of Return 2. Risk: Standard Deviation 3. Risk Adjusted: Sharpe Ratio 4. Equity: Assets”5 The class of those who have the ability to think their own thoughts is separated by an unbridgeable gulf from the class of those who cannot.

The following illustrations highlight the significance of failure to adjust for dividends in high yielding stocks: 329 330 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets The same error impacts exits (stops) in a similar manner. A casual glance at the first chart of Cousins Properties clearly shows significant volatility that is a function only of a corporate action, not of monetary losses. This “phantom” volatility can result in your exit price being breached when it otherwise would not have been, as well as negatively impacting risk-adjusted return metrics. A price chart supplied by the typical database or charting service often does not tell the whole story. Failure to adjust for cash dividends will result in an understatement of the profitability of owning dividend paying stocks. This error is a direct function of the dividend yield of the security in question; the higher the yield, the greater the error. Failure to adjust for cash dividends will also overstate the profitability of any short-selling strategies because the short seller (who must borrow shares to short) is responsible for compensating the actual owner of the shares for any dividends paid.


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

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, business cycle, 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, 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

A seventh objection is that relying solely on leverage relative to total assets, rather than relative to risk-weighted assets, again risks arbitrage, with banks choosing riskier assets since they would not be penalized for doing so and might, in this way, hope to meet their return on equity targets. Peter Sands of Standard Chartered has made this argument.45 But shareholders should only be interested in their risk-adjusted returns. If taking on more risk does not raise risk-adjusted returns, shareholders should flee. If it does raise risk-adjusted returns, it should have happened anyway. Moreover, with substantially higher equity, banks could take on more risk safely. Finally, the disaster came from what banks wrongly thought to be safe. Risk-weighting is extremely unreliable, because the samples from which the weights are derived are always too small or irrelevant. A similar objection arises over regulatory arbitrage: with high levels of equity imposed on banks, risk would migrate elsewhere, as happened prior to the crisis of 2007–08.


pages: 354 words: 26,550

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

algorithmic trading, asset allocation, asset-backed security, automated trading system, backtesting, Black Swan, Brownian motion, business cycle, business process, buy and hold, capital asset pricing model, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, computerized trading, diversification, equity premium, fault tolerance, financial 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 maximum gain potential at every frequency is determined by the sum of all per-period ranges at that frequency. The gain potential in the high-frequency space is nothing short of remarkable, as is the maximum potential loss, which is equal to the negative maximum gain. Careful strategy design, extensive back testing, risk management, and implementation are needed to realize the high-frequency gain potential. The profitability of a trading strategy is often measured by Sharpe ratios, a risk-adjusted return metric first proposed by Sharpe (1966). As Table 7.2 shows, maximum Sharpe ratios increase with increases in trading frequencies. From March 11, 2009, through March 22, 2009, the maximum possible annualized Sharpe ratio for EUR/USD trading strategies with daily position rebalancing was 37.3, while EUR/USD trading strategies that held positions for 10 seconds could potentially score Sharpe ratios well over the 5,000 mark.

Journal of Financial Economics 39, 321–351. 318 REFERENCES Lyons, Richard K., 1996. “Optimal Transparency in a Dealer Market with an Application to Foreign Exchange.” Journal of Financial Intermediation 5, 225–254. Lyons, Richard K., 2001. The Microstructure Approach to Exchange Rates. MIT Press. MacKinlay, A.C., 1997. “Event Studies in Economics and Finance.” Journal of Economic Literature XXXV, 13–39. Mahdavi, M., 2004. “Risk-Adjusted Return When Returns Are Not Normally Distributed: Adjusted Sharpe Ratio.” Journal of Alternative Investments 6 (Spring), 47–57. Maki, A. and T. Sonoda, 2002. “A Solution to the Equity Premium and Riskfree Rate Puzzles: An Empirical Investigation Using Japanese Data.” Applied Financial Economics 12, 601–612. Markowitz, Harry M., 1952. “Portfolio Selection,” Journal of Finance 7 (1), 77–91. Markowitz, Harry, 1959.


pages: 416 words: 106,532

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

Airbnb, altcoin, asset allocation, asset-backed security, autonomous vehicles, bitcoin, blockchain, Blythe Masters, business cycle, business process, buy and hold, capital controls, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial innovation, fixed income, George Gilder, Google Hangouts, high net worth, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, RAND corporation, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ross Ulbricht, Satoshi Nakamoto, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, Uber for X, Vanguard fund, WikiLeaks, Y2K

Gox Data sourced from CoinDesk The year 2014 was the only time bitcoin had a negative Sharpe ratio, when it lost 60 percent of its value from the start to the end of the year. Recall that 2014 was the year of bitcoin’s painful decent from its late 2013 high to its early 2015 low, with Chinese regulations, Mt. Gox implosions, and Silk Road associations plaguing the price of the asset.10 Meanwhile, 2016 was bitcoin’s best risk-adjusted return year since 2013. Digging into the comparison between 2013 and 2016, it’s remarkable that 2013’s Sharpe ratio was only double that of 2016, even though bitcoin’s returns in 2013 were so much greater, as shown in Figure 7.16. Figure 7.16 Bitcoin’s annual appreciation Data sourced from CoinDesk With capital appreciation in 2013 at 45 times greater than that of 2016, it would be reasonable to expect bitcoin in 2013 to have had a Sharpe ratio many times greater than in 2016.

What’s most surprising is bitcoin’s Sharpe ratio in 2016 was almost as high as its overall Sharpe ratio since the launch of Mt. Gox, the first exchange that gave mainstream investors access to bitcoin (1.65 for 2016 vs. 1.66 since Mt. Gox). Figure 7.18 Bitcoin’s Sharpe ratio compared to major U.S. stock indices in 2016 Data sourced from Bloomberg and CoinDesk Some people are apt to think that the best years to be a bitcoin investor are past. However, looking at the Sharpe Ratio, 2016 had risk-adjusted returns that were as good as those of an investor who bought bitcoin when the mainstream first had the opportunity to do so. CORRELATION Diversification is accomplished by selecting a variety of assets that have low to negative correlation with one another. A group of stocks is inherently more diversified than a single stock, and therefore the volatility should be lower. Cryptoassets have near-zero correlation to other capital market assets.


pages: 401 words: 109,892

The Great Reversal: How America Gave Up on Free Markets by Thomas Philippon

airline deregulation, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, barriers to entry, bitcoin, blockchain, business cycle, business process, buy and hold, Carmen Reinhart, carried interest, central bank independence, commoditize, crack epidemic, cross-subsidies, disruptive innovation, Donald Trump, Erik Brynjolfsson, eurozone crisis, financial deregulation, financial innovation, financial intermediation, gig economy, income inequality, income per capita, index fund, intangible asset, inventory management, Jean Tirole, Jeff Bezos, Kenneth Rogoff, labor-force participation, law of one price, liquidity trap, low cost airline, manufacturing employment, Mark Zuckerberg, market bubble, minimum wage unemployment, money market fund, moral hazard, natural language processing, Network effects, new economy, offshore financial centre, Pareto efficiency, patent troll, Paul Samuelson, price discrimination, profit maximization, purchasing power parity, QWERTY keyboard, rent-seeking, ride hailing / ride sharing, risk-adjusted returns, Robert Bork, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Snapchat, spinning jenny, statistical model, Steve Jobs, supply-chain management, Telecommunications Act of 1996, The Chicago School, the payments system, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, Travis Kalanick, Vilfredo Pareto, zero-sum game

They have managed until recently to keep their fees high by hiding them from their clients. Most people simply do not know what they pay. Conflicts of interest are pervasive in the industry. For instance, Daniel Bergstresser, John M. R. Chalmers, and Peter Tufano (2009) find that broker-sold mutual funds deliver lower risk-adjusted returns, even before subtracting distribution costs. John Chalmers and Jonathan Reuter (2012) show that brokers’ client portfolios earn significantly lower risk-adjusted returns than matched portfolios based on target-date funds. Broker clients allocate more dollars to higher-fee funds. In fact, investors tend to perform better when they do not have access to brokers. Sendhil Mullainathan, Markus Noeth, and Antoinette Schoar (2012) document that advisers fail to de-bias their clients and often reinforce their biases.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

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

Do you feel the hedge fund industry is following the path of the mutual fund industry, which became equivalent to the market, or actually worse than the market once you take into account transaction costs? I believe the overall returns make hedge funds a less compelling investment than they once were. Where do you see the steady-state equilibrium for the hedge fund industry? It seems to me that the steady state would be when there is no excess risk-adjusted return in the hedge funds. Wouldn’t you expect there to be some excess return simply because hedge fund investors have to be compensated for accepting greater illiquidity? I agree with that. Where do you think we are now? Do hedge funds still have some premium in risk-adjusted returns versus other investments? My instincts are that they still have an edge, but not by very much. Do you still invest in hedge funds? I haven’t found new good ones to invest in for a while. If I did, though, I would happily invest. So you are still invested in ones from earlier days?

but rather the appropriate questions were “How inefficient is the market?” and “How can we exploit the inefficiencies?” The claim of market efficiency, which implies that no market edge is possible, is a hollow statement because you can’t prove a negative. But you can disprove market efficiency if there are people who have a demonstrable edge. There is a market inefficiency if there is a participant who can generate excess risk-adjusted returns that can be logically explained in a way that is difficult to rebut. Convertible arbitrage is a good example. You can lay out exactly how it works, why it works, and approximately how much return you expect to get. How would you summarize your philosophy of the markets after all these years? I think inefficiencies are there for the finding, but they are fairly hard to find. Do you think it has gotten harder to find inefficiencies, given the increased competition?


