risk-adjusted returns

The Missing Billionaires: A Guide to Better Financial Decisions
by Victor Haghani and James White
Published 27 Aug 2023

We're still relying on the mechanics of Expected Utility—remember the definition of CER involves Expected Utility—we're just using CER as a tool for translating Utility into a metric that is more concrete and intuitive. Risk‐adjusted Return and the Price of Risk Investment professionals often talk about “Risk‐adjusted Return.” In particular, active investment managers of various stripes tend to state their goal as something like “to achieve superior Risk‐adjusted Return.” This concept intuitively makes sense. The level of risk involved in delivering a given level of return is clearly something we should care about. But what exactly is meant by Risk‐adjusted Return is often left a bit fuzzy. We now have a good way of thinking clearly and precisely about this: CER has the two qualities we want in a Risk‐adjusted Return metric: Given two investments with very different risk‐and‐return profiles, and which are independent of each other and the rest of your holdings, we'd be indifferent between them if they had the same impact on the CER of our total wealth.

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More complex and realistic models can find decreasing equity holdings with age to be optimal, but it is useful to know that the starting point in the most basic case is that the allocation to risky assets stays constant with age. The formula for the optimal spending rate to a very long horizon is:b where is the long (infinite) horizon optimal spending rate, is the Risk‐Adjusted Return of the optimal portfolio, is the investor's rate of time preference, and is the investor's level of constant relative risk‐aversion. As Risk‐adjusted Return rises you can optimally spend more, but an extra 1% higher Risk‐adjusted Return doesn't increase your current spending by 1%. This is because your preference for smooth and constant spending over time has to be weighed against the prospect of spending more in the future by deferring spending today to invest at the higher rate.

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In case the rule isn't indelibly imprinted in your memory, yet, here it is for an infinite horizon: where is the long (infinite) horizon optimal spending rate, is the Risk‐adjusted Return of the portfolio, is the investor's rate of time preference, and is the investor's level of constant relative risk‐aversion. The optimal fraction to spend is a function of three inputs: The Risk‐adjusted Return, , of the total portfolio when invested at the optimal risk level. Higher allows for higher spending rates, but not on a one‐for‐one basis for investors who are not infinitely risk‐averse. The rate of time preference, . Higher increases the optimal spending rate. The endowment's level of risk‐aversion, . If the portfolio's expected Risk‐adjusted Return is higher than the endowment's time preference (as it is under our Base Case), then higher risk‐aversion increases the optimal spending rate.

Investing Amid Low Expected Returns: Making the Most When Markets Offer the Least
by Antti Ilmanen
Published 24 Feb 2022

Fallen-angel bonds earned double the excess return of the all-HY bond index between 1997 and 2020 (4.6% versus 2.3%); the edge remained unchanged between 2010 and 2020 (6.5% versus 4.2%).38 Front-end opportunity: I emphasized the high risk-adjusted returns of conservative credit strategies at short maturities, which would require leverage to really matter. This pattern is consistent with common leverage aversion among investors. The risk-adjusted return (information ratio) has declined monotonically with maturity – e.g. 0.45 IR for one- to three-year corporates and 0.05 for over 25-year corporates between 1989 and 2020. The patterns are similar since 2010 (IRs of 0.74 and 0.12).

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Equation 4 gives this extended version of FLAM. Equation 4: FLAM was originally written for an active stock-picker, but it works as well for asset allocation and factor allocation even in terms of absolute risk-adjusted returns (SRs). When we assess diversification across market risk premia or long/short premia which have nearly uncorrelated returns, the breadth math of halving the volatility and doubling the risk-adjusted returns with four independent return sources is more realistic than it is for diversification within a stock portfolio whose constituents are highly correlated. Specifically, risk parity investing may involve taking equal risk in three or four nearly uncorrelated asset classes with similar SRs and thereby boost the portfolio SR to be 1.5–2 times the typical asset class SR.

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We've all heard that diversification is the only free lunch in investing. But did you know that well-executed diversification is indistinguishable from magic?2 Diversification's ability to reduce portfolio volatility and to improve risk-adjusted returns is perhaps best captured by the role of breadth in the fundamental law of active management (FLAM) in the previous chapter. Improving breadth seems an easier way to double risk-adjusted returns than improving skill. I cover below a few practical examples. Global equity diversification versus home bias. The FLAM has less bite in this case because equity markets are highly correlated across markets, so the effective increase in breadth is limited.

Trading Risk: Enhanced Profitability Through Risk Control
by Kenneth L. Grant
Published 1 Sep 2004

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.

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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.

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In addition to the sheer divergence from market realities that is implicit in this approach, such methodologies often miss the more subtle components of exposure that may cause more actual revenue damage than that caused by events that have a statistical probability of recurrence, say, once in a century. Under these circumstances, you may find yourself in the frustrating situation of having to manage to limit structures that significantly diminish your ability to deliver risk-adjusted return and, in some cases, may actually provide incentive to make suboptimal risk-reward decisions. This is indeed unfortunate, and any associated frustration you feel is certainly justified. However, it would be a serious mistake to allow this to interfere with creating the most efficient portfolio parameters that you can possibly devise, given the constraints you face.

Commodity Trading Advisors: Risk, Performance Analysis, and Selection
by Greg N. Gregoriou , Vassilios Karavas , François-Serge Lhabitant and Fabrice Douglas Rouah
Published 23 Sep 2004

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.

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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).

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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.

Portfolio Design: A Modern Approach to Asset Allocation
by R. Marston
Published 29 Mar 2011

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.

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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. 

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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.

Expected Returns: An Investor's Guide to Harvesting Market Rewards
by Antti Ilmanen
Published 4 Apr 2011

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.

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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.

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Table of Contents Title Page Copyright Page Foreword Dedication Acknowledgments Abbreviations and acronyms Disclaimer Part I - Overview, historical returns, and academic theories Chapter 1 - Introduction 1.1 HISTORICAL PERFORMANCE 1.2 FINANCIAL AND BEHAVIORAL THEORIES: A BRIEF HISTORY OF IDEAS 1.3 FORWARD-LOOKING INDICATORS 1.4 VIEW-BASED EXPECTED RETURNS 1.5 GENERAL COMMENTS ABOUT THE BOOK 1.6 NOTES Chapter 2 - Whetting the appetite: Historical averages and forward-looking returns 2.1 HISTORICAL PERFORMANCE SINCE 1990 2.2 SAMPLE-SPECIFIC RESULTS: DEALING WITH THE PITFALLS 2.3 FORWARD-LOOKING RETURN INDICATORS 2.4 NOTES Chapter 3 - The historical record: The past 20 years in a longer perspective 3.1 STOCKS 3.2 BONDS 3.3 REAL ASSET INVESTING AND ACTIVE INVESTING 3.4 FX AND MONEY MARKETS 3.5 REAL RETURN HISTORIES 3.6 NOTES Chapter 4 - Road map to terminology 4.1 CONSTANT OR TIME-VARYING EXPECTED RETURNS? 4.2 RATIONAL OR IRRATIONAL EXPECTATIONS FORMATION? 4.3 RETURN MEASUREMENT ISSUES 4.4. RETURNS IN WHAT CURRENCY? 4.5 RISK-ADJUSTED RETURNS 4.6 BIASED RETURNS 4.7 NOTES Chapter 5 - Rational theories on expected return determination 5.1 THE OLD WORLD 5.2 THE NEW WORLD 5.3 DETOUR: A BRIEF SURVEY OF THE EFFICIENT MARKETS HYPOTHESIS 5.4 NOTES Chapter 6 - Behavioral finance 6.1 LIMITS TO ARBITRAGE 6.2 PSYCHOLOGY 6.3 APPLICATIONS 6.4 CONCLUSION 6.5 NOTES Chapter 7 - Alternative interpretations for return predictability 7.1 RISK PREMIA OR MARKET INEFFICIENCY 7.2 DATA MINING AND OTHER “MIRAGE” EXPLANATIONS 7.3 NOTES Part II - A dozen case studies Chapter 8 - Equity risk premium 8.1 INTRODUCTION AND TERMINOLOGY 8.2 THEORIES AND THE EQUITY PREMIUM PUZZLE 8.3 HISTORICAL EQUITY PREMIUM 8.4 FORWARD-LOOKING (EX ANTE OBJECTIVE) LONG-TERM EXPECTED RETURN MEASURES 8.5 SURVEY-BASED SUBJECTIVE EXPECTATIONS 8.6 TACTICAL FORECASTING FOR MARKET TIMING 8.7 NOTES Chapter 9 - Bond risk premium 9.1 INTRODUCTION, TERMINOLOGY, AND THEORIES 9.2 HISTORICAL AVERAGE RETURNS 9.3 ALTERNATIVE EX ANTE MEASURES OF THE BRP 9.4 YIELD CURVE STEEPNESS: IMPORTANT PREDICTIVE RELATIONS 9.5 EXPLAINING BRP BEHAVIOR: FIRST TARGETS, THEN FOUR DRIVERS 9.6 TACTICAL FORECASTING—DURATION TIMING 9.7 NOTES Chapter 10 - Credit risk premium 10.1 INTRODUCTION, TERMINOLOGY, AND THEORY 10.2 HISTORICAL AVERAGE EXCESS RETURNS 10.3 FOCUS ON FRONT-END TRADING—A POCKET OF ATTRACTIVE REWARD TO RISK 10.4 UNDERSTANDING CREDIT SPREADS AND THEIR DRIVERS 10.5 TACTICAL FORECASTING OF CORPORATE BOND OUTPERFORMANCE 10.6 ASSESSING OTHER NON-GOVERNMENT DEBT 10.7 CONCLUDING REMARKS 10.8 NOTES Chapter 11 - Alternative asset premia 11.1 INTRODUCTION TO ALTERNATIVES 11.2 REAL ESTATE 11.3 COMMODITIES 11.4 HEDGE FUNDS 11.5 PRIVATE EQUITY FUNDS 11.6 NOTES Chapter 12 - Value-oriented equity selection 12.1 INTRODUCTION TO DYNAMIC STRATEGIES 12.2 EQUITY VALUE: INTRODUCTION AND HISTORICAL PERFORMANCE 12.3 TWEAKS INCLUDING STYLE TIMING 12.4 THE REASONS VALUE WORKS 12.5 DOES THE VALUE STRATEGY WORK IN EQUITIES BEYOND INDIVIDUAL STOCK SELECTION ... 12.6 RELATIONS BETWEEN VALUE AND OTHER INDICATORS FOR EQUITY SELECTION 12.7 NOTES Chapter 13 - Currency carry 13.1 INTRODUCTION 13.2 HISTORICAL AVERAGE RETURNS 13.3 IMPROVEMENTS/REFINEMENTS TO THE BASELINE CARRY STRATEGY 13.4 WHY DO CARRY STRATEGIES WORK?