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

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

It is available at: http://ssrn .com/abstract::61698 1. T he weU-credentiailcd and highly respected authors of this study performed an analysis of the cost and performance of more than 4000 mutual funds sold by finan cial advisors and selected by investors from 1996 to 2002. Here is what they found: • Funds selected by fi nancial adviso rs significantly IIndtrptrfonntd those selected by investors on their own. The risk-adjusted returns were lowtr. • Funds selected by advisors were hightr cost than those selected by investors on their own. • Advisors did not provide superior asset allocation to their clients. 150 The Real Way Smart Investors Beat 95% of the ~Pros" • Advisors did not preveR[ their diems from pursumg illadvised investor behaviou r, like chasing performance. You have to ask yourself, given these fi ndings, why you would rely on these "investmenr professionals" for financial advice.


pages: 416 words: 118,592

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

accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, backtesting, beat the dealer, Bernie Madoff, BRICs, butter production in bangladesh, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial 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, stocks for the long run, survivorship bias, The Myth of the Rational Market, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

As a result, pricing irregularities and predictable patterns in stock returns can appear over time and even persist for short periods. But I suspect that the end result will not be an abandonment of the belief of many in the profession that the stock market is remarkably efficient in its utilization of information. The EMH’s basic underlying notion that there are obvious opportunities to earn excess risk-adjusted returns and that people will flock to exploit them until they disappear is as reasonable and commonsense as anything put forward by the EMH’s critics. Systematically beating the market remains really hard, and the EMH remains an extremely useful working hypothesis. If any $100 bills are lying around, they will not be there for long. Part Four A PRACTICAL GUIDE FOR RANDOM WALKERS AND OTHER INVESTORS 12 A FITNESS MANUAL FOR RANDOM WALKERS In investing money, the amount of interest you want should depend on whether you want to eat well or sleep well.

If the spread of news is unimpeded, prices will react quickly so that they reflect all that is known. This led me to predict in the 1981 edition that favorable discounts would not always be available. I wrote, “I would be very surprised to see the early-1980s levels of discounts perpetuate themselves indefinitely.” For the same reason, I am skeptical that simple popular rules such as “Buy low P/E stocks” or “Buy small company stocks” will perpetually produce unusually high risk-adjusted returns. And I am also skeptical that large discounts on some emerging-market funds will persist indefinitely. I have recounted the story of the finance professor and his students who spotted a $100 bill lying on the street. “If it was really a $100 bill,” the professor reasoned out loud, “someone would have already picked it up.” Fortunately, the students were skeptical, not only of Wall Street professionals but also of learned professors, and so they picked up the money.


pages: 701 words: 199,010

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

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

TABLE 2.1 LTCM Returns versus Standard Asset Classes The Sharpe ratio is an important, risk-adjusted tool for comparing the performances of different investments or portfolio managers. The higher the ratio, the better the portfolio manager. (See Box 2.1.) Table 2.1 shows that, before 1998, the LTCM fund had a Sharpe ratio five times that of the standard returns of U.S. Treasury bills and bonds. Box 2.1 The Sharpe Ratio The Sharpe ratio measures the return of a portfolio minus the risk-free rate divided by the portfolio’s standard deviation. It is a risk-adjusted return measure that assists in comparing different portfolios or investments, even in the presence of leverage. If portfolio A has a higher Sharpe ratio than portfolio B, then there is no amount of leverage that can make portfolio B as good as A. Sometimes hedge fund returns are distributed non-normally. In these cases, a better measure is the Sortino ratio, which is similar to the Sharpe ratio but divides excess return by the semistandard deviation rather than the standard deviation.

Return distributions are assumed to be normal.2 Using these techniques, we could postulate how LTCM’s risk might have looked at any time before their 1998 collapse. Suppose LTCM had 50 trading strategies in its portfolio, with an average annual return of 6.70% and an average annual volatility of 2.33%. LTCM measured trade correlations at 0.1. This implies that the entire portfolio, without leverage, would have an expected return of 6.70% with an annual volatility of 0.33%. This hypothetical portfolio looked very attractive from a risk-adjusted return perspective, but LTCM altered the return-risk profile with leverage. Their leverage ratio at the beginning of 1998 was 28, which created a portfolio with an expected annual return of 35% and an annual volatility of 22%.3 A simple VaR calculation shows that, with a leverage ratio of 28, the portfolio’s maximum one-month loss would be $58 million. In 99 out of 100 months, the portfolio’s loss would not exceed $58 million.

securitization The act of taking several individual securities or loans and bunching them together and selling the whole pool as a security. shadow banking system The banking system that is performed by investment banks, insurance companies, and other types of companies that are in the business of some form of short-term borrowing and long-term lending. Sharpe ratio Measures the return of a portfolio minus the risk-free rate divided by the portfolio's standard deviation. It is a risk-adjusted return measure that assists in comparing different portfolios or investments, even in the presence of leverage. short the spread A short spread trade is positioned to profit if the spread narrows. For example, if swap yields are 10% and bond yields are 5%. A short spread trade profits when swap yields decrease relative to bond yields. slap hands The game, within the larger game of spoofing, where a trader places a limit order and removes it before the market can hit it.


pages: 320 words: 33,385

Market Risk Analysis, Quantitative Methods in Finance by Carol Alexander

asset allocation, backtesting, barriers to entry, Brownian motion, capital asset pricing model, constrained optimization, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, 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

Finally, the head of the desk will allocate limits to traders designed to maximize the (risk adjusted) return on capital. These allocations (e.g. the trading limits at the very end of the process) cannot be negative, which is the same in mathematical terms as the constraint that no short sales are allowed. And, just as a portfolio manager’s attitude to risk will influence his choice of optimal portfolio, the degree of risk aversion of the head of desk will determine the trading limits that are optimal and the board’s attitude to risk will determine their choice of allocation to global product lines. Even the performance of a trader, just like the performance of a portfolio or of a product line, can be compared in terms of risk-adjusted returns. In mathematical terms these problems are all equivalent. In this chapter we consider the allocation problem from the perspective of an asset manager, but the same principles apply to capital allocation in an investment bank or in any other large financial institution.


pages: 483 words: 141,836

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

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

Next, academics turned to studying trading strategies that hadn’t been published. What if you bought every company after a good earnings announcement, or after a dividend increase, or after a new CEO came on board? What if you bought only small companies, or big companies, or companies in growing industries? Time after time it turned out that the market had priced things very precisely, so the average risk-adjusted return on these strategies was the same. However, it’s very important to understand that all of this evidence—by necessity—dealt with averaging large numbers of transactions over long periods of time. The market could look efficient in these tests and still have lots of individual transactions that were attractive—that a smart investor could discover and use to beat the market. No statistical test could prove that attractive opportunities don’t exist, a point that was well understood by efficient markets workers.

Sharpe Ratios and Wealth Returning to the old school, the two main branches found different kinds of market opportunities, distinguished by Sharpe ratio. We’re going to get a bit mathematical again, but you don’t need the numbers to follow the argument. The Sharpe ratio of a strategy is defined as the return of the strategy minus what you could make investing the same capital in risk-free instruments, divided by the standard deviation of the return. It is a measure of risk-adjusted return. A strategy with an annualized Sharpe ratio of 1 will make more than the risk-free rate about five years out of six. A strategy with an annualized Sharpe ratio of 2 will make more than the risk-free rate about 39 years out of 40. However, it’s hard to find Sharpe ratios near or above 1 in high-capacity, liquid strategies that are inexpensive to run. You don’t need a Sharpe ratio near or at 1 to get rich.


pages: 466 words: 127,728

The Death of Money: The Coming Collapse of the International Monetary System by James Rickards

Affordable Care Act / Obamacare, Asian financial crisis, asset allocation, Ayatollah Khomeini, bank run, banking crisis, Ben Bernanke: helicopter money, bitcoin, Black Swan, Bretton Woods, BRICs, business climate, business cycle, buy and hold, capital controls, Carmen Reinhart, central bank independence, centre right, collateralized debt obligation, collective bargaining, complexity theory, computer age, credit crunch, currency peg, David Graeber, debt deflation, Deng Xiaoping, diversification, Edward Snowden, eurozone crisis, fiat currency, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, floating exchange rates, forward guidance, G4S, George Akerlof, global reserve currency, global supply chain, Growth in a Time of Debt, income inequality, inflation targeting, information asymmetry, invisible hand, jitney, John Meriwether, Kenneth Rogoff, labor-force participation, Lao Tzu, liquidationism / Banker’s doctrine / the Treasury view, liquidity trap, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market clearing, market design, money market fund, money: store of value / unit of account / medium of exchange, mutually assured destruction, obamacare, offshore financial centre, oil shale / tar sands, open economy, plutocrats, Plutocrats, Ponzi scheme, price stability, quantitative easing, RAND corporation, reserve currency, risk-adjusted returns, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Satoshi Nakamoto, Silicon Valley, Silicon Valley startup, Skype, sovereign wealth fund, special drawing rights, Stuxnet, The Market for Lemons, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, trade route, undersea cable, uranium enrichment, Washington Consensus, working-age population, yield curve

The centerpiece of the complex is the White House, a sprawling, multitiered, bleach-white International Style home with a large outdoor pool trimmed with the obligatory steel-post-and-Kevlar tenting reminiscent of the Denver Airport. I was there in the winter of 2003 for a private gathering of top financiers from the institutional, hedge fund, and private equity worlds to discuss the next big thing in alternative investing—a project to blend hedge fund and private equity strategies to optimize risk-adjusted returns. As typically happens at such gatherings, there was downtime for drinks and getting to know the other guests. During one such break, I chatted with the head of one of the largest institutional portfolios in the world. He asked me about my career, and I recounted my early days at Citibank on assignment in Karachi. That had been in the 1980s, not long after the shah of Iran had been deposed in the Iranian Revolution.