Market Sense and Nonsense
by Jack D. Schwager
Published 5 Oct 2012

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).

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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.

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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.

Beyond Diversification: What Every Investor Needs to Know About Asset Allocation
by Sebastien Page
Published 4 Nov 2020

As Frazzini and Pedersen (2014) explain in “Betting Against Beta”: Many investors—such as individuals, pension funds, and mutual funds—are constrained in the leverage that they can take, and they therefore overweight risky securities instead of using leverage. . . . This behavior of tilting toward high-beta assets suggests that risky high-beta assets require lower risk-adjusted returns than low-beta assets, which require leverage. Makes sense. The authors show that a strategy that levers low-risk assets and shorts high-risk assets (“betting against beta”) delivers “significant positive risk-adjusted returns” across markets. Even lower-risk Treasury and corporate bonds seem to outperform their higher-beta counterparts. They make a credible case. They control for other factor exposures such as size, value, momentum, and liquidity, and they uncover similar risk premiums across a wide range of assets, including country bond indexes, commodities, and currencies.

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Edelen provides an intuitive example: Consider the performance of an open-end fund manager who occasionally has private information that leads to positive risk-adjusted returns, but who also satisfies investors’ liquidity demands. A well-functioning performance measure should identify this manager as being informed. Yet fund flows force the manager to engage in liquidity-motivated trading. Depending on the timing and relative magnitude of information arrival and investor flows, the fund’s average risk-adjusted return could very well be negative even though the manager is informed. Thus, the very act of providing a liquid equity position to investors at low cost, arguably the primary service of an open-end mutual fund, can cause an informed fund manager to have negative abnormal returns.

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Like taking away the wrong Jenga piece will make the tower of blocks collapse, if we take away this important theoretical building block, the CAPM edifice crumbles. Markowitz demonstrates that the market portfolio is no longer “efficient,” which means that other portfolios offer a better expected risk-adjusted return, and the key conclusions and applications of the model are no longer valid (an interesting conclusion given the popularity of index funds). At the Q Group conferences, which bring together academics and investment professionals, we’ve had the chance on a few occasions to hear Markowitz and Sharpe debate these and related issues.

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined
by Lasse Heje Pedersen
Published 12 Apr 2015

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.

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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.

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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.

The Little Book of Hedge Funds
by Anthony Scaramucci
Published 30 Apr 2012

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.

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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.

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—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.

Unconventional Success: A Fundamental Approach to Personal Investment
by David F. Swensen
Published 8 Aug 2005

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.

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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.

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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 and Jesse Downing
Published 19 Feb 2016

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.

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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.

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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.

The New Science of Asset Allocation: Risk Management in a Multi-Asset World
by Thomas Schneeweis , Garry B. Crowder and Hossein Kazemi
Published 8 Mar 2010

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.

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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.

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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?

The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns
by John C. Bogle
Published 1 Jan 2007

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.

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(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.

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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.

Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio
by Victor A. Canto
Published 2 Jan 2005

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.

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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.

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CAPM Beta Jensen’s Alpha T-Statistics Sharpe Ratio Small Cap 1 5.64% 2.07 0.65 Large Cap 1 0.00% Growth 1.06 –1.46% 1.87 0.43 Value 0.93 0.81% 1.67 0.60 International 0.62 –1.27% 0.04 0.29 0.53 Strategic Asset Allocation Based On… Period Sharpe Ratio 0.66 0.03% 24.09 0.72 Yearly Sharpe Ratio 0.52 0.02% 24.58 0.63 Market Weights 0.54 0.1% 27 0.57 Comparing the Historical- and Market-Based Allocations As I pointed out in Chapter 1, “In Search of the Upside,” financial economics developments over the past three decades provide us with the necessary tools to develop risk-adjusted returns in a rigorous and systematic way. Arguably, systematic risk is the more important risk measure for investors who are considering adding an asset class to a diversified portfolio. According to the capital asset pricing model (CAPM), the only sort of risk priced (that is, risk requiring a higher rate-of-return) is systematic risk, which is correlated with the market.

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money
by Steven Drobny
Published 18 Mar 2010

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.

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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.

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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
Published 13 Sep 2015

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.

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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.

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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.

Financial Market Meltdown: Everything You Need to Know to Understand and Survive the Global Credit Crisis
by Kevin Mellyn
Published 30 Sep 2009

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.

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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.

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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.

All About Asset Allocation, Second Edition
by Richard Ferri
Published 11 Jul 2010

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.

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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.

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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.

Mastering the Market Cycle: Getting the Odds on Your Side
by Howard Marks
Published 30 Sep 2018

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.

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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.

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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. (Market efficiency is another essential topic, but I won’t go into it further—see chapter 2 of The Most Important Thing, as well as the latter half of “Getting Lucky” from January 2014.) ∾ The key thing to note is that fluctuations in attitudes toward risk can cause exceptions to the principles described here.

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors
by Wesley R. Gray and Tobias E. Carlisle
Published 29 Nov 2012

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.

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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.”

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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.

Beyond the 4% Rule: The Science of Retirement Portfolios That Last a Lifetime
by Abraham Okusanya
Published 5 Mar 2018

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.

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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.

Fortune's Formula: The Untold Story of the Scientific Betting System That Beat the Casinos and Wall Street
by William Poundstone
Published 18 Sep 2006

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.”

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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.

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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.

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing
by Vijay Singal
Published 15 Jun 2004

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.

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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.

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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
Published 1 Nov 2006

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.

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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.

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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.

Advances in Financial Machine Learning
by Marcos Lopez de Prado
Published 2 Feb 2018

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.

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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.

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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.”

Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market
by Steven Drobny
Published 31 Mar 2006

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.

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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.

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The NASDAQ was putting up 50 to 100 percent years and a lot of people’s return expectations had just exploded. We were lucky to have a number of experienced investors who had seen multiple cycles.They understood that delivering a 25 percent return over multiple years was actually a very competitive return. We tell our investors,“We’re here to give you a competitive risk-adjusted return, whether commodities are going up or going down.” Our portfolio doesn’t correlate on a daily, weekly, or monthly basis to either equity indexes or commodity indexes because we’re both long and short in both commodities and equities. We want to be judged by the absolute return, not anything relative. 

Finding Alphas: A Quantitative Approach to Building Trading Strategies
by Igor Tulchinsky
Published 30 Sep 2019

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.

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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.

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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.

The Power of Passive Investing: More Wealth With Less Work
by Richard A. Ferri
Published 4 Nov 2010

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.

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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.

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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.

The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing
by Michael J. Mauboussin
Published 14 Jul 2012

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).

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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.

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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 and Barry Schachter
Published 30 Jun 2007

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.

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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.

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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). 

Why Aren't They Shouting?: A Banker’s Tale of Change, Computers and Perpetual Crisis
by Kevin Rodgers
Published 13 Jul 2016

We should ‘go proprietary’, in other words. To be fair, Roz and the other Credit Risk people realised that this was a poor idea. The arguments that went back and forth between us traders and Credit Risk were made more complicated by what the bank’s market risk system said about our business. This system’s methodology, called ‘risk-adjusted return on capital’ or RAROC, an invention of our, by this time, ex-CEO (Charlie Sanford – he of the economy plane ticket) gave very different answers. RAROC (pronounced ‘rare rock’) was a revolutionary idea back in the late 1970s when it was first thought up. These days, variations of it underpin the risk management approach of virtually every bank on earth.

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range trade A type of derivative that pays a fixed amount to the buyer if the price of a specified financial product remains with a certain range for the lifetime of the deal; a form of exotic option. RAPID Revolutionary API Development: Deutsche Bank’s FX department’s modern-day system to price and risk-manage certain flows in spot FX. RAROC Risk-adjusted return on capital: a measurement framework for risk and return in Bankers Trust that compared potential gain to maximum potential loss (a forerunner of VaR). relative value trading Closely related to arbitrage but, instead of being certain of a profit, transacting in situations where a profit is merely statistically very likely.

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A Ackermann, Josef, 226 Admati, Anat, 211 ‘agency’ model, 7 agency-like risk, 119, 127 aggressive prices, 14–15 AIG, 90, 174 algorithms, 58, 71, 72, 73, 76, 77, 78, 79, 192–4, 195 Alpari, 83 alternative assets, 146–7 American Home Mortgage Investment Corporation, 89 amputation analogy, 128, 129 analytic formula, 106 Apple, 234 Application Program Interfaces (APIs), 59–60, 66, 71, 78 arbitrage, 31–2, 42, 44, 54, 70, 72, 91, 133, 142, 152 Asia, 3, 37–8, 56, 58, 106, 114, 117–18, 135–7, 146, 228 Financial Crisis (1997), xi, 114, 135–7, 139, 146, 228 Asian options, 106 assets, 6, 89, 92–5, 97, 104, 108, 112–14, 116, 117, 123, 136, 146, 147, 149, 150, 154–7, 160, 166–74, 203, 211, 223, 228, 233 alternative, 146–7 risk-weighted assets (RWA), 124–31, 134, 166, 208, 211 Atom Bank, 233 atomic bomb, see nuclear bomb Australia, 30, 65 dollars, 28, 75 Autobahn, 48–51, 53, 64, 68, 69–70, 78, 225 Automated Risk Manager (ARM), 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 79, 101, 129, 169 B back testing, 149 BaFin, 200, 202 bandwidth, 71 ‘banging the close’, 190 Bank for International Settlements (BIS), 73, 77, 80, 208 Bank of America, 174 Bank of England, 32, 173, 182, 233 bank runs, 89 bank tax, 216 Bankers Trust, xi–xii, 3–6, 24, 29–37, 40, 44, 46, 48, 65, 68, 103–14, 116–35, 137–143, 149, 166, 168, 169 carry trade, 108–10 Deutsche Bank takeover, 29–30, 34–5, 37, 46, 48, 166, 168 Emerging Markets, xi, 29–30, 116–17, 130–1, 143 Engine, 127–31, 135, 139, 157, 167, 169 exotic derivatives, 105–11, 127 Gibson Greetings suing, 109 Monte Carlo pricing, 111–14, 150 Procter and Gamble suing, 109 quantitative analysis, 103–8, 110–14, 126, 150 Risk Management, 122–31 risk-adjusted return on capital (RAROC), 126–7, 131 rouble-denominated bonds (GKOs), 118–32, 134, 138–43, 149, 152, 158, 159, 173 Russian Financial Crisis (1998), 115–16, 124, 137–143 Spreadsheet Solutions Framework (SSF), 111, 121, 138, 153 value at risk (VaR), 127–31, 135, 139, 169 Barcelona, Spain 87–9, 151, 159, 171, 172 Barclays, 63–6, 67, 68, 167, 186–7, 197 barrier products, 104, 107, 108 BARX, 64, 65 Basel Accords, 124, 125, 130, 166, 207–9, 211, 217, 231 basis points, 12 basis risk, 152 Bear Stearns, 88–9, 90, 172, 230 Bentham, Jeremy, 199 Berlin Wall, 204 Bernanke, Ben, 90 bid-offer spreads, see spread ‘Big Are Getting Bigger, The’, 41, 145 Big Bang (1986), 201 big figure, 13 binomial tree, 112–14 Bitcoin, 234 Black Monday (1987), 108, 138, 204, 207 Black–Scholes formula, 94–5, 97, 99, 105, 128, 219 Black, Fischer, 94–5, 97, 134, 135 BlackBerries, 169 Blackpool, England, 33 Blair, Tony, 120 Bloomberg, 9, 22–3, 142, 187, 189, 191, 197 Bohr, Niels, 43 bonds, xiv, 8, 23, 28, 33, 38, 89, 108–10, 118–43, 146–7, 151, 154–64, 170–2, 174, 192, 197, 211, 224–5 carry trade, 108–10 collateralised debt obligations (CDOs), xiv, 154–64, 170–2, 174, 192, 224–5 credit default swaps (CDSs), 151 mortgage-backed, xiv, 197 rouble-denominated (GKOs), 118–32, 134, 138–43, 149, 152, 158, 159, 173 total return swaps (TRSs), 119–20, 136, 152 US government, 119, 129, 133, 147 bonuses, 5, 117, 161, 210, 216 boxes and arrows, 159 Brazil, 167, 231 Breuer, Rolf, 42 Bristol, England, x British Bankers Association (BBA), 182, 187 British pound, 9, 13, 28, 53, 184 Bruno, Philip, 234 BTAnalytics, 107, 108, 112, 134, 150 Businessweek, 170 C Cable, 9, 13, 28 California, United States, 234 calls, 19–21, 23, 27, 51, 53, 98, 99 Cambios, 21, 123 Cambridge University, 165 Canada, 88, 172 dollars, 28 Cannes, France, 35, 41, 44, 46, 55, 64, 65, 77 carry trade, 108–10 Case–Schiller index, 163, 170 cash settlement, 109 ‘CDO-squared’, 161 central banking, 79, 102–3, 187, 193, 220 Central Counterparties (CCPs), 209, 213–15, 229 chain of command, 224–6 Chase Manhattan, 51, 168, 169 Chemical Bank, 168, 169 Chicago Mercantile Exchange (CME), 193, 209 China, 78, 149, 229 Chopin, Frédéric, 61 circuit breakers, 80 Citigroup, 23, 27, 36–7, 47, 51, 68, 167, 216, 226, 229 Clackatron, 32–3, 54, 91 Cold War, 204 collateralised debt obligations (CDOs), xiv, 154–64, 170–2, 174, 192, 224–5 multi-sector CDOs, 155 risk 154, 156, 158–63 sub-prime mortgages, 159–60, 170–2 synthetic, 163–4 tranches, 154–9, 161, 174, 208 colocation, 71 colonial currencies, 28 commodities, viii, xii, 144–50, 153–4, 157, 174, 223, 231–2 commodities indexes, 147–50, 153–4, 157, 163 commoditisation, 110–11 Commodity Futures Trading Commission (CFTC), 202 commodity futures, 92 Communism, 199 complex risk, 159, 222 complexity, 90, 100, 103–14, 121, 127, 130, 134, 136, 143, 157, 196, 200–1, 218–24, 225, 228, 229 Comprehensive Capital Analysis and Review (CCAR), 212 computers, ix–x, xii–xv, 6, 21–8, 32–55, 56–84, 89–90, 99–114, 116, 121, 127, 130, 134, 136, 138, 143, 144, 147, 150, 157, 160, 161, 164, 167, 169–70, 172, 190–200, 209, 213, 218–24, 225, 228, 231–5 algorithms, 58, 71, 72, 73, 76, 77, 78, 79, 192–4, 195 Autobahn, 48–51, 53, 64, 68, 69–70, 78, 225 Application Program Interfaces (APIs), 59–60, 66, 71, 78 Automated Risk Manager (ARM), 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 79, 101, 129, 169 BARX, 64, 65 BTAnalytics, 107, 108, 112, 134, 150 Clackatron, 32–3, 54, 91 complexity, 90, 100, 103–14, 121, 127, 130, 134, 136, 143, 157, 196, 200–1, 218–24, 225, 228, 229 DBAnalytics, 150, 153 decimalisation, 63–6, 67, 73, 77 decentralisation, 232–5 efficiency improvements, 169–70 Electronic Broking Services (EBS), 24–8, 31, 32–3, 42, 45, 46, 48, 50, 59–60, 63, 69–70, 71, 72, 73, 77, 83, 188 Electronic Communication Networks (ECNs), 59, 61, 66, 193 Engine, 127–31, 135, 139, 157, 167, 169 FX-fixing scandal (2013), 190–2 OPTICS, 101, 103, 131, 153, 170 options trade automation, 27, 33, 38–47 Piranha, 48 prime brokerage (PB), 61–3, 66, 120, 209 regulation, 209, 213 Revolutionary Application Program Interface Development (RAPID), 56–9, 77, 101 Reuters Dealing machines, 23, 25–8, 31, 32, 50, 59, 73 rogue systems, 79–80 screen scraping, 32–3, 50, 59 simplification, 231–2 spoofing, 192–3 spot trade automation, 23–8, 31–3, 42, 48–55, 56–84, 111, 199, 201 spreads, 80–3 concentration risk, 213 Conservative Party (UK), 201 correlation, 14, 79, 129–30, 142, 156–8, 160, 163 cost–benefit analysis, 41 Costello, Elvis, 140 counterparty credit risk, 141–2, 172, 201, 209 Countrywide, 90 credit default swaps (CDSs), xiv, 150–3, 157, 158, 164, 225 credit risk, 7, 8, 62–3, 123, 125–6, 130, 141, 151, 209 counterparty, 141–2, 172, 201, 202–3, 209 Credit Suisse, 140, 198 credit trading, 146–7, 150–63, 166, 175, 224 crowd-funding, 235 currency pairs, 9, 12, 14, 46, 53, 75, 80 Currenex, 59 CVIX, 74 D D:Ream, 120, 121 daisy-chaining, xiv DBAnalytics, 150, 153 decentralisation, 232–5 decimalisation, 63–6, 67, 73, 77 delta hedging, 97 ‘dentists, the’, 82–3 deposits, 124 derivatives, xi, xii, xiv, 44–5, 87, 91–114, 118, 119, 121, 127, 132, 134, 138–43, 144–5, 150–74, 182–6, 189, 196, 209, 210, 214, 218–25, 231 Asian options, 106 barrier products, 104, 107, 108 carry trade, 108–10 collateralised debt obligations (CDOs), xiv, 154–64, 170–2, 174, 192, 224–5 commoditisation, 110–11 credit default swaps (CDSs), xiv, 150–3, 157, 158, 164, 225 delta hedging, 97 democratisation, 108, 219 double knockouts, 107 exotics, 105–11, 127 foreign exchange, 10, 13, 20, 23, 27, 33–4, 38, 43–7, 49, 77, 95–114, 222–3 forward rate agreements (FRAs), 182 gamma, 97–8, 100 Greeks, 98, 99, 101, 105, 107, 110, 111, 112, 114, 127, 129, 150, 157, 158, 182, 219 interest rate derivatives, 8, 92, 104, 132, 182–6, 214 lattice methods, 112, 150, 157 lookbacks, 107 over-the-counter (OTC) market, 96, 209 power options, 107 pricing, 91–5, 107, 111–14, 128, 133, 150 range trades, 107, 108 rho, 98 risk-weighted assets (RWA), 124–31, 134, 166, 208, 211 spoofing, 99, 192–3 strike price, 94, 95, 104, 113, 218 swaps, 8, 92, 119–20, 121, 125, 126, 132, 134, 136, 141, 148, 149, 152, 173 theta, 98, 140 time decay, 98 total return swaps (TRSs), 119–20, 136, 152 tree approach, 112–14 vega, 98 volatility, 94, 98, 128–9 weather, 144–5, 146 zero coupon bonds, 118–31, 134, 138–43, 149, 152, 158, 173 desk real estate, 25 Deutsche Bank, vii–x, xii–xiii, 12, 21, 23, 28, 29–30, 33–55, 56–80, 84, 87, 101, 122, 144–74, 179–80, 187, 190, 195–6, 199, 200, 201, 209–10, 212–13, 216, 222, 224–6, 231 Autobahn, 48–51, 53, 64, 68, 69–70, 78, 225 Automated Risk Manager (ARM), 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 79, 101, 129, 169 BaFin audits, 200, 202 Bankers Trust acquisition, 29–30, 34–5, 37, 46, 48, 166, 168 Barcelona Conference, 87–9, 151, 159, 171, 172 Commodities, 144–50, 153–4, 157, 165, 175, 223, 231–2 Complex Risk, 159, 222 Compliance, 195, 196 Corporate and Investment Banking division, 166 Credit Trading, 146–7, 150–63, 166, 175, 224 CVIX, 74 DBAnalytics, 150, 153 e-trading, 55, 57, 67–73, 75, 84, 122 Foreign Exchange, vii–x, xii–xiii, 12, 21, 23, 28, 29–30, 33–55, 56–80, 84, 87, 101, 122, 165, 175, 210, 212–13, 224, 225 