The challenge, of course, is being attentive to the indications and warnings and making a timely transition to one of the alternatives already mentioned. On the whole, a portfolio of 20 percent gold, 20 percent land, 10 percent fine art, 20 percent alternative funds, and 30 percent cash should offer an optimal combination of wealth preservation under conditions of inflation, deflation, and social unrest, while providing high risk-adjusted returns and reasonable liquidity. But no portfolio intended to achieve these goals works for the “buy-and-hold” investor. This portfolio must be actively managed. As indications and warnings become more pronounced, and as greater visibility is offered on certain outcomes, the portfolio must be modified in sensible ways. If gold reaches $9,000 per ounce, there may then come a time to sell gold and acquire more land.


Mastering Private Equity by Zeisberger, Claudia,Prahl, Michael,White, Bowen, Michael Prahl, Bowen White

asset allocation, backtesting, barriers to entry, Basel III, business process, buy low sell high, capital controls, carried interest, commoditize, corporate governance, corporate raider, correlation coefficient, creative destruction, discounted cash flows, disintermediation, disruptive innovation, distributed generation, diversification, diversified portfolio, family office, fixed income, high net worth, information asymmetry, intangible asset, Lean Startup, market clearing, passive investing, pattern recognition, performance metric, price mechanism, profit maximization, risk tolerance, risk-adjusted returns, risk/return, shareholder value, Sharpe ratio, Silicon Valley, sovereign wealth fund, statistical arbitrage, time value of money, transaction costs

Benchmarking of most of the parameters of the business plan against competitors’ indicates that the assumptions are ambitious but not overstretched. The price range derived from applying the average/median industry LTM and NTM EBITDA and cycle multiples to your business plan appears supported by other valuation methodologies. At this price, using the proposed funding structure, the returns are at the lower end of your acceptable risk adjusted returns. You have submitted your preliminary bid. The vendors’ advisers have indicated that you have just got into the next round of the auction but your bid is still not compelling enough—they always say that! What other factors, outside of the traditional valuation methodologies, could encourage you to improve your bid or what implicit risks are there in the existing valuation which would strengthen your resolve that you have already put your best foot forward?

Do you have a fund currency advantage in this investment vs competitive bids from funds denominated in a different currency? Fund Portfolio Construction Is the investing PE fund already a well performing fund? If this is the case, the envisaged lower returns from this investment at a marginally higher price are not likely to have any material effect on the overall acceptable range of outcome for the fund. As such, would the fund accept a lower risk adjusted return investment to complete its investment program? Some food for thought, after all there is no prize for coming second in an auction. Buyout Pricing Adjustments and Closing Mechanisms Once a winning bid has established a headline purchase price—i.e., an in-principle agreed upon price—the buyer and seller negotiate the final purchase price. Although the broad principles are set out in a bid letter or a marked-up SPA, the exact amount and composition of transaction proceeds are subject to a series of pricing adjustments.


pages: 209 words: 53,175

The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness by Morgan Housel

"side hustle", airport security, Amazon Web Services, Bernie Madoff, business cycle, computer age, coronavirus, discounted cash flows, diversification, diversified portfolio, Donald Trump, financial independence, Hans Rosling, Hyman Minsky, income inequality, index fund, invisible hand, Isaac Newton, Jeff Bezos, Joseph Schumpeter, knowledge worker, labor-force participation, Long Term Capital Management, margin call, Mark Zuckerberg, new economy, Paul Graham, payday loans, Ponzi scheme, quantitative easing, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, Stephen Hawking, Steven Levy, stocks for the long run, the scientific method, traffic fines, Vanguard fund, working-age population

Morningstar once looked at the performance of tactical mutual funds, whose strategy is to switch between stocks and bonds at opportune times, capturing market returns with lower downside risk.⁵⁰ They want the returns without paying the price. The study focused on the mid-2010 through late 2011 period, when U.S. stock markets went wild on fears of a new recession and the S&P 500 declined more than 20%. This is the exact kind of environment the tactical funds are supposed to work in. It was their moment to shine. There were, by Morningstar’s count, 112 tactical mutual funds during this period. Only nine had better risk-adjusted returns than a simple 60/40 stock-bond fund. Less than a quarter of the tactical funds had smaller maximum drawdowns than the leave-it-alone index. Morningstar wrote: “With a few exceptions, [tactical funds] gained less, were more volatile, or were subject to just as much downside risk” as the hands-off fund. Individual investors fall for this when making their own investments, too. The average equity fund investor underperformed the funds they invested in by half a percent per year, according to Morningstar—the result of buying and selling when they should have just bought and held.⁵¹ The irony is that by trying to avoid the price, investors end up paying double.


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

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

To profit from a quantitative trading business, it is essential to manage your risks in a way that limits your drawdowns to a tolerable level and yet be positioned to use optimal leverage of your equity to achieve maximum possible growth of your wealth. Furthermore, if you have more than one strategy, you will also need to find a way to optimally allocate capital among them so as to maximize overall risk-adjusted return. The optimal allocation of capital and the optimal leverage to use so as to strike the right balance between risk management and maximum growth is the focus of this chapter, and the central tool we use is called the Kelly formula. A OPTIMAL CAPITAL ALLOCATION AND LEVERAGE Suppose you plan to trade several strategies, each with their own expected returns and standard deviations. How should you allocate capital among them in an optimal way?


pages: 272 words: 64,626

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs by Andy Kessler

23andMe, Andy Kessler, bank run, barriers to entry, Berlin Wall, Bob Noyce, British Empire, business cycle, business process, California gold rush, carbon footprint, Cass Sunstein, cloud computing, collateralized debt obligation, collective bargaining, commoditize, computer age, creative destruction, disintermediation, Douglas Engelbart, Eugene Fama: efficient market hypothesis, fiat currency, Firefox, Fractional reserve banking, George Gilder, Gordon Gekko, greed is good, income inequality, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, libertarian paternalism, low skilled workers, Mark Zuckerberg, McMansion, Netflix Prize, packet switching, personalized medicine, pets.com, prediction markets, pre–internet, profit motive, race to the bottom, Richard Thaler, risk tolerance, risk-adjusted returns, Silicon Valley, six sigma, Skype, social graph, Steve Jobs, The Wealth of Nations by Adam Smith, transcontinental railway, transfer pricing, wealth creators, Yogi Berra

Money for people, money for offices, money for computers, for labs, for bandwidth, for heat, for coffee and on and on. If you don’t have enough, you need to attract it, like my Net Net example raising capital and going public. But how? Fortunately, money sloshes around the globe seeking its highest return. To be a true Free Radical, be the highest return. Money goes wherever it damn pleases. Moving around the globe, pulsing through electronic networks and bank databases, seeking to maximize its risk-adjusted return. Maybe someone’s risk tolerance is low so they invest their money in U.S. Treasury Bills. So be it. Others (like me) think that teams of smart people inventing the future are actually less risky than big corporations that are or will soon be under attack from these entrepreneurs, so I invest in small companies and start-ups. That’s me and my money’s prerogative. Others are even more daring and invest in Estonia or the Congo or even Venezuela, places with potentially high returns but huge political or physical risks.


pages: 224 words: 13,238

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

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

Trade and portfolio analytics to model price and evaluate transactions and strategies . Access to securities lending markets to provide direct connectivity to lenders through securities lending networks . Risk management to run and monitor portfolio and aggregate risks . Performance reporting and risk attribution to compute performance records of each strategy, fund, and fund family and provide risk-adjusted return reports to investors independently from the fund administrator.3 14.4 The Impact of Increased Trading Automation Automation has led to an increase in both trades and market data, challenging the infrastructure at hedge funds and prime brokers. The TABB Group estimates that during peak cycles, top-tier prime brokers could be hit with close to 150 trades per second and more than 10 times as 3 Sungard, ‘‘The Emergence of Hedge Funds,’’ SungardWorld 3 no. 1, http://www.sungard.com/ company_info/v311623.pdf.


pages: 250 words: 64,011

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Donald Trump, en.wikipedia.org, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta analysis, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

As a Bloomberg Business headline noted, “Hedge Funds Trail Stocks for Fifth Year with 7.4% Return.”32 This may be a classic case of cherry picking, however, as looking at other time periods produces very different results—including a Wall Street Journal article that noted, “Over the past 15 years, [hedge funds’] returns have beaten the overall stock market.”33 And, to be fair, outperforming the S&P 500 (a constantly changing list of approximately 500 stocks) in terms of nominal returns may not be the goal of all hedge funds, as Berger and others have noted. Rather, the ultimate objective is often to provide the best risk-adjusted returns—a measure that factors in the risk that was taken in order to achieve the returns. Although sometimes, the predictions are off. In one classic example, hedge fund Long-Term Capital Management (LTCM) “lost $4.4 billion of its $4.7 billion in capital” in less than one year, in part due to spreads that didn’t converge as predicted.34 Regardless of their performance, hedge funds sometimes get a bad rap because of the salaries that some hedge fund managers earn.


pages: 1,164 words: 309,327

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

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

For example, managers lower their portfolio betas by selling securities and leaving the proceeds in cash, by selling high beta securities and buying low beta securities, by selling index futures, by buying puts, by selling calls, or by selling securities short. They raise their portfolio betas by doing the opposite. Risk-adjusted excess returns are best computed frequently because the portfolio beta changes whenever the manager exchanges assets that have different betas. To accurately estimate risk-adjusted returns, analysts must multiply market returns by concurrent portfolio betas. Analysts who compute risk-adjusted returns often also compute market-timing returns. The market-timing return is the difference between the portfolio beta times the market return and the market return. It indicates whether the portfolio manager is a skilled market timer. To summarize, raw portfolio returns can be broken into the sum of three parts: the market return, the market timing return, and the risk-adjusted excess return: Raw Return = (Raw Return - Beta X Market Return) + (Beta X Market Return - Market Return) + Market Return = Excess Return + Market Timing Return + Market Return.