FX-fixing scandal (2013), 187, 190 Global Currencies and Commodities, 165–6 Great Financial Crisis (2007–8), 174–5 Liquid Commodity Index (DBLCI), 148, 149 market share, 36, 41, 48, 51–2, 54, 55, 56, 57, 64, 66–8, 70, 77 MortgageIT acquisition, 160 offsites, 35–41, 46, 55, 64, 65, 77 options trade automation, 27, 33, 38–47 Plankton Strategy, 38, 40, 55, 58 production credits, 49, 50 regulation, 200, 202, 209–10, 212–13 Revolutionary Application Program Interface Development (RAPID), 56–9, 77, 101 Risk Management, 175, 206–7, 209, 226 risk-weighted assets (RWA), 124–31, 134, 166 simplification, 231–2 small deals team, 52, 55 spot trade automation, 48–55, 56–77 video messaging system, 71 weather derivatives, 144–5, 146 Deutschmark, 9, 96, 97, 99, 100, 104 Dewar, Sally, 197 Dickens, Charles, 162 Digital Reasoning Systems Inc., 197–8 dividends, 91, 147, 212 Dodd–Frank Act (2010), 209, 230 Dostoyevsky, Fyodor, 30 dot-com boom (1997–2000), 37, 146, 207 double knockouts, 107 Dow Jones, 79, 138, 147 Dresdner Bank, 20, 48 drive-bys, 17, 20, 68, 132 E e-trading, 55, 57, 67–73, 75, 84, 122 efficiency improvements, 169–70 efficient market theory, 203 Electronic Broking Services (EBS), 24–8, 31, 32–3, 42, 45, 46, 48, 50, 59–60, 63, 69–70, 71, 72, 73, 77, 83, 188 Electronic Communication Networks (ECNs), 59, 61, 66, 193 elephant deals, 38 email, 169, 191, 195, 197, 199 emerging markets, xi, 6, 29–30, 116–43, 147, 228 Empire State Building, New York, 130 ‘Engine’, 127–31, 135, 139, 157, 167, 169 engineering, x–xi, 10 Enron, 156 equities, viii, 23, 50, 66, 71, 74, 80, 89, 95, 146, 193, 207, 211, 235 circuit breakers, 80 colocation, 71 sales-traders, 50, 78 euro, 22, 28, 31, 37, 53, 69, 80, 81–3, 189 Euromoney, 36, 41, 47, 51–2, 54, 55, 56, 57, 64, 66–8, 70, 77, 81, 233 Europe, Middle Eastern and African (EMEA) markets, 117–18 European Union (EU), 102, 196, 201, 203, 207, 210, 212, 230 Exchange Rate Mechanism (ERM), xi, 22, 102–3, 106, 123, 130, 136, 207, 224 Liikanen Report (2012), 230 Maastricht Treaty (1992), 102 Market Infrastructure Regulation (EMIR), 209 ‘Every Day I Write the Book’ (Elvis Costello), 140 Excel, 107, 221 exchange rates, 9, 11–13, 14, 17, 32, 100 exotic derivatives, 105–11, 127 F ‘F9 monkeys’, 221 Fannie Mae, 90 Federal Reserve, xii, 90, 109, 143, 187, 202–6, 212 Federal Reserve Bank of Chicago, 193 fibre-optic cables, 77, 84 FIFA (Fédération Internationale de Football Association), 167 Financial Conduct Authority (FCA), 190 Financial Services Act (2013), 210 Financial Stability Board (FSB), 212, 214, 216 Financial Times, 116, 187 financial transactions tax, 216 fines, 181, 190, 196 First Chicago, 168 Fitch, 155 fixings, 181, 187–92, 198 Flannery, Mark, 215 Flash Crash (2010), 79–80, 193 Florence, Italy, 78 Florida, United States, 116 football, 10–11, 43, 83, 117, 167, 195 Forbes, 234 foreign direct investment (FDI), 136 foreign exchange (FX), viii, xi, xii, xiv, 3–28, 29–55, 56–84, 89, 90–1, 95–114, 115–44, 145, 158–9, 165, 174, 175, 180, 181–94, 196–7, 210, 213, 222–3, 224, 225, 233 aggressive prices, 14–15 Application Program Interfaces (APIs), 59–60, 66, 71, 78 Autobahn, 48–51, 53, 64, 68, 69–70, 78, 225 Automated Risk Manager (ARM), 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 79, 101, 129, 169 banging the close, 190 BARX, 64, 65 bidirectional flows, 69 calls, 19–21, 23, 27, 51, 53, 98, 99 carry trade, 108–10 Clackatron, 32–3, 54, 91 colocation, 71 complex risk, 159, 222 corporations, 9, 36, 61, 76, 96, 109–10 costs, 42 currency pairs, 9, 12, 14, 46, 53, 75, 80 decimalisation, 63–6, 67, 73, 77 drive-bys, 17, 20, 68, 132 Electronic Broking Services (EBS), 24–8, 31, 32–3, 42, 45, 46, 48, 50, 59–60, 63, 69–70, 71, 72, 73, 77, 83, 188 Electronic Communication Networks (ECNs), 59, 61, 66, 193 elephant deals, 38 emerging markets, xi, 6, 29–30, 116–43 Engine, 127–31, 135, 139, 157, 167, 169 fixings, 181, 187–92, 198 forwards trades, 10, 52, 77, 92, 121 hedge funds, 9, 29, 36, 57, 61, 66, 69–74, 77, 81, 120, 123, 131–5, 141–3 hedging, 97, 108, 117–18, 190, 225 high frequency trading (HFT), 57, 63, 73, 74, 75, 76, 77, 80, 84, 180, 194 LIBOR scandal (2012), 181–7, 188, 189, 190, 197, 198 liquidity, 18–19, 21, 27, 80–1, 83, 233 making rates, 13–21, 27, 38–9, 50, 96 market makers, 18–19, 21, 83 market shares, 41, 48, 51–2, 54, 55, 65, 66–8, 70, 77, 81, 169 ‘mine-and-yours’, 15–16, 18, 19, 192 Monte Carlo pricing, 111–14 off system trades, 111 OPTICS, 101, 103, 131, 153, 170 options trade, 10, 13, 20, 23, 27, 33–4, 38, 43–7, 49, 77, 95–114, 222–3 over-the-counter (OTC) market, 96, 209 passive v. active strategies, 17–19 pay, 43 pension funds, 9, 61, 76, 96 pips, 13, 18, 41, 65, 73, 77 Piranha, 48 pre-deal services, 8 prime brokerage (PB), 61–3, 66, 120, 209 production credits, 49–50 proprietary trading, 22, 31, 39, 46–7, 125 relative value trades, 72–3 retail trade, retail aggregators, 21, 61, 66, 74, 75, 79, 82–3 Reuters Dealing machines, 23, 25–8, 31, 32, 50, 59, 73 Revolutionary Application Program Interface Development (RAPID), 56–9, 77, 101 risk, 15, 16, 19, 20–1, 24, 29, 31, 38, 39, 40, 44–5, 49, 51, 53, 62, 77, 95, 96, 98, 99, 101–8, 110–14, 121–31, 135, 192 risk management systems, 24, 40, 44–5, 53–5, 56–9, 64, 67, 70–1, 72, 73, 76, 77, 79, 99, 101–8, 110–14, 121–31, 135 rogue systems, 79–80 salespeople, 8–9, 11, 13–15, 20, 24, 28, 29, 33, 35, 37, 46, 47–52, 68, 96, 120 settlements, 5, 24, 40, 44, 51, 61, 101, 209 screen scraping, 32–3, 50, 59 skewed prices, 18, 19, 73, 96, 98 slippage, 188–9 speculation, 21, 31, 102–14 spot, 3–28, 33–4, 42–4, 46, 48–55, 67, 76, 77, 91, 96, 97, 98–9, 108, 111, 118, 122, 159, 165, 169, 188, 189, 199, 201, 222 spread, 12, 14–15, 16, 18, 19, 31, 41–2, 44, 45, 53, 55, 61, 64, 68, 69, 75, 80, 96, 225 traders, 3–28, 33, 35, 38, 41, 43, 46, 50, 52–4, 67, 73, 76, 78, 96, 99, 122, 189 Triangle arbitrage, 31–2, 42, 54, 91, 122 two-way pricing, 11–21, 23, 99 volatility, 14, 26, 46–7, 74–5, 80, 98, 137 wallet, 40 window, 118, 137, 188 WM/R, 187–8 zero coupon bonds, 118–32, 134, 138–43, 149, 152, 158, 159, 173 forward rate agreements (FRAs), 182 forwards trades, 10, 52, 77, 92, 121 Four Seasons, The, 3–5, 9 France, 6–7, 35, 37, 41, 46, 55, 64, 65, 77, 102, 140, 159, 207 Freddie Mac, 90 free market economics, 202–6 FTSE 100, 147 Fuld, Dick, 226 FX All, 59, 78 FX-fixing scandal (2013), 181, 187–92, 198 FXCM, 82–3 G G20 nations, 209, 212, 216 gambling, 31, 102–14 gamma, 97–8, 100 Gaussian copula, 158 geekiness, x, 10, 43, 223 geopolitics, 229 Germany, vii–x, xii, xiii, 16, 28, 36, 44, 54, 75, 144–5, 159, 167, 180, 200, 204 mark, 9, 96, 97, 99, 100, 104 Gibson Greetings, 109 GKOs (rouble-denominated bonds), 118–32, 134, 138–43, 149, 152, 158, 159, 173 Glass–Steagall Act (1933), 168, 201, 230 globally systemically important banks (G-SIBs), 216 Goldman Sachs, 23, 146, 198, 216, 230 Commodity Index (GSCI), 148 Google, 78, 234 GQ, 141 Gramm–Leach–Bliley Act (1999), 168, 201 Great Depression (1929–39), 168 Great Financial Crisis (2007–8), xiii, xiv–xv, 74–5, 87–90, 114, 124, 163, 172–5, 179–80, 196, 204–6, 207, 217, 219, 220, 223, 227–8 Greece, 82, 92, 181 Greeks, 98, 99, 101, 105, 107, 110, 111, 112, 114, 127, 129, 150, 157, 158, 182, 219 Greenspan, Alan, 202–6 GSA, 233 Gulf War (1990–1), xi, 22, 95 Gulliver, Stuart, 231 H ‘haircuts’, 132 Haldane, Andrew, 173 hand signals, 16 Hang Seng, 137 Harrison, William, 168 Harrow School, London, 149 Harvard University, 166 La Haye Sainte, Belgium, 10 hedge funds, xi, 9, 29, 36, 57, 61, 66, 69–74, 77, 81, 97, 114, 116, 122, 123, 131–5, 141–2, 164, 171, 172, 201, 229, 233 hedging, 94, 95, 97, 108, 117–18, 144, 145–6, 157, 190, 225, 226 delta hedging, 97 Hellwig, Martin, 211 Henry, John, 28 Herrhausen, Alfred, 226 high frequency trading (HFT), 57, 63, 73, 74, 75, 76, 77, 80, 84, 180, 194 Hodgkin, Howard, 195 Holder, Noddy, 232 Hong Kong, 137 Hotspot, 59 Hounslow, London, 193 HSBC, 216, 229, 231 Hussein, Saddam, 22, 95 I ICAP, 73 Iceland, 167, 174 identity theft insurance, 231 Immendorff, Jörg, 56 indexes, 147–50, 153–4, 157, 163, 190 India, 149 Indonesia, 135, 137, 139 IndyMac, 90 initial margin, 120, 132 insurance, 93, 151, 164, 168, 172, 174, 229 Intelligent Flow Monster, 146 interest rates, viii, 7, 10, 92, 108–10, 135, 225 carry trade, 108–10 derivatives, 104, 132, 182–6, 214 futures, 182 LIBOR (London Interbank Offered Rate), 109, 181–7, 188, 189, 190, 197, 198 swaps, 8, 92, 182–4 International Monetary Fund (IMF), 132, 135, 140, 211, 214, 230 International Swap Dealers Association (ISDA), 152, 153 Internet, xii, 5, 42, 76, 78, 169, 191–2, 195, 197, 199, 228, 233–5 investment banking, 36, 87, 110, 133, 145–6, 151, 158, 165–8, 229–30 iPhone, 5, 228 Iran, 181–2 Iraq, xi, 22, 95 Israel, 29, 138 Italy, 10, 13, 22, 78, 115–16, 141 lira, 22, 102–3, 123 iTraxx, 153 J Jagger, Mick, 87 Japan, 61, 72, 79, 159 yen, 9, 14, 17, 28, 71–2, 75, 79, 80, 136, 184 JPMorgan Chase, 131, 150, 158, 163, 168, 182, 197, 216, 229, 231, 23 J.

Safe Haven: Investing for Financial Storms
by Mark Spitznagel
Published 9 Aug 2021

In their world, it sounds reasonable, and it's a comfortable story, asserted but not proved: You've got to take more risk to make higher returns; sleeping well comes at a cost. No guts, no glory. To make matters worse, academics furthered this idea by positing that investing and risk mitigation are about lowering or calibrating a portfolio's volatility relative to its average return—the risk‐adjusted return or dreaded Sharpe ratio—unwittingly at the expense of the growth rate of wealth. They thus claim an intellectually dishonest victory based on their own theoretical scoreboard. It is a solution in search of a problem, and a bad idea. (It's even a big reason for our great dilemma.) I don't really believe that most investors even have this bad idea.

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Wise people speak of shunning risk through diversification, dubbed “the only free lunch in finance,” using financial engineering terms that few even bother to understand, and spreading risk across assets in hopes of capturing some safer, mediocre average performance. No matter what it cost you, your intentions were good, and at least you evaded disaster; your risk‐adjusted returns were high—though you’re poorer. The conventional approach to risk mitigation in the investment industry is deceptive and defeatist, with so little substance and significance, and so much smoke and mirrors. It disregards economic meaning in favor instead of quantitative meaning and accepts bad results all in the name of safety from bad results.

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See also Commodity trading advisor (CTA) strategies Rhine Falls, 54–55, 57 Risk: basis, 169 diversification as dilution of, 116 diversifying, 44–45 as exposure to bad contingencies, 8 great dilemma of, 10–13 investment, 8 lowering, 162–163, 187 and math of compounding, 53–54 with Nietzsche's demon, 70 reducing costliness of, 11. See also Risk mitigation and returns, 16–17, 27 systematic, 116, 117, 120 Risk‐adjusted returns, 17 Risk aversion, 36 Risk‐free rate, 17 Risk management, Graham on, 9 Risk‐mitigated payoff profile, 77–79. See also Xs and Os Profile Risk‐mitigated portfolios, S&P 500 outperformed by, 4 Risk mitigation. See also specific strategies as being “at war with luck,” 25 CAGR raised by, 15–16 as context dependent, 127.

The Handbook of Personal Wealth Management
by Reuvid, Jonathan.
Published 30 Oct 2011

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.

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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.

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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.

The Firm
by Duff McDonald
Published 1 Jun 2014

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.

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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.

Learn Algorithmic Trading
by Sebastien Donadio
Published 7 Nov 2019

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).

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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.

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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.

Concentrated Investing
by Allen C. Benello
Published 7 Dec 2016

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.

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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.

More Money Than God: Hedge Funds and the Making of a New Elite
by Sebastian Mallaby
Published 9 Jun 2010

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.

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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.

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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.

The Upside of Inequality
by Edward Conard
Published 1 Sep 2016

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.

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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.

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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.

The Unusual Billionaires
by Saurabh Mukherjea
Published 16 Aug 2016

To further assess the robustness of these findings, I also stress-tested these results for maximum drawdown to evaluate the strength of the portfolio during periods of market volatility: First, I calculate CAGR returns for each of the sixteen portfolios and the Sensex; Next, I compute the maximum drawdown for each portfolio (defined as the maximum drop in cumulative returns from the highest peak to the lowest subsequent trough); and Finally, I calculate the risk-adjusted returns, i.e. returns in excess of the risk-free rate (assumed to be 8 per cent) divided by the maximum drawdown. The results can be summarized as follows: Each of the sixteen CCPs has outperformed the benchmark Sensex. Even a subset of the CCP, i.e. the large-cap version of the CCP has been successful in beating the Sensex on all sixteen occasions.

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Large-cap portfolio stocks: Infosys, Wipro, Cipla, Asian Paints, Tech Mahindra, HDFC Ltd, HDFC Bank and Punjab National Bank. Our ninth iteration that begins in June 2008 is also outperforming the Sensex with an alpha of 11.1 per cent. The large-cap version also beats the Sensex with an alpha of 8.7 per cent. The large-cap version on account of lower drawdown has the highest risk adjusted return at 0.37 times as compared to 0.34 times for all-cap version and 0.01 times for the Sensex (Exhibit 167). Exhibit 167: Ninth iteration summary Source: Bloomberg, Ambit Capital research. Note: *Portfolio kicks off on 30 June 2008. Excess returns have been calculated as returns in excess of risk-free rate (assumed to be 8%) divided by absolute maximum drawdown.

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This iteration gave the weakest result in terms of both the absolute performance of our Coffee Can Portfolio and the alpha generated versus the Sensex. The large-cap version continued its outperformance in this iteration as well beating both the all-cap version and the Sensex on both absolute and risk-adjusted return measures (Exhibit 176). Exhibit 176: Twelfth iteration summary Source: Bloomberg, Ambit Capital research. Note: *Portfolio kicks off on 30 June 2011. Excess returns have been calculated as returns in excess of risk-free rate (assumed to be 8%) divided by absolute maximum drawdown. Maximum drawdown is defined as the maximum drop in cumulative returns from the highest peak to the lowest subsequent trough.