They use specialized indexes to evaluate portfolios that invest in other asset classes. Table 22-1 provides a list of commonly used benchmark indexes. Market-adjusted returns are portfolio returns minus corresponding market index returns. The market-adjusted returns in the above example are 10 percent and —15 percent. For most purposes, market-adjusted returns demonstrate how well the portfolio has performed better than raw returns do. 22.2.2.2 Risk-adjusted Returns Analysts sometimes further adjust raw returns to account for the exposure of portfolios to known risks. For example, consider the exposure of a portfolio to market risk. Market risk is the risk that values will rise or fall with marketwide changes in value. It varies by security, and therefore also by portfolio. Analysts characterize market risk by the market beta of a security. Beta measures the extent to which the security fluctuates in value with the market.


pages: 250 words: 77,544

Personal Investing: The Missing Manual by Bonnie Biafore, Amy E. Buttell, Carol Fabbri

asset allocation, asset-backed security, business cycle, buy and hold, diversification, diversified portfolio, Donald Trump, employer provided health coverage, estate planning, fixed income, Home mortgage interest deduction, index fund, Kickstarter, money market fund, mortgage tax deduction, risk tolerance, risk-adjusted returns, Rubik’s Cube, Sharpe ratio, stocks for the long run, Vanguard fund, Yogi Berra, zero-coupon bond

Keep in mind, you’ll have to pay taxes on any capital gains if you sell your shares. 94 Chapter 5 The Sharpe ratio helps you figure out whether a fund manager’s returns are due to great investment decisions or to an inordinate fondness for risk. It is a ratio of the return you earn to the risk you take, that is, how much the fund returns for the risk taken. The higher the Sharpe ratio, the better a fund’s risk-adjusted returns. To find a fund’s Sharpe ratio, click the Ratings & Risks tab on a Morningstar fund web page. Finding Funds Funds are investor-friendly investments, but finding the right funds isn’t a slam-dunk. Besides a ton of funds to choose from, you’ve got tons of fund information to wade through. And you may need different types of funds for the asset allocation you’ve picked for retirement investments, college savings, and other goals.


pages: 236 words: 77,735

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

affirmative action, asset allocation, backtesting, barriers to entry, Bernie Madoff, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, call centre, Credit Default Swap, diversification, diversified portfolio, estate planning, fiat currency, financial innovation, fixed income, Flash crash, follow your passion, German hyperinflation, High speed trading, housing crisis, index fund, joint-stock company, money market fund, moral hazard, Myron Scholes, passive investing, Ponzi scheme, price discovery process, random walk, risk tolerance, risk-adjusted returns, risk/return, stocks for the long run, stocks for the long term, too big to fail, trade route, Vanguard fund, walking around money

The best opportunities involve the worst of the worst junk bonds, are illiquid, and end up as the hunting ground of the best hedge fund managers in the world. Not exactly the place for Main Street investors. Plus, the real trash is not available via your online broker. From a practical level, the cost of trading, minimum size, and research to do it right is highly specialized. Adding to the problem of incorporating junk bonds into a portfolio is the high correlation to equity markets with lower risk-adjusted returns. Over the past five years we can see a correlation to the S&P 500 of 0.63. This is not bad, and some would say if this was for an equity basket, it is better than the 0.9 correlation with the MSCI Emerging Markets Index. However, when you match that with raw annual volatility of 21 percent versus 25 percent for the S&P 500, you just don’t get a big enough bang for your buck to compensate for the risk.


pages: 192 words: 75,440

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel

backtesting, barriers to entry, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, family office, fixed income, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, merger arbitrage, offshore financial centre, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stocks for the long run, systematic trading, unpaid internship, value at risk, yield curve, yield management

We, of course, want you to be excited and will encourage you to pursue your dream job, yet we would be remiss if we didn’t caution that getting a job with a hedge fund is extremely difficult. There are simply many more people who want to work at hedge funds than there are openings. In that scenario, the hiring firms can afford to be very selective and bring on only those people who they believe have precisely what it takes to succeed. Although hedge funds differ significantly depending on their investment style, the goal of all of them is to produce superior risk-adjusted returns for their investors. To work at one, you should ideally be passionate about investing. At some funds, you may be able to get away with just being interested in investing, but it will not set you apart as much from other candidates. You need to be able to thrive in a pressure-packed environment and work as part of a close-knit group of highly skilled professionals in which the performance of each investment can be measured on a daily basis.


pages: 318 words: 77,223

The Only Game in Town: Central Banks, Instability, and Avoiding the Next Collapse by Mohamed A. El-Erian

activist fund / activist shareholder / activist investor, Airbnb, balance sheet recession, bank run, barriers to entry, break the buck, Bretton Woods, British Empire, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, collapse of Lehman Brothers, corporate governance, currency peg, disruptive innovation, Erik Brynjolfsson, eurozone crisis, financial innovation, Financial Instability Hypothesis, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, friendly fire, full employment, future of work, Hyman Minsky, If something cannot go on forever, it will stop - Herbert Stein's Law, income inequality, inflation targeting, Jeff Bezos, Kenneth Rogoff, Khan Academy, liquidity trap, Martin Wolf, megacity, Mexican peso crisis / tequila crisis, moral hazard, mortgage debt, Norman Mailer, oil shale / tar sands, price stability, principal–agent problem, quantitative easing, risk tolerance, risk-adjusted returns, risk/return, Second Machine Age, secular stagnation, sharing economy, sovereign wealth fund, The Great Moderation, The Wisdom of Crowds, too big to fail, University of East Anglia, yield curve, zero-sum game

Instead, take some of this positioning and barbell it into lower- and higher-risk exposures—namely, accumulating more cash and short-dated high-quality government bonds while investing a smaller part of it in less-trafficked areas that involve new opportunities (including new tech startups), directly sourced infrastructure, and the completion of markets in the emerging word. • When it comes to portfolio positioning in the more highly trodden segments of the markets, recognize that sector- and security-specific portfolio differentiation (or what is known as “alpha” in the marketplace) will likely be a better potential generator of risk-adjusted returns than market-wide positioning (“beta”); and if you are going to opt for beta anyway, have a look at the work done on getting smarter passive exposures by such thinkers as Rob Arnott, the CEO of Research Affiliates. Concurrently, investors will need to be more sensitive to specific events, including M&A opportunities and emerging firms using disruptive technologies. In turn, these considerations speak to four potentially more controversial views.


pages: 267 words: 71,941

How to Predict the Unpredictable by William Poundstone

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

The buy and sell trigger pairs that underperformed the market did so because they had too much downtime in bonds. Say you had picked 8 and 22 as your limits. This would have averaged only a 4.85 percent return. You might think that was a dud. But with those limits, the PE-directed portfolio would have been in stocks only 37 percent of the time. It beat the market while it was in stocks and offered the safety of fixed-income investments the rest of the time. By risk-adjusted return, that’s not so bad. To top the S&P 500’s return, you need to be more selective about limit values. The historical record suggests that this is not too difficult to do. Many pairs of buy and sell thresholds would have beaten the market by half a percentage point a year. Most trading schemes have you trade a lot to eke out a minuscule advantage. With this system you hardly trade at all. Had you used 13 and 28 limits, you would have made only three trades in the past 132 years.


pages: 333 words: 76,990

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

"Robert Solow", asset allocation, banking crisis, banks create money, barriers to entry, Berlin Wall, Big bang: deregulation of the City of London, Bretton Woods, business cycle, buy and hold, Cass Sunstein, central bank independence, collective bargaining, computer age, credit crunch, debt deflation, decarbonisation, diversification, dividend-yielding stocks, equity premium, Fall of the Berlin Wall, financial innovation, fixed income, Flash crash, forward guidance, Francis Fukuyama: the end of history, George Akerlof, housing crisis, index fund, invention of the printing press, Isaac Newton, James Watt: steam engine, joint-stock company, Joseph Schumpeter, Kickstarter, liberal capitalism, light touch regulation, liquidity trap, Live Aid, market bubble, Mikhail Gorbachev, mortgage debt, negative equity, Network effects, new economy, Nikolai Kondratiev, Nixon shock, oil shock, open economy, price stability, private sector deleveraging, Productivity paradox, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Simon Kuznets, South Sea Bubble, special economic zone, stocks for the long run, technology bubble, The Great Moderation, too big to fail, total factor productivity, trade route, tulip mania, yield curve

One of the famous discussions about this relationship, and its implications for investors and asset allocation, followed a controversial speech given by George Ross Goobey, general manager of the Imperial Tobacco pension fund in the UK in 1956 to the Association of Superannuation and Pension Funds (ASPF).2 He argued the merits of investing in equities to generate inflation-linked growth for pension funds. He became famous for allocating the entirety of the pension fund's investments to equities, a move that is often associated with the start of the so-called cult of the equity. Prior to this, equities were largely seen as volatile or risky assets that achieved lower risk-adjusted returns than government bonds and, consequently, required a higher yield (and therefore lower valuation). As more institutions warmed to the idea of shifting funds into equities to protect against inflation, the yield on equities declined and the so-called reverse yield gap was born. This refers to the fall in dividend yields to below government bond yields: a pattern that continued, in most developed economies, until the collapse of the technology bubble in the late 1990s.


pages: 258 words: 71,880

Street Fighters: The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street by Kate Kelly

bank run, buy and hold, collateralized debt obligation, corporate governance, corporate raider, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, fixed income, housing crisis, index arbitrage, Long Term Capital Management, margin call, moral hazard, quantitative hedge fund, Renaissance Technologies, risk-adjusted returns, shareholder value, technology bubble, too big to fail, traveling salesman