The Gone Fishin' Portfolio: Get Wise, Get Wealthy...and Get on With Your Life
by Alexander Green
Published 15 Sep 2008

After all, I spend several hundred hours a year researching, recommending, and monitoring just a few dozen individual stocks. And not without some success. There are hundreds of investment letters published in the United States. In both 2006 and 2007, the independent Hulbert Financial Digest ranked the Oxford Club Communique, which I direct, among the top five investment letters in the nation for risk-adjusted returns over the previous five years. (This ranking is due, in part, to the success of my individual stock picks. However, Hulbert also monitors the performance of the Gone Fishin’ Portfolio, which has returned 17.3% annually since its inception in 2003 through 2007.) Why no individual stocks in the Gone Fishin’ Portfolio?

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A Wall Street veteran, he has over two decades’ experience as a financial writer, research analyst, investment advisor, and professional portfolio manager. Under his direction, The Oxford Club’s portfolios have beaten the Wilshire 5000 Index by a margin of more than three to one. The Oxford Club Communique, whose portfolio he directs, is ranked third in the nation for risk-adjusted returns over the past five years by the independent Hulbert Financial Digest. Mr. Green has written for several leading financial publications and has appeared on many radio and television shows, including Fox News, CNBC, and The O’Reilly Factor. He has also been profiled by Forbes, Kiplinger’s Personal Finance, Marketwatch.com, and other major media.

Damsel in Distressed: My Life in the Golden Age of Hedge Funds
by Dominique Mielle
Published 6 Sep 2021

Knievel was the stuntman who successfully launched his motorcycle 133 feet over fourteen Greyhound buses, but that would not necessarily make him a good hedge fund manager—just a good candidate for groin injury. In fact, one study shows that men tend to overtrade, thereby reducing their net risk-adjusted returns by 2.6 percent.1 Good investors do not relish risk for the sake of it. Rather, they tolerate risk. They can endure being proven wrong, and all the garden-variety unpleasantness that comes with it: being judged and mocked by your colleagues (somehow news of a losing investment always gets around faster than a winning one); being yelled at by the partners; and, generally speaking, looking like an imbecile for what feels like an extended period of time.

…

They corroborate my experience that some strategies (currency trading, for example) have greater capacity than others (distressed investing, in my case), and consequently that investors, all things being equal, should prefer deep capacity rather than niche strategies. The problem then becomes whether hedge funds can deliver outsized risk-adjusted returns in markets that are highly liquid and efficient. His answer is that they can, to the extent that they offer superior investment skills. And since above-average skills are, by definition, in limited supply as money (i.e., demand for skills) pours into the hedge fund industry, it begins funding managers whose skills “are not superior to those that are needed for index investing.”

The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated
by Gautam Baid
Published 1 Jun 2020

• A small- and mid-cap-oriented investment firm closes the fund to new investors when it begins to incur excessive impact costs because of its bigger size, which precludes it from taking meaningful positions in smaller companies. A marketing firm does not. • An investment firm passes on to clients its savings benefits in the form of reduced fees when its operating cost ratios improve with increasing assets under management. A marketing firm does not. If investment firms focus on generating the best possible risk-adjusted returns and maintain an ongoing dialogue with clients regarding their process and philosophy in a clear manner, the rest should take care of itself. Keep it simple. Simplicity drives value, and delivering value to clients ensures long-term survival and prosperity (figures 16.1a and 16.1b). (a) FIGURE 16.1 (a) Simplicity drives value, and (b) delivering value to clients ensures long-term survival and prosperity.

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As the cycle starts turning, add more to your position. Given the contrarian and long-term nature of the capital cycle approach to investing, this strategy entails firm variant perception and long holding periods. Such a disciplined and patient deep value investing approach enables investors to achieve high risk-adjusted returns when the cycle eventually turns. According to financial historian and investment strategist Edward Chancellor, capital cycle investing is more profitable than strategies based on growth or value orientation. Chancellor’s book Capital Returns: Investing Through the Capital Cycle, captures the essence of the capital cycle approach to investing.

…

Take advantage of it. We should not aim for the highest possible returns in the shortest period of time but rather we should seek above-average returns over a long period of time with the lowest possible risk. Risk management should take a higher precedence in the investment process, and risk-adjusted returns are a far superior indicator of performance than absolute returns. This is especially true during bull markets, when aggressive risk taking often is mistaken for intelligence. What’s important is the underlying process used by the fund manager or investment advisory firm and the amount of risk taken on in client portfolios to achieve those high returns.

The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk
by William J. Bernstein
Published 12 Oct 2000

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.

…

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.

I.O.U.: Why Everyone Owes Everyone and No One Can Pay
by John Lanchester
Published 14 Dec 2009

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.

…

Morgan, 152 derivatives and, 64–71, 116, 120–21, 183 Exxon deal and, 67–68, 70, 121 Glass-Steagall Act and, 64–65 JPMorgan Chase, 190, 227 Julian, Harriette, 130–31 Kahneman, Daniel, 136–42, 193 Kerviel, Jérôme, 51 Keynes, John Maynard, 49n, 136 predictions of, 213–15 risk and, 55, 145 Kindleberger, Charles P., 104 King, Mervyn, 167, 178, 206 King’s Cross station, 88–90 Kreuger, Ivar, 105 Kynaston, David, 21, 23 leaderless group challenge, 139–40 Leeson, Nick, 51–52, 54 Lehman Brothers, 190, 204, 225 collapse of, 39, 75, 78 Leland, Hayne, 151 leverage: of banks, 35–36, 41–42, 70, 190 debt and, 60–61, 181, 190 deleveraging and, 41–42, 83 derivatives and, 51, 54–55 housing and, 60–61, 83, 95, 97 regulation and, 181, 186, 188, 190 risk and, 35–36 Li, David X., 115–17, 157–58 liabilities, 31–35 in balance sheets, 25–28, 31–34, 37 of banks, 25, 32–35, 37, 41, 204 of individuals, 27–28, 35 leverage and, 35, 41, 60 libel law, 93 life expectancies, 17, 213 liquidity, 212 housing and, 28–29, 90, 96–97 investments and, 60–61 Lloyds TSB, 36, 38–40 loans, lending, 74–76, 108–9 in balance sheets, 27, 30, 34 of banks, 22, 24, 27, 33–36, 41–42, 58–60, 67, 69–70, 74, 83–84, 91–94, 102, 117, 127, 129–30, 143, 146, 165, 187, 216–17, 229 credit and, 209, 216–17 derivatives and, 50–51, 55, 66–75, 80, 121–22 Exxon deal and, 67–68 interest rates and, 59–60, 66, 74, 102, 108, 145, 172–73 paying the bill and, 220–21 predatory, 122, 127–32 risk and, 66–67, 69–72, 74–75, 80, 95, 117, 145, 174 securitization in, 69, 74 see also mortgages London, 53, 84 housing in, 88–90 see also City of London Long-Term Capital Management (LTCM): collapse of, 142, 162, 164–65, 230–31 derivatives and, 54–56, 80 loss aversion, 137 Lovelock, James, 231 Lowenstein, Roger, 161 Macmillan, Harold, 216 Madoff, Bernard, 105, 171, 191–92, 195 Mailer, Norman, 172 Manias, Panics, and Crashes (Kindleberger), 104 manufacturing, 4, 13, 58, 109, 229 and financial vs. industrial interests, 197, 199 Marxist analysis of, 15–16 stocks and, 148–49 market discipline, 183–84 Markopolos, Harry, 192 Markowitz, Harry, 147–49, 158 mark to market, 42, 105–6 Marx, Karl, 15–16 Maryland, housing in, 125–31 Masters, Blythe, 68, 121 mathematics, 5, 231 derivatives and, 47–48, 52–54, 115–17, 166 risk and, 46, 55–56, 74, 133, 136, 146–50, 154, 158, 160–67, 202 of share pricing, 147–48 Meriwether, John, 54 Merrill Lynch, 39, 77, 120, 190, 227 Merton, Robert, 54–55 microeconomics, 137 Minsky, Hyman, 104 Monetary Policy Committee, 178–79 money: assumptions based on primacy of, 202–4 cost of, 102–3 flows of, 7–9, 26 inconceivable amounts of, 8 Money Machine, The (Coggan), 25 Moody’s Investors Service, 62, 70, 114, 119, 208, 210 Morgan, John Pierpont, 20, 64 Morgan Stanley, 40, 64, 227 Morris, Charles, 42 mortgages, 38–40, 83–87, 89–95, 97–102, 110–32 in balance sheets, 27–28 balloon payments on, 100 and buy-to-let properties, 177 conforming, 112, 124 credit ratings and, 123–24, 126 of Cutter family, 126–27 defaults on, 159–60, 163, 165, 229 derivatives and, 38, 57–58, 75–76, 112–22, 132, 157–60, 172, 210–12 discriminatory practices and, 99–101, 127 durations of, 95 endowment, 86–87, 89–90, 146 Iceland’s economic crisis and, 10–11 interest and, 8, 58, 86, 89, 91–92, 95, 100, 102, 108, 110, 112–14, 122, 128, 145–46, 174, 176, 212 “liar,” 126, 132 “no doc,” 132 No Income, No Job or Assets (NINJA), 126 piggyback, 132 predatory lending and, 122, 127–32 regulation and, 99–100, 185 risk and, 145, 158–60, 163–65 sizes of, 92–94 subprime, 38, 75, 83, 100, 113–19, 122–25, 127, 132, 157–59, 165, 202, 210 see also houses, housing, home ownership Nasdaq, 104 nationalization, 24, 39–40, 228–30 New York Times, The, 77, 98, 208 “Night in Tunisia, A,” 45 Nikkei 225, 51–52, 54 9/11 terrorist attacks, 2, 107 Northern Rock, 5, 39, 94, 194, 206 Obama, Barack, 77, 205 regulation and, 188, 190, 223–24 Objectivism, 142–43, 173 oil, 3–4, 107–8, 148–49 “On Default Correlation” (Li), 116 options, 50–52, 151, 174, 184 how they work, 46–47, 50–51 Osaka exchange, 54 Pacioli, Luca, 26 panic of 1893, 64 panic of 1907, 20, 64 Parker, Charlie, 45 Paulos, John Allen, 8 pensions, 76–77, 165, 204 in balance sheets, 27–28, 31 Phillips, Julia, 199 politics, politicians, 5–6, 19–21, 23–25, 81, 118–19, 169–70, 176–78, 217–26, 228–32 AIG bailout and, 76–78 banks and, 25, 33, 43, 182, 186, 195, 202, 207, 211, 217, 228–31 bonds and, 29–30, 61–62, 103, 109, 118, 144, 176–77, 208–9 derivatives and, 57, 183–86 financial industry’s ascent and, 19–20 free-market capitalism and, 14–15, 19, 21, 23–24 housing and, 87–89, 91, 96–101, 177–78 Iceland’s economic crisis and, 9–10, 12, 24, 223 interest rates and, 102–3, 107–8, 172–80, 221 paying the bill and, 219–23 regulation and, 15, 19–21, 24, 169, 180–92, 195, 199, 201, 223–26 risk and, 142–43, 164–66, 174, 184 Ponzi, Charles, 105 Ponzi schemes, 191–92 poor, poverty, 3–4, 13, 21, 82, 179, 196 housing and, 100, 113, 118, 121–23, 126–27, 130–31, 163 pork bellies, 48–49 portfolio insurance, 151–52, 162 “Portfolio Selection” (Markowitz), 147 Posner, Richard A., 120, 174, 182, 193 Powell, Anthony, 62 price, prices, 105–11, 203 and banking-and-credit crisis, 216–18, 220 bonds and, 61, 63, 102–3, 108–10, 144 derivatives and, 38, 46–52, 54, 56, 75, 158–59, 166 of houses, 5, 28–29, 37–38, 61, 71, 86–91, 101, 109–11, 113, 115, 125, 157, 160, 164–66, 173–76, 194, 208 of oil, 3–4, 107–8, 148–49 risk and, 145–50, 158–59, 164–66 of stocks, 102, 105–6, 109–10, 147–51, 158, 174 of toxic assets, 37–38, 42 volatility of, 47–48, 148–50 “Pricing of Options and Corporate Liabilities, The” (Black and Scholes), 45, 47–48, 147 probabilities, 46, 55, 74, 115, 141, 145, 153–55, 160–63 profits, 20, 28, 104–6, 110, 192, 226–28, 230 banks and, 33, 35, 67, 78, 227–28 and benefits of debt, 59–60 derivatives and, 50, 54, 57, 63, 65, 106, 114, 121–22 Enron and, 105–6 regulation and, 204, 226 risk and, 150, 226 Protection of Homeowners in Foreclosure Act, 131 “Quiet Coup, The” (Johnson), 19–20, 185–86 Ragtime (Doctorow), 64 Rand, Ayn, 142–43, 173 Reagan, Ronald, 14, 19–20, 24, 142, 185 recessions, 42, 89, 94, 142, 171, 175, 219 regulation, deregulation, 15, 19–22, 24, 169, 180–202 banking and, 21, 33, 180–91, 194–96, 199–200, 202, 204–5, 208, 211, 223–27 bond ratings and, 208–9 derivatives and, 68, 70, 73, 153, 183–86, 200–201 framework and regime of, 189–92 market discipline and, 183–84 mortgages and, 99–100, 185 proposals for, 223–26 risk and, 143, 153, 164, 187–88, 191, 195, 202, 204–5, 212, 224, 226 in U.K., 21–22, 105n, 180–82, 194–96, 199–201, 218 in U.S., 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.