Those who sought Cayne’s help with the more nitty-gritty aspects of Bear’s business were often disappointed. During the early 2000s, Pat Lewis, then a midlevel employee in the firm’s internal financial department, spent three years working on what he called the “risk-based capital allocation” plan, a way of assessing the financial health of Bear’s various business units by seeing how much risk they took on relative to how much revenue they generated—in other words, their risk-adjusted returns. For three years Lewis and his boss tracked the numbers in Bear’s key departments, and built the technical models needed to run the assessments. CFO Sam Molinaro liked the idea, and it seemed a sensible way to formulate decisions about the use of Bear’s cash based on the soundness of the individual units it was supporting. All that was needed was a green light from Cayne. Knowing their plan would require a bite-sized explanation, Lewis and his boss spent weeks working on what they called a “Jimmy document” that could explain how the risk-based capital allocation program would work in plain terms; the document was eventually presented to Cayne but it was all for naught.


pages: 352 words: 87,930

Space 2.0 by Rod Pyle

additive manufacturing, air freight, barriers to entry, Colonization of Mars, commoditize, crony capitalism, crowdsourcing, Donald Trump, Elon Musk, experimental subject, Intergovernmental Panel on Climate Change (IPCC), Jeff Bezos, low earth orbit, Mars Rover, mouse model, risk-adjusted returns, Search for Extraterrestrial Intelligence, Silicon Valley, Silicon Valley startup, stealth mode startup, Stephen Hawking, telerobotics, trade route, wikimedia commons, X Prize, Y Combinator

He has since spent well over $100 million of that money on SpaceX, an investment that has netted him NASA contracts worth billions, as well as launches for the US Air Force and private satellite companies. Elon Musk, founder of SpaceX. Image credit: SpaceX SpaceX, like Blue Origin and a select few other companies in Space 2.0, is largely personality-driven. It’s a big-vision company, directed by a larger-than-life individual with big ideas. “If the objective was to achieve the best risk-adjusted return,” Musk noted in 2016, “starting a rocket company is insane. But that was not my objective. I had certainly come to the conclusion that if something didn’t happen to improve rocket technology we would be stuck on earth forever. And the big aerospace companies had no interest in radical innovation. All they wanted to do was make their old technology slightly better every year, and sometimes it would actually get worse.”51 Elon Musk meets Charles Bolden, NASA administrator, 2012.


pages: 332 words: 81,289

Smarter Investing by Tim Hale

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

A sobering thought. Overconfidence destroys wealth The same researchers also looked at 35,000 household accounts from a large brokerage firm from February 1991 to January 1997 (Barber and Odean, 2002). They found that, consistent with other research that shows that men tend to be more overconfident than women (although both are overconfident), men trade 45% more than women. This is reflected in risk-adjusted returns 1.4% a year lower than women. Looking at single women and men, single men trade 67% more than women and generate annual risk-adjusted net returns 2.3% less than single women. Given that by and large self-invested investors, i.e. those that buy and sell shares and other investments themselves through a brokerage account, underperform the markets, as we saw above, this is bad news for wealth accumulation for either sex. 5.5 Simple steps to control evolution All is not lost.


pages: 353 words: 88,376

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

Albert Einstein, asset allocation, asset-backed security, Brownian motion, business cycle, business process, buy and hold, 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, dogs of the Dow, equity premium, fixed income, implied volatility, index fund, intangible asset, interest rate swap, inventory management, London Interbank Offered Rate, margin call, 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

An R-squared of 100 means that all the movements of a security are explained completely by movements in the index. A high R-squared (between 85 and 100) indicates that the fund’s performance patterns have been in line with the index. A fund with a low R-squared (70 or less) does not move in lockstep with an index. A higher R-squared value indicates a more useful beta figure. For example, if a fund has an R-squared value close to 100 but has a beta below 1, it most likely is offering higher risk-adjusted returns. A low R-squared means that an investor should ignore the beta. Related Terms: • Alpha • Mutual Fund • Treasury Bill—T-Bill • Benchmark • Risk Run Rate What Does Run Rate Mean? (1) How the financial performance of a company would look if one extrapolated current results out over a certain period. (2) The average annual dilution from company stock option grants over the most recent three-year period recorded in the annual report.


pages: 355 words: 92,571

Capitalism: Money, Morals and Markets by John Plender

activist fund / activist shareholder / activist investor, Andrei Shleifer, asset-backed security, bank run, Berlin Wall, Big bang: deregulation of the City of London, Black Swan, bonus culture, Bretton Woods, business climate, business cycle, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, collapse of Lehman Brothers, collective bargaining, computer age, Corn Laws, corporate governance, creative destruction, credit crunch, Credit Default Swap, David Ricardo: comparative advantage, deindustrialization, Deng Xiaoping, discovery of the americas, diversification, Eugene Fama: efficient market hypothesis, eurozone crisis, failed state, Fall of the Berlin Wall, fiat currency, financial innovation, financial intermediation, Fractional reserve banking, full employment, God and Mammon, Gordon Gekko, greed is good, Hyman Minsky, income inequality, inflation targeting, information asymmetry, invention of the wheel, invisible hand, Isaac Newton, James Watt: steam engine, Johann Wolfgang von Goethe, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, joint-stock company, Joseph Schumpeter, labour market flexibility, liberal capitalism, light touch regulation, London Interbank Offered Rate, London Whale, Long Term Capital Management, manufacturing employment, Mark Zuckerberg, market bubble, market fundamentalism, mass immigration, means of production, Menlo Park, money market fund, moral hazard, moveable type in China, Myron Scholes, Nick Leeson, Northern Rock, Occupy movement, offshore financial centre, paradox of thrift, Paul Samuelson, plutocrats, Plutocrats, price stability, principal–agent problem, profit motive, quantitative easing, railway mania, regulatory arbitrage, Richard Thaler, rising living standards, risk-adjusted returns, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, shareholder value, short selling, Silicon Valley, South Sea Bubble, spice trade, Steve Jobs, technology bubble, The Chicago School, The Great Moderation, the map is not the territory, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, too big to fail, tulip mania, Upton Sinclair, Veblen good, We are the 99%, Wolfgang Streeck, zero-sum game

In periods when the return on capital is higher than the rate of economic growth, the resulting premium is the amount investors require to compensate for the risk of deviations from the norm that could destroy income and wealth. There can be no scientific guarantee that the return on capital will outstrip economic growth and at the time of writing there is widespread concern in capital markets that the risk-adjusted return on capital is below current rates of growth in the developed world. But should Piketty’s assertion turn out to be right, the already extreme levels of inequality in pre-tax income and wealth in much of the developed world would indeed become even more extreme. Hence his case for far higher marginal tax rates on high incomes and a progressive global wealth tax.199 If policymakers found his arguments persuasive, that would imply a return to the level of taxation reached in Western democracies in the 1970s – the high water mark in terms of redistribution.


pages: 363 words: 98,024

Keeping at It: The Quest for Sound Money and Good Government by Paul Volcker, Christine Harper

anti-communist, Ayatollah Khomeini, banking crisis, Bretton Woods, business cycle, central bank independence, corporate governance, Credit Default Swap, Donald Trump, fiat currency, financial innovation, fixed income, floating exchange rates, forensic accounting, full employment, global reserve currency, income per capita, inflation targeting, liquidationism / Banker’s doctrine / the Treasury view, margin call, money market fund, Nixon shock, Paul Samuelson, price stability, quantitative easing, reserve currency, Right to Buy, risk-adjusted returns, Ronald Reagan, Rosa Parks, secular stagnation, Sharpe ratio, Silicon Valley, special drawing rights, too big to fail, traveling salesman, urban planning

Along with other members of my generation, I was struck a couple of decades ago by the enthusiastic presentation of a young London investment banker to a high-level business conference at the Villa d’Este in Lake Como, Italy. He concluded with a strong warning: businesses, particularly financial businesses, that were not fully aware of and capable of using the new instruments of finance would be doomed to failure. I found myself sitting in the audience next to William Sharpe, a 1990 Nobel laureate in economics whose “Sharpe ratio” has become a widely accepted measure of risk-adjusted returns for fund managers. I nudged him and asked how much this new financial engineering contributed to economic growth, measured by GNP. “Nothing,” he whispered back to me. It was not the answer I anticipated. “So what does it do?” was my response. “It just moves around the [economic] rents* in the financial system. Besides it’s a lot of fun.” (Later, at dinner, he suggested the possibility of small ways in which economic welfare could be advanced, but I felt I had already gotten the gist of his thinking.)


pages: 338 words: 106,936

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

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, business cycle, butterfly effect, buy and hold, 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, Vilfredo Pareto, volatility smile

Here, though, the opposite is the case: on Wall Street, success breeds imitation, and the more firms there are implementing a strategy, the less profitable it is for anyone. There are some indications, however, that the Prediction Company has been wildly successful. As one former board member I spoke with pointed out, it is still an active subsidiary of UBS, after more than a decade. Another knowledgeable source told me that, over the firm’s first fifteen years, its risk-adjusted return was almost one hundred times larger than the S&P 500 return over the same period. Farmer stayed with the firm for about a decade before his passion for research lured him back to academia. He took a position at the Santa Fe Institute as a full-time researcher in 1999. Packard stayed with the company for a few more years, serving as CEO until 2003, when he left to start a new company, called ProtoLife.


pages: 339 words: 109,331

The Clash of the Cultures by John C. Bogle

asset allocation, buy and hold, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, estate planning, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, Flash crash, Hyman Minsky, income inequality, index fund, interest rate swap, invention of the wheel, market bubble, market clearing, money market fund, mortgage debt, new economy, Occupy movement, passive investing, Paul Samuelson, Ponzi scheme, post-work, principal–agent problem, profit motive, random walk, rent-seeking, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, statistical arbitrage, survivorship bias, The Wealth of Nations by Adam Smith, transaction costs, Vanguard fund, William of Occam, zero-sum game