Money Mavericks: Confessions of a Hedge Fund Manager
by Lars Kroijer
Published 26 Jul 2010

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.

…

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.

Smart Money: How High-Stakes Financial Innovation Is Reshaping Our WorldÑFor the Better
by Andrew Palmer
Published 13 Apr 2015

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.

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Flowers, 69, 81 Japan, banking crisis in, 75 Japan, financial innovation in, 27, 29, 39–40 Jha, Saumitra, 27 Jiménez-Martín, Sergi, 73 Job creation, young small firms and, 147–148 Joint-stock firms, 23 JPMorgan, 77, 169 Jump-to-default risk, 238 Käärmann, Kristo, 190 Kabbage, 218 Kahneman, Daniel, 47, 137 Kanjorski, Paul, 145 Kauffman Foundation, 158 Kennedy, John F., 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.

Traders, Guns & Money: Knowns and Unknowns in the Dazzling World of Derivatives
by Satyajit Das
Published 15 Nov 2006

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.

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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.

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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 

A Man for All Markets
by Edward O. Thorp
Published 15 Nov 2016

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.

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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.

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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 Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds
by Maneet Ahuja , Myron Scholes and Mohamed El-Erian
Published 29 May 2012

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.”

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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.

Stolen: How to Save the World From Financialisation
by Grace Blakeley
Published 9 Sep 2019

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.

Trend Following: How Great Traders Make Millions in Up or Down Markets
by Michael W. Covel
Published 19 Mar 2007

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.

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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.

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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.

The Shifts and the Shocks: What We've Learned--And Have Still to Learn--From the Financial Crisis
by Martin Wolf
Published 24 Nov 2015

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.

High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems
by Irene Aldridge
Published 1 Dec 2009

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.

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“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.

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond
by Chris Burniske and Jack Tatar
Published 19 Oct 2017

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.

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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.

The Great Reversal: How America Gave Up on Free Markets
by Thomas Philippon
Published 29 Oct 2019

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.

Woke, Inc: Inside Corporate America's Social Justice Scam
by Vivek Ramaswamy
Published 16 Aug 2021

In other words, every single company in the S&P 500 was already abiding by Goldman’s diversity standard long before Goldman issued its proclamation. Goldman’s announcement was hardly a profile in courage; it was just an ideal way to attract praise without taking any real risk. Another great risk-adjusted return for Goldman Sachs. Goldman’s timing was also impeccable in another way. Its diversity quota proclamation stole the headlines from a much less flattering event: Goldman had just agreed to pay $5 billion in fines to governments around the world for its role in a scheme stealing billions from the Malaysian people.2 In what has become known as the 1MDB scandal, Goldman paid more than $1 billion in bribes to win work raising money for the 1Malaysia Development Berhad Fund, which was supposedly meant to fund public development projects.

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That is, if certain ESG stocks happen to underperform, Gore can simply say those businesses didn’t implement ESG values “properly,” just as someone in the opposite camp could say that underperforming pure-profit businesses didn’t focus on pure profits “properly”—a classic case of the logical trap known as the No-True-Scotsman fallacy.i On this count, Larry Fink is more polished than Gore. In a paper titled “A Fundamental Reshaping of Finance,” Fink writes, “[Blackrock’s] investment conviction is that sustainability—and climate-integrated portfolios can provide better risk-adjusted returns to investors.”5 Fink’s comments were well crafted to elude criticism: he didn’t say that ESG portfolios will provide better returns, but only that they can. That’s harder to argue with, even though average mom-and-pop investors probably don’t stop to make the distinction. And Fink has ample quantitative support to back up his claims.

Hedge Fund Market Wizards
by Jack D. Schwager
Published 24 Apr 2012

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.

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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?

The Smartest Investment Book You'll Ever Read: The Simple, Stress-Free Way to Reach Your Investment Goals
by Daniel R. Solin
Published 7 Nov 2006

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.

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing
by Burton G. Malkiel
Published 10 Jan 2011

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.

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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.”

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal
by Ludwig B. Chincarini
Published 29 Jul 2012

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.

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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.

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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.

Market Risk Analysis, Quantitative Methods in Finance
by Carol Alexander
Published 2 Jan 2007

Constraints on allocations may be needed, such as no short sales, or to restrict investments within a certain range such as ‘no more than 10% of the fund is invested in US equities’. Capital allocation in an investment bank. First, the board selects optimal allocations to global product lines. Then, within each product line, the senior managers assign capital to different desks. 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.

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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. The efficient allocation of sparse resources is the fundamental problem in microeconomics.

Red-Blooded Risk: The Secret History of Wall Street
by Aaron Brown and Eric Kim
Published 10 Oct 2011

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.

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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.

The Death of Money: The Coming Collapse of the International Monetary System
by James Rickards
Published 7 Apr 2014

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.

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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.

Mastering Private Equity
by Zeisberger, Claudia,Prahl, Michael,White, Bowen , Michael Prahl and Bowen White
Published 15 Jun 2017

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?

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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.

The Psychology of Money: Timeless Lessons on Wealth, Greed, and Happiness
by Morgan Housel
Published 7 Sep 2020

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.

The Asian Financial Crisis 1995–98: Birth of the Age of Debt
by Russell Napier
Published 19 Jul 2021

Of course, every market had its experts so I decided to write a book called Investing with the Taiwan Masters and so I journeyed to Taipei to interview the five fund managers with the best risk-adjusted returns. I was delighted that they were all prepared to meet me and some of them spoke English. I always began with the preamble by explaining why I had come to see them and congratulating them on their excellent performance. The first question was always the same: “To what do you attribute your ability to produce high risk-adjusted returns through your investment in the Taiwan stock market?” The answer was the same in each case whether in English or Mandarin: “Special informations.”

Quantitative Trading: How to Build Your Own Algorithmic Trading Business
by Ernie Chan
Published 17 Nov 2008

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.