The combined expense/turnover cost ratio for the lowest-cost quartile is 0.96 percent, and for the highest-cost quartile 2.86 percent, a cost advantage of fully 1.90 percentage points per year. This cost gap helped drive an even larger 1.5 percentage-point advantage in annual performance—9.0 percent versus 7.5 percent (see Exhibit 5.2). Since the high-cost funds (annual standard deviation of 19.8 percent) have assumed about 30 percent more risk than the low-cost funds (standard deviation of 17.5 percent), the gap in risk-adjusted returns is even larger. Please do not underestimate the impact of what might seem moderate differences between returns and costs. Based on an actual investment of $10,000 20 years ago, a fund earning 9.0 percent over the past 20 years would have produced a profit of $46,000; at a 7.5 percent rate, the profit would be just $32,500—a 40 percent increase in capital appreciation. Whether we like it or not, the jury is in.


pages: 502 words: 107,657

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, job satisfaction, Johann Wolfgang von Goethe, lifelogging, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, speech recognition, statistical model, Steven Levy, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

No question about it: All involved relished this fiesta, and the party raged on and on, continuing almost nine years, consistently outperforming the overall market all along. The system chugged, autonomously trading among a dozen market sectors such as technology, transportation, and healthcare. John says the system “beat the market each year and exhibited only two-thirds its standard deviation—a home run as measured by risk-adjusted return.” But all good things must come to an end, and, just as John had talked his client up, he later had to talk him down. After nearly a decade, the key measure of system integrity began to decline. John was adamant that they were running on fumes, so with little ceremony the entire fund was wound down. The system was halted in time, before catastrophe could strike. In the end, all the investors came out ahead.


pages: 363 words: 107,817

Modernising Money: Why Our Monetary System Is Broken and How It Can Be Fixed by Andrew Jackson (economist), Ben Dyson (economist)

bank run, banking crisis, banks create money, Basel III, Bretton Woods, business cycle, call centre, capital controls, cashless society, central bank independence, credit crunch, David Graeber, debt deflation, double entry bookkeeping, eurozone crisis, financial exclusion, financial innovation, Financial Instability Hypothesis, financial intermediation, floating exchange rates, Fractional reserve banking, full employment, Hyman Minsky, inflation targeting, informal economy, information asymmetry, intangible asset, land reform, London Interbank Offered Rate, market bubble, market clearing, Martin Wolf, means of production, money: store of value / unit of account / medium of exchange, moral hazard, mortgage debt, negative equity, Northern Rock, price stability, profit motive, quantitative easing, Real Time Gross Settlement, regulatory arbitrage, risk-adjusted returns, seigniorage, shareholder value, short selling, South Sea Bubble, The Great Moderation, the payments system, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, unorthodox policies

For example, in a situation where a bank had a profitable lending opportunity but no money in its Investment Pool it could seek to attract more funds by changing the interest rate it offers on its Investment Accounts.4 An increase in the offered rate would attract money into its Investment Accounts. Conversely interest rates could be lowered if the supply of funds outweighed demand from eligible and creditworthy borrowers. Banks could also seek to alter the funds in Investment Accounts through non-interest rate measures, such as altering any guarantees provided on the accounts. By doing so (e.g. by guaranteeing 90% of the value of an account rather than 80%) the bank alters the risk-adjusted return which – other things equal – will affect the attractiveness of the investment and the amount of funds put into such an Investment Account. If a bank did wish to attract money into Investment Accounts by raising interest rates, it would need to either increase the interest rates it charged on loans in order to maintain profits (the ‘spread’) or accept a lower margin. However, it is important to note that any increase in the interest rate banks charge on loans is limited in scope.


Capital Ideas Evolving by Peter L. Bernstein

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

Unlike traditional marketable securities, absolute return investments provide returns largely independent of overall market moves. . . . An important attribute of Yale’s investment strategy concerns the alignment of interests between investors and investment managers [especially relating to] many of the pitfalls of the principal-agent relationship. . . . Private equity offers extremely attractive long-term risk-adjusted return characteristics, stemming from the University’s strong stable of value-added managers that exploit market inefficiencies. . . . Real estate, oil and gas, and timberland provide attractive return prospects, excellent portfolio diversification, [are] a hedge against unanticipated inf lation, [and] an opportunity to exploit inefficiencies. . . . The real assets portfolio plays a meaningful role in the Endowment as a powerful diversifying tool and a generator of strong returns.


pages: 354 words: 105,322

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

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

As a coup de grâce, fund managers claim capital gains tax treatment on what are thinly veiled management fees, so everyday taxpayers are looted again. This is why private equity fund mavens are billionaires living on latifundia-style estates near Telluride, Colorado, and Jackson Hole, Wyoming. There’s no reason for you to facilitate the looting or be a victim. Hedge funds are a challenging case. They work in theory, not in practice. Hedge funds aim to produce real risk-adjusted returns, known as alpha. This is done through market timing, long-short strategies, and arbitrage. Investors who are long stocks for the long run endure periodic crashes and prolonged bear markets to enjoy spectacular bull markets. The problem is we may not live long enough to recover severe losses, or we may be forced sellers (tuition, anyone?) at market lows. Hedge funds purport to outperform long-only portfolios.


pages: 1,042 words: 266,547

Security Analysis by Benjamin Graham, David Dodd

activist fund / activist shareholder / activist investor, asset-backed security, backtesting, barriers to entry, business cycle, buy and hold, 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 airline, low cost carrier, moral hazard, mortgage debt, Myron Scholes, 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

Fueled by performance pressures and a growing expectation of low (and inadequate) returns from traditional equity and debt investments, institutional investors have sought high returns and diversification by allocating a growing portion of their endowments and pension funds to alternatives. Pioneering Portfolio Management, written in 2000 by David Swensen, the groundbreaking head of Yale’s Investment Office, makes a strong case for alternative investments. In it, Swensen points to the historically inefficient pricing of many asset classes,10 the historically high risk-adjusted returns of many alternative managers, and the limited performance correlation between alternatives and other asset classes. He highlights the importance of alternative manager selection by noting the large dispersion of returns achieved between top-quartile and third-quartile performers. A great many endowment managers have emulated Swensen, following him into a large commitment to alternative investments, almost certainly on worse terms and amidst a more competitive environment than when he entered the area.

If you don’t agree with that statement, try looking for the bonds that were rated AAA a few decades ago or mortgage-backed securities that went from AAA to junk status in 2007. In Security Analysis, the principle is developed and reiterated that “a high coupon rate is not adequate compensation for the assumption of substantial risk of principal.” (p. 125 on accompanying CD) This statement would seem to rule out investing in high yield bonds, which has been successfully pursued over the last 30 years with absolute and risk-adjusted returns well above those on investment-grade bonds. A more thorough reading, however, shows that securities that the authors say should not be purchased “on an investment basis” can still be considered “for speculation.” Nevertheless, today Graham and Dodd’s blanket statement certainly seems doctrinaire—especially in that it implements a distinction that has almost entirely ceased to exist. The statement that certain assets either are or aren’t appropriate for purchase on an investment basis is probably one of the dicta to which I reacted negatively 43 years ago.


pages: 402 words: 110,972

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

AI winter, algorithmic trading, asset allocation, banking crisis, barriers to entry, Big bang: deregulation of the City of London, business cycle, butter production in bangladesh, butterfly effect, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, citizen journalism, collateralized debt obligation, corporate governance, Craig Reynolds: boids flock, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Danny Hillis, demand response, disintermediation, distributed generation, diversification, diversified portfolio, Emanuel Derman, en.wikipedia.org, experimental economics, financial innovation, fixed income, Gordon Gekko, implied volatility, index arbitrage, index fund, information retrieval, intangible asset, Internet Archive, John Nash: game theory, Kenneth Arrow, load shedding, Long Term Capital Management, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, market fragmentation, market microstructure, Mars Rover, Metcalfe’s law, moral hazard, mutually assured destruction, Myron Scholes, natural language processing, negative equity, Network effects, optical character recognition, paper trading, passive investing, pez dispenser, phenotype, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, QWERTY keyboard, RAND corporation, random walk, Ray Kurzweil, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, Robert Metcalfe, Ronald Reagan, Rubik’s Cube, semantic web, Sharpe ratio, short selling, Silicon Valley, Small Order Execution System, smart grid, smart meter, social web, South Sea Bubble, statistical arbitrage, statistical model, Steve Jobs, Steven Levy, Tacoma Narrows Bridge, the scientific method, The Wisdom of Crowds, time value of money, too big to fail, transaction costs, Turing machine, Upton Sinclair, value at risk, Vernor Vinge, yield curve, Yogi Berra, your tax dollars at work

We use a variety of holdback samples, statistical and financial measures, and heuristics to identify what we consider promising models. The ability of the GA to incorporate these diverse elements (in contrast to simpler statistical tools) is part of its appeal in this context. • Risk and return. The obvious candidate for fitness in this context is financial return. This is used in most simple GA trading strategies, and certainly makes sense. In more complex strategies, there are other aspects to consider. Measures of risk-adjusted returns, such as the Sharpe ratio, are more appropriate. • Statistical fitness. In many contexts, the performance of an investment strategy will depend on multiple models working together; there may not even be a sensible measure of the return to a single model in a complex strategy using combined forecasts and portfolio optimization. In these situations, a statistical measure of the fitness of the model is appropriate.


pages: 401 words: 115,959

Philanthrocapitalism by Matthew Bishop, Michael Green, Bill Clinton

Albert Einstein, anti-communist, barriers to entry, battle of ideas, Bernie Madoff, Bob Geldof, Bonfire of the Vanities, business process, business process outsourcing, Charles Lindbergh, clean water, cleantech, corporate governance, corporate social responsibility, Dava Sobel, David Ricardo: comparative advantage, don't be evil, family office, financial innovation, full employment, global pandemic, global village, God and Mammon, Hernando de Soto, high net worth, Intergovernmental Panel on Climate Change (IPCC), invisible hand, James Dyson, John Harrison: Longitude, joint-stock company, knowledge economy, knowledge worker, Live Aid, lone genius, Marc Andreessen, market bubble, mass affluent, microcredit, Mikhail Gorbachev, Nelson Mandela, new economy, offshore financial centre, old-boy network, peer-to-peer lending, performance metric, Peter Singer: altruism, plutocrats, Plutocrats, profit maximization, profit motive, Richard Feynman, risk tolerance, risk-adjusted returns, Ronald Coase, Ronald Reagan, shareholder value, Silicon Valley, Slavoj Žižek, South Sea Bubble, sovereign wealth fund, stem cell, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Malthus, Thorstein Veblen, trade liberalization, transaction costs, trickle-down economics, wealth creators, winner-take-all economy, working poor, World Values Survey, X Prize

The hope was that distributing the remaining cash quickly would help salvage the fund’s reputation after it had threatened to renege on grants promised to some of the late princess’s favorite causes. Putting an end date on a foundation is a rarity historically. It is too early to tell whether today’s philanthrocapitalists will take a different approach, or succumb to the lure of a sort of immortality. Great claims are made for MRI. One report from McKinsey argues that this type of investment can earn the same risk-adjusted returns as mainstream investments. Well, maybe. On the other hand, many MRIs, such as the Ford Foundation loans to nonprofits, are explicitly given at below market rates. It may be worthwhile for a foundation such as Ford to accept a slightly lower return on investment in return for philanthropic impact, but foundations need to be clear what effect this will have on their future giving potential.


pages: 374 words: 114,600

The Quants by Scott Patterson

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

The Absolute Return Fund had lost more than half of its assets from its peak, dropping to about $1.5 billion from about $4 billion in mid-2007. AQR in total had about $7 billion in so-called alternative funds and about $13 billion in long-only funds, down sharply from the $40 billion it sat on heading into August 2007, when it was planning an IPO. In a little more than a year, AQR had lost nearly half its war chest. AQR’s poor performance shocked its investors. So-called absolute return funds were supposed to provide positive risk-adjusted returns in any kind of market—they were expected to zig when the market zagged. But Absolute Return seemed to follow the S&P 500 like a magnet. One reason behind the parallel tracks: in early 2008, AQR had made a big wager that U.S. stocks would rise. According to its value-centric models, large U.S. stocks were a bargain relative to a number of other assets, such as Treasury bonds and markets in other countries.


pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

The Citizens United decision is an open invitation to tech firms to escalate the prices they charge for audiences, as billionaire donors are eager to foot the bill. Recall again Vaidhyanathan’s title, The Googlization of Everything. For Big Data buffs, “Googlization” is ultimately a hopeful process: systematic use of analytics to squeeze maximum effectiveness out of any decision; maximum relevance from any search; THE HIDDEN LOGICS OF SEARCH 97 maximum risk-adjusted return from any investment. To paraphrase Jeff Jarvis, today’s businesses should ask themselves, “What would Google do?” But the answer to that question is all too clear: use their data to outflank competitors and extract maximum profits from their customers.210 “Googlization” has an even darker meaning, too: that whole industries stand to be taken over by Google itself.211 Walmart (Walmart!) has said that it considers Google one of its most formidable competitors.


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

Asian financial crisis, banking crisis, barriers to entry, beat the dealer, Black-Scholes formula, commodity trading advisor, computer vision, East Village, Edward Thorp, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John Meriwether, John von Neumann, locking in a profit, Long Term Capital Management, margin call, money market fund, Myron Scholes, paper trading, passive investing, pattern recognition, random walk, risk tolerance, risk-adjusted returns, short selling, Silicon Valley, statistical arbitrage, the scientific method, transaction costs, Y2K

My typical target is a double within twelve months. Unless I believe the stock has that potential, I probably will not be interested. In the case of Tektronix, the stock hit the double less than six months after we bought it, and we significantly reduced our position. Usually, when I get out of a stock, I still believe there is at least 20 or 30 percent left on the upside, but the key question is whether I can get a better risk-adjusted return somewhere else. So once a stock you buy approximately doubles, the question is no longer, "Will it move higher?" but rather, "Can I buy something else that will give me a higher return with less risk?" Yes, it comes back to the notion that we restrict ourselves to fifteen stocks. If we have a position in the portfolio, it means that it still has to be more attractive on a risk-reward basis than any other opportunity we could find as a replacement.


pages: 482 words: 121,672

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

accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, beat the dealer, Bernie Madoff, bitcoin, butter production in bangladesh, buttonwood tree, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Detroit bankruptcy, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, financial repression, fixed income, framing effect, George Santayana, 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, stocks for the long run, survivorship bias, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond, zero-sum game

Those traders who ensure that information is quickly reflected in market prices must be able, at least, to cover their costs. But it is reasonable to ask whether our financial markets are relatively efficient, and I believe that the evidence is very powerful that our markets come very close to the EMH ideal. Information does get reflected rapidly in security prices. The EMH’s basic underlying notion—that if there are obvious opportunities to earn excess risk-adjusted returns, people will flock to exploit them until they disappear—is as reasonable and commonsense as anything put forward by the EMH’s critics. If any $100 bills are lying around, they will not be there for long. CAPITALIZATION-WEIGHTED INDEXING REMAINS AT THE TOP OF THE CLASS In conclusion, capitalization-weighted indexing is unlikely to be deposed as the overwhelming favorite in the battle for index supremacy.


pages: 431 words: 132,416

No One Would Listen: A True Financial Thriller by Harry Markopolos

backtesting, barriers to entry, Bernie Madoff, buy and hold, call centre, centralized clearinghouse, correlation coefficient, diversified portfolio, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, family office, financial thriller, fixed income, forensic accounting, high net worth, index card, Long Term Capital Management, Louis Bachelier, offshore financial centre, Ponzi scheme, price mechanism, quantitative trading / quantitative finance, regulatory arbitrage, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, statistical arbitrage, too big to fail, transaction costs, your tax dollars at work

It has reported losses of no more than 55 basis points in just four of the past 139 consecutive months, while generating highly consistent gross returns of slightly more than 1.5 percent a month and net annual returns roughly in the range of 15.0 percent. Among all the funds on the database in that same period, the Madoff/ Fairfield Sentry fund would place at number 16 if ranked by its absolute cumulative returns. Among 423 funds reporting returns over the last five years, most with less money and shorter track records, Fairfield Sentry would be ranked at 240 on an absolute return basis and come in number 10 if measured by risk-adjusted return as defined by its Sharpe ratio. What is striking to most observers is not so much the annual returns—which, though considered somewhat high for the strategy, could be attributed to the firm’s market making and trade execution capabilities—but the ability to provide such smooth returns with so little volatility. The best known entity using a similar strategy, a publicly traded mutual fund dating from 1978 called Gateway, has experienced far greater volatility and lower returns during the same period.


pages: 545 words: 137,789

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

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

Another way to characterize such arrangements is as a “trader’s option,” because they give the employee a free option on the upside to his trades. Some banks try to mitigate this problem by charging their trading desks a rental fee on the firm’s money they trade with. If the desk invests in risky areas, such as commodities, the rental fee is higher than if it invests in Treasury bonds, say. When these schemes work, traders get rewarded only if they create positive risk-adjusted returns, commonly known as “alpha.” This type of reward structure can mitigate incentive problems, but it doesn’t eliminate them. Clever traders will try to game the system by taking risks that aren’t reflected in the benchmark they are judged against. Take the practice of writing credit default swaps. In exchange for receiving a positive return most of the time, the providers of credit protection bear the hidden “tail risk” of a low probability of a very bad outcome.


pages: 515 words: 142,354

The Euro: How a Common Currency Threatens the Future of Europe by Joseph E. Stiglitz, Alex Hyde-White

bank run, banking crisis, barriers to entry, battle of ideas, Berlin Wall, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, cashless society, central bank independence, centre right, cognitive dissonance, collapse of Lehman Brothers, collective bargaining, corporate governance, correlation does not imply causation, credit crunch, Credit Default Swap, currency peg, dark matter, David Ricardo: comparative advantage, disintermediation, diversified portfolio, eurozone crisis, Fall of the Berlin Wall, fiat currency, financial innovation, full employment, George Akerlof, Gini coefficient, global supply chain, Growth in a Time of Debt, housing crisis, income inequality, incomplete markets, inflation targeting, information asymmetry, investor state dispute settlement, invisible hand, Kenneth Arrow, Kenneth Rogoff, knowledge economy, light touch regulation, manufacturing employment, market bubble, market friction, market fundamentalism, Martin Wolf, Mexican peso crisis / tequila crisis, money market fund, moral hazard, mortgage debt, neoliberal agenda, new economy, open economy, paradox of thrift, pension reform, pensions crisis, price stability, profit maximization, purchasing power parity, quantitative easing, race to the bottom, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, savings glut, secular stagnation, Silicon Valley, sovereign wealth fund, the payments system, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, transfer pricing, trickle-down economics, Washington Consensus, working-age population

Europe had created a divergent system even as it thought it was putting together a convergent one. Several features of the eurozone that were thought of as essential to its success were actually central to its divergence. Standard economics is based on the gravity principle: money moves from capital-rich countries with low returns to countries with capital shortage. The presumption was that the risk-adjusted returns in such countries would be high. But in Europe under the euro, movements of not just capital but also labor seem to defy the principles of gravity. Money flowed upward.1 In this chapter, I explain how Europe created this gravity-defying system. Understanding the sources of the divergence is essential to creating a eurozone that works. DIVERGENCE IN CAPITAL AND FINANCIAL MARKETS AND THE SINGLE-MARKET PRINCIPLE One of the strengths of the eurozone was that capital and labor could move freely throughout the region.


pages: 497 words: 150,205

European Spring: Why Our Economies and Politics Are in a Mess - and How to Put Them Right by Philippe Legrain