Impact: Reshaping Capitalism to Drive Real Change
by Ronald Cohen
Published 1 Jul 2020

As we saw in our discussion of impact ventures earlier, when we view the world through an impact lens, we discover opportunities to achieve higher growth and returns that we would otherwise pass by. In short, doing good can be excellent business. From Measuring Risk to Measuring Impact The measurement of risk, which began in the second half of the twentieth century,9 had a profound effect on investment portfolios across the world. The new notion of risk-adjusted returns led investors to include higher-risk investment categories in their investment portfolios, when the expected return was sufficiently high. This thinking brought the idea of portfolio diversification, which in turn opened the door to new higher risk and return asset classes, including venture capital, private equity and investment in emerging countries.

Just Keep Buying: Proven Ways to Save Money and Build Your Wealth
by Nick Maggiulli
Published 15 May 2022

I’ve examined rebalancing periods ranging from once a month to once a year, yet I never been able to find a clear winner. Unfortunately, no rebalancing frequency consistently outperformed all the others. Researchers at Vanguard came to a similar conclusion after analyzing the optimal rebalancing frequency for a 50/50 global stock/bond portfolio. Their paper states, “The risk-adjusted returns are not meaningfully different whether a portfolio is rebalanced monthly, quarterly, or annually; however, the number of rebalancing events and resulting costs increase significantly.”⁹⁴ And though their analysis examined rebalancing between assets with different risk characteristics (e.g., stocks and bonds), the same logic also holds when rebalancing between assets with similar risk characteristics.

Electronic and Algorithmic Trading Technology: The Complete Guide
by Kendall Kim
Published 31 May 2007

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. 

Eat People: And Other Unapologetic Rules for Game-Changing Entrepreneurs
by Andy Kessler
Published 1 Feb 2011

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.

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day
by John H. Johnson
Published 27 Apr 2016

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.

Trading and Exchanges: Market Microstructure for Practitioners
by Larry Harris
Published 2 Jan 2003

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.

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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.

Personal Investing: The Missing Manual
by Bonnie Biafore , Amy E. Buttell and Carol Fabbri
Published 24 May 2010

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.

Rigged Money: Beating Wall Street at Its Own Game
by Lee Munson
Published 6 Dec 2011

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.

Getting a Job in Hedge Funds: An Inside Look at How Funds Hire
by Adam Zoia and Aaron Finkel
Published 8 Feb 2008

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.

The Only Game in Town: Central Banks, Instability, and Avoiding the Next Collapse
by Mohamed A. El-Erian
Published 26 Jan 2016

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.

The Long Good Buy: Analysing Cycles in Markets
by Peter Oppenheimer
Published 3 May 2020

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.

Street Fighters: The Last 72 Hours of Bear Stearns, the Toughest Firm on Wall Street
by Kate Kelly
Published 14 Apr 2009

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.

How to Predict the Unpredictable
by William Poundstone

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.

Tomorrow's Capitalist: My Search for the Soul of Business
by Alan Murray
Published 15 Dec 2022

In the letter, Fink declared that BlackRock would henceforth place environmental sustainability at the core of all investment decisions. He stated, “As a fiduciary, our responsibility is to help clients navigate this transition. Our investment conviction is that sustainability and climate-integrated portfolios can provide better risk-adjusted returns to investors. And with the impact of sustainability on investment returns increasing, we believe that sustainable investing is the strongest foundation for client portfolios going forward.”1 The financial industry paid attention. BlackRock is the largest asset manager in the world, overseeing more than $9 trillion.

Space 2.0
by Rod Pyle
Published 2 Jan 2019

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.

Smarter Investing
by Tim Hale
Published 2 Sep 2014

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.

Heads I Win, Tails I Win
by Spencer Jakab
Published 21 Jun 2016

Some of those are unavailable to ordinary savers or prohibitively expensive. David Swensen, the fund manager who has led Yale University’s endowment to phenomenal returns, in his book Unconventional Success: A Fundamental Approach to Personal Investment proposed a model portfolio that could get some of that extra risk-adjusted return through funds that are both accessible and affordable for individuals. In the portfolio’s recent incarnation in May 2015, only 30 percent is in a U.S. stock index fund, with a fifth in international stocks and half in income-producing securities. The mix has changed over the years, but performance has been excellent.

Capitalism: Money, Morals and Markets
by John Plender
Published 27 Jul 2015

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.

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
Published 27 Jul 2009

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.

Keeping at It: The Quest for Sound Money and Good Government
by Paul Volcker and Christine Harper
Published 30 Oct 2018

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.

Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die
by Eric Siegel
Published 19 Feb 2013

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 Road to Ruin: The Global Elites' Secret Plan for the Next Financial Crisis
by James Rickards
Published 15 Nov 2016

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?)

The Clash of the Cultures
by John C. Bogle
Published 30 Jun 2012

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.

The Physics of Wall Street: A Brief History of Predicting the Unpredictable
by James Owen Weatherall
Published 2 Jan 2013

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.

Modernising Money: Why Our Monetary System Is Broken and How It Can Be Fixed
by Andrew Jackson (economist) and Ben Dyson (economist)
Published 15 Nov 2012

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.

Trillions: How a Band of Wall Street Renegades Invented the Index Fund and Changed Finance Forever
by Robin Wigglesworth
Published 11 Oct 2021

Not only did this gain Sharpe his PhD, but it eventually evolved into a seminal paper on what he called the “capital asset pricing model” (CAPM), a formula that investors could use to calculate the value of financial securities. The broader, groundbreaking implication of CAPM was introducing the concept of risk-adjusted returns—one had to measure the performance of a stock or a fund manager versus the volatility of its returns—and indicated that the best overall investment for most investors is the entire market, as it reflects the optimal tradeoff between risks and returns. This laid the intellectual groundwork for the coming invention of the index fund.

Capital Ideas Evolving
by Peter L. Bernstein
Published 3 May 2007

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. . . .

Security Analysis
by Benjamin Graham and David Dodd
Published 1 Jan 1962

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.

…

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 Quants
by Scott Patterson
Published 2 Feb 2010

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.

Nerds on Wall Street: Math, Machines and Wired Markets
by David J. Leinweber
Published 31 Dec 2008

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.

Philanthrocapitalism
by Matthew Bishop , Michael Green and Bill Clinton
Published 29 Sep 2008

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.

Stock Market Wizards: Interviews With America's Top Stock Traders
by Jack D. Schwager
Published 1 Jan 2001

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.

The Black Box Society: The Secret Algorithms That Control Money and Information
by Frank Pasquale
Published 17 Nov 2014

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!)

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition)
by Burton G. Malkiel
Published 5 Jan 2015

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.

Unknown Market Wizards: The Best Traders You've Never Heard Of
by Jack D. Schwager
Published 2 Nov 2020

Average Annual Compounded Return This value is the return level that, when compounded annually, will yield the cumulative return. Although I pay more attention to the return/risk metrics than return, a performance record can have superior return/risk values and an unacceptably low return level. Therefore, it is still necessary to check return alone. The 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., T-bill rate). For example, if the average return is 8% per year and the T-bill rate is 3%, the excess return would be 5%. The standard deviation is a measure of the variability of return.

No One Would Listen: A True Financial Thriller
by Harry Markopolos
Published 1 Mar 2010

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.

How Markets Fail: The Logic of Economic Calamities
by John Cassidy
Published 10 Nov 2009

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.

The Euro: How a Common Currency Threatens the Future of Europe
by Joseph E. Stiglitz and Alex Hyde-White
Published 24 Oct 2016

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.

European Spring: Why Our Economies and Politics Are in a Mess - and How to Put Them Right
by Philippe Legrain
Published 22 Apr 2014

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.

In Pursuit of the Perfect Portfolio: The Stories, Voices, and Key Insights of the Pioneers Who Shaped the Way We Invest
by Andrew W. Lo and Stephen R. Foerster
Published 16 Aug 2021

But that means you have to be able to survive those downward spikes. That brings us back to the liquidity issues—you need to have sufficient liquidity to survive the bumps.”47 Alpha Hunters and Beta Grazers Leibowitz then tackled another issue at the heart of investing: the pursuit of superior risk-adjusted returns, the so-called alpha—or, as he describes it, “the holy grail of active investment.”48 If you recall, alpha (a term coined by Eugene Fama’s student, Michael Jensen) refers to the excess return earned by an investor above and beyond the expected return predicted by its beta exposure in the capital asset pricing model (CAPM)—a holy grail indeed.

Radical Uncertainty: Decision-Making for an Unknowable Future
by Mervyn King and John Kay
Published 5 Mar 2020

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.

Advanced Stochastic Models, Risk Assessment, and Portfolio Optimization: The Ideal Risk, Uncertainty, and Performance Measures
by Frank J. Fabozzi
Published 25 Feb 2008

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.

The Bankers' New Clothes: What's Wrong With Banking and What to Do About It
by Anat Admati and Martin Hellwig
Published 15 Feb 2013

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.

Extreme Money: Masters of the Universe and the Cult of Risk
by Satyajit Das
Published 14 Oct 2011

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.

The Brussels Effect: How the European Union Rules the World
by Anu Bradford
Published 14 Sep 2020

Similarly, the effects on innovation can also be positive as EU regulations push companies to develop products that are not only more environmentally sustainable but at the same time more cost efficient. For instance, it is well documented that energy efficiency technologies often save consumers and businesses money, providing substantial risk-adjusted returns for those who adopt them. However, due to many informational and behavioral market failures, consumers regularly under-adopt them unless regulation is in place to compel them.13 Compliance with high standards can therefore also be an important source of cost savings and competitiveness for firms.

Unelected Power: The Quest for Legitimacy in Central Banking and the Regulatory State
by Paul Tucker
Published 21 Apr 2018

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).