3D printing, Airbnb, Asian financial crisis, bank run, banking crisis, barriers to entry, Basel III, battle of ideas, Berlin Wall, Big bang: deregulation of the City of London, Boris Johnson, Bretton Woods, BRICs, British Empire, business cycle, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, cleantech, collaborative consumption, collapse of Lehman Brothers, collective bargaining, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, currency manipulation / currency intervention, currency peg, debt deflation, Diane Coyle, disruptive innovation, Downton Abbey, Edward Glaeser, Elon Musk, en.wikipedia.org, energy transition, eurozone crisis, fear of failure, financial deregulation, first-past-the-post, forward guidance, full employment, Gini coefficient, global supply chain, Growth in a Time of Debt, hiring and firing, hydraulic fracturing, Hyman Minsky, Hyperloop, immigration reform, income inequality, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Irish property bubble, James Dyson, Jane Jacobs, job satisfaction, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, labour market flexibility, labour mobility, liquidity trap, margin call, Martin Wolf, mittelstand, moral hazard, mortgage debt, mortgage tax deduction, North Sea oil, Northern Rock, offshore financial centre, oil shale / tar sands, oil shock, open economy, peer-to-peer rental, price stability, private sector deleveraging, pushing on a string, quantitative easing, Richard Florida, rising living standards, risk-adjusted returns, Robert Gordon, savings glut, school vouchers, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, smart meter, software patent, sovereign wealth fund, Steve Jobs, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, Tyler Cowen: Great Stagnation, working-age population, Zipcar

Estimates of the boost to trade vary; perhaps the most authoritative study, by Richard Baldwin and others, reckons the euro increased it by 5 per cent between 1999 and 2006.112 The euro has also stimulated cross-border business investment, notably in manufacturing, enabling firms to merge and restructure their activities across national lines, while also attracting increased investment from outside the eurozone. Studies agree that the euro has boosted foreign direct investment (but differ as to how much).113 No doubt the euro has also generated some other positive financial flows across the eurozone, allowing investors to diversify their portfolios and earn higher risk-adjusted returns, particularly on equity investments. But because the euro’s launch coincided with the biggest financial bubble in history across the Western financial system, those positive flows were swamped by misdirected cross-border bank lending. And when European banks came unstuck, it turned out that they were still national after all. Sharing the euro has also protected its members against damaging exchange-rate instability, notably in 2008 and 2009, as the chapter will explain.


pages: 807 words: 154,435

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

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

We can indeed benefit from the insights of both Thales of Miletus and Harry Markowitz, and learn from both of the contradictory narratives of the world of finance propagated by Gene Fama and Bob Shiller. But we must also recognise the limits to the insights we derive from their small-world models. There are those in the finance sector who create programs which purport to define strategies that would maximise risk-adjusted returns. But these programs do nothing of the kind. Radical uncertainty precludes optimising behaviour. In the world as it is, we cope rather than optimise. The numbers which were used in these calculations are invented. Or they are derived from historic data series and assume a non-existent stationarity in the world. Struggling to cope with a large world which they could only imperfectly understand, the proponents of these calculations invented a small world which gave them the satisfaction of clear-cut answers.


pages: 923 words: 163,556

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

algorithmic trading, Benoit Mandelbrot, capital asset pricing model, collateralized debt obligation, correlation coefficient, distributed generation, diversified portfolio, fixed income, index fund, Louis Bachelier, Myron Scholes, p-value, quantitative trading / quantitative finance, random walk, risk-adjusted returns, short selling, stochastic volatility, Thomas Bayes, transaction costs, value at risk

Further specialization within econometrics, and the area that directly relates to this book, is financial econometrics. As Jianqing Fan (2004) writes, financial econometricsuses statistical techniques and economic theory to address a variety of problems from finance. These include building financial models, estimation and inferences of financial models, volatility estimation, risk management, testing financial economics theory, capital asset pricing, derivative pricing, portfolio allocation, risk-adjusted returns, simulating financial systems, hedging strategies, among others. Robert Engle and Clive Granger, two econometricians who shared the 2003 Nobel Prize in Economics Sciences, have contributed greatly to the field of financial econometrics. Historically, the core probability and statistics course offered at the university level to undergraduates has covered the fundamental principles and applied these principles across a wide variety of fields in the natural sciences and social sciences. universities typically offered specialized courses within these fields to accommodate students who sought more focused applications.


pages: 726 words: 172,988

The Bankers' New Clothes: What's Wrong With Banking and What to Do About It by Anat Admati, Martin Hellwig

Andrei Shleifer, asset-backed security, bank run, banking crisis, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, bonus culture, break the buck, business cycle, Carmen Reinhart, central bank independence, centralized clearinghouse, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, diversified portfolio, en.wikipedia.org, Exxon Valdez, financial deregulation, financial innovation, financial intermediation, fixed income, George Akerlof, Growth in a Time of Debt, income inequality, information asymmetry, invisible hand, Jean Tirole, joint-stock company, joint-stock limited liability company, Kenneth Rogoff, Larry Wall, light touch regulation, London Interbank Offered Rate, Long Term Capital Management, margin call, Martin Wolf, money market fund, moral hazard, mortgage debt, mortgage tax deduction, negative equity, Nick Leeson, Northern Rock, open economy, peer-to-peer lending, regulatory arbitrage, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Satyajit Das, shareholder value, sovereign wealth fund, technology bubble, The Market for Lemons, the payments system, too big to fail, Upton Sinclair, Yogi Berra

The bank’s new CEO, Antony Jenkins, announced in August 2012 that his ROE target would be above the bank’s stated “cost of capital” of 11.5 percent (see “New Barclays CEO Sets Sights on ‘Credible’ RoE Plan,” Reuters, August 30, 2012). He did not explain how this cost of equity was estimated and whether it could be reduced if the bank had more equity. In fact, Allison (2011, loc. 409) states that megabanks generally fail to generate the risk-adjusted returns that shareholders should expect. Mayo (2011) describes how, as an analyst, he has often been critical of banks’ investment decisions. 18. On the flaws of ROE targets, see, for example, Anat Admati, “Beware of Bankers’ Flawed ROE Measure,” New York Times, July 25, 2011, and “Change Bank Pay Now—BoE’s Robert Jenkins,” Reuters, October 31, 2011, and note 33 of this chapter. 19. Andrew Haldane, the executive director for financial stability at the Bank of England, has argued that the high ROEs banks achieved for a period of time prior to the crisis can be fully explained by increased leverage and risk and cannot be interpreted as an indication of bankers’ performance; see Haldane (2010). 20.


pages: 741 words: 179,454

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

affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

Sharpe ratios are ex post (based on actual risk) rather than ex ante (expected risk). Actual returns should be compared to expected risk at the time the position was taken. Insufficient attention is paid to the asymmetry of hedge fund returns, which do not follow the familiar bell-shaped normal distribution. Risk models grossly underestimate tail risk, exposure to large price moves. Traders arb internal risk metrics to inflate risk-adjusted returns to increase bonuses. Real hedge fund risks—correlation, liquidity, complexity, and model risk—are not measured properly. If the portfolio of long and short positions is perfectly balanced and prices move identically, then the gains and losses should cancel out, reducing risk but earning zero return. To make money, the correlation, the relationship between the long and short securities, must change.


pages: 920 words: 233,102

Unelected Power: The Quest for Legitimacy in Central Banking and the Regulatory State by Paul Tucker

Andrei Shleifer, bank run, banking crisis, barriers to entry, Basel III, battle of ideas, Ben Bernanke: helicopter money, Berlin Wall, Bretton Woods, business cycle, capital controls, Carmen Reinhart, Cass Sunstein, central bank independence, centre right, conceptual framework, corporate governance, diversified portfolio, Fall of the Berlin Wall, financial innovation, financial intermediation, financial repression, first-past-the-post, floating exchange rates, forensic accounting, forward guidance, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, George Akerlof, incomplete markets, inflation targeting, information asymmetry, invisible hand, iterative process, Jean Tirole, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, liberal capitalism, light touch regulation, Long Term Capital Management, means of production, money market fund, Mont Pelerin Society, moral hazard, Northern Rock, Pareto efficiency, Paul Samuelson, price mechanism, price stability, principal–agent problem, profit maximization, quantitative easing, regulatory arbitrage, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Robert Bork, Ronald Coase, seigniorage, short selling, Social Responsibility of Business Is to Increase Its Profits, stochastic process, The Chicago School, The Great Moderation, The Market for Lemons, the payments system, too big to fail, transaction costs, Vilfredo Pareto, Washington Consensus, yield curve, zero-coupon bond, zero-sum game

Say a statute empowers an agency to make rules requiring “prudent conduct” of banks and that the overall purpose of the statute is financial stability, defined as conditions under which the supply of core financial services will be preserved in the face of a shock up to a specified size (see part IV). Then when issuing rules defining prudent conduct, “prudent conduct” should be interpreted to mean conduct material to preserving stability as defined, not conduct that would help to protect investors or make the economy dynamic or deliver a rationally assessed risk-adjusted return. This approach echoes the 1950s’ US Legal Process School of Hart and Sacks, but distinguishing between different kinds of administrative-agency regime according to their general purpose (commitment, exploration/experimentation, delegated politicized decision making) (Hart and Sacks, Legal Process). My thanks to Jeremy Waldron for alerting me to this. See also Stack, “Purposivism.” 12 This precept has helped me make sense of my discomfort, relayed in chapter 7, when the UK’s former Financial Services Authority planned to move from basing the protection of retail investors on the regulation of distribution to the regulation of products. 13 Bellamy, “Constitutional Democracy.” 14 This is akin to the explication of Westminster supremacy in Goldsworthy, Parliamentary Sovereignty. 15 As explained in the introduction to part II, our robustness test does not seek an “overlapping consensus” in reasons/justifications.