Expected Returns E-Book

Antti Ilmanen

• Herausgeber: John Wiley & Sons
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 1140

Veröffentlichungsjahr: 2011

Beschreibung

This comprehensive reference delivers a toolkit for harvesting market rewards from a wide range of investments. Written by a world-renowned industry expert, the reference discusses how to forecast returns under different parameters. Expected returns of major asset classes, investment strategies, and the effects of underlying risk factors such as growth, inflation, liquidity, and different risk perspectives, are also explained. Judging expected returns requires balancing historical returns with both theoretical considerations and current market conditions. Expected Returns provides extensive empirical evidence, surveys of risk-based and behavioral theories, and practical insights.

Leseprobe

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?

13.5 CARRY HERE, CARRY THERE, CARRY EVERYWHERE

13.6 NOTES

Chapter 14 - Commodity momentum and trend following

14.1 INTRODUCTION

14.2 PERFORMANCE OF SIMPLE COMMODITY MOMENTUM STRATEGIES

14.3 TWEAKS

14.4 WHY DOES MOMENTUM—SUCH A NAIVE STRATEGY—WORK?

14.5 MOMENTUM IN OTHER ASSET CLASSES

14.6 NOTES

Chapter 15 - Volatility selling (on equity indices)

15.1 INTRODUCTION

15.2 HISTORICAL PERFORMANCE OF VOLATILITY-TRADING STRATEGIES

15.3 TWEAKS/REFINEMENTS

15.4 THE REASONS VOLATILITY SELLING IS PROFITABLE

15.5 OTHER ASSETS

15.6 NOTES

Chapter 16 - Growth factor and growth premium

16.1 INTRODUCTION TO UNDERLYING FACTORS IN CHAPTERS 16–19

16.2 INTRODUCTION TO THE GROWTH FACTOR

16.3 THEORY AND EVIDENCE ON GROWTH

16.4 ASSET MARKET RELATIONS

16.5 TIME-VARYING GROWTH PREMIUM

16.6 NOTES

Chapter 17 - Inflation factor and inflation premium

17.1 INTRODUCTION

17.2 INFLATION PROCESS—HISTORY, DETERMINANTS, EXPECTATIONS

17.3 INFLATION SENSITIVITY OF MAJOR ASSET CLASSES AND THE INFLATION PREMIUM

17.4 TIME-VARYING INFLATION PREMIUM

17.5 NOTES

Chapter 18 - Liquidity factor and illiquidity premium

18.1 INTRODUCTION

18.2 FACTOR HISTORY: HOW DOES LIQUIDITY ITSELF VARY OVER TIME?

18.3 HISTORICAL EVIDENCE ON AVERAGE LIQUIDITY-RELATED PREMIA

18.4 TIME-VARYING ILLIQUIDITY PREMIA

18.5 NOTE

Chapter 19 - Tail risks (volatility, correlation, skewness)

19.1 INTRODUCTION

19.2 FACTOR HISTORY

19.3 HISTORICAL EVIDENCE ON AVERAGE ASSET RETURNS VS. VOLATILITY AND CORRELATION

19.4 THEORY AND EVIDENCE ON THE SKEWNESS PREMIUM

19.5 VERDICT ON WHY HIGH-VOLATILITY ASSETS FARE SO POORLY

19.6 TIME-VARYING PREMIA FOR TAIL RISK EXPOSURES

19.7 NOTES

Part III - Back to broader themes

Chapter 20 - Endogenous return and risk: Feedback effects on expected returns

20.1 FEEDBACK LOOPS ON THE DIRECTION OF RISKY ASSETS

20.2 FEEDBACK LOOPS ON LESS DIRECTIONAL POSITIONS

20.3 AGENDA FOR MARKET-TIMERS AND RESEARCHERS

20.4 NOTES

Chapter 21 - Forward-looking measures of asset returns

21.1 POPULAR VALUE AND CARRY INDICATORS AND THEIR PITFALLS

21.2 BUILDING BLOCKS OF EXPECTED RETURNS

21.3 NOTES

Chapter 22 - Interpreting carry or non-zero yield spreads

22.1 INTRODUCTION

22.2 FUTURE EXCESS RETURNS OR MARKET EXPECTATIONS?

22.3 EMPIRICAL HORSE RACES FOR VARIOUS ASSETS

22.4 CONCLUSIONS

22.5 NOTES

Chapter 23 - Survey-based subjective expected returns

23.1 NOTES

Chapter 24 - Tactical return forecasting models

24.1 INTRODUCTION

24.2 WHAT TYPE OF MODEL?

24.3 WHICH ASSETS/TRADES?

24.4 WHICH INDICATOR TYPES?

24.5 ENHANCEMENTS AND PITFALLS

24.6 NOTES

Chapter 25 - Seasonal regularities

25.1 SEASONAL, CYCLICAL, AND SECULAR PATTERNS IN ASSET RETURNS

25.2 MONTHLY SEASONALS AND THE JANUARY EFFECT

25.3 OTHER SEASONALS

Chapter 26 - Cyclical variation in asset returns

26.1 TYPICAL BEHAVIOR OF REALIZED RETURNS AND EX ANTE INDICATORS THROUGH THE ...

26.2 TYPICAL BEHAVIOR OF REALIZED RETURNS AND EX ANTE INDICATORS ACROSS ...

26.3 NOTES

Chapter 27 - Secular trends and the next 20 years

27.1 CONTRASTING 1988–2007 WITH 1968–1987

27.2 REVERSIBLE AND SUSTAINABLE SECULAR TRENDS

27.3 THE NEXT 20 YEARS

27.4 NOTES

Chapter 28 - Enhancing returns through managing risks, horizon, skill, and costs

28.1 INTRODUCTION: HOW CAN INVESTORS ENHANCE RETURNS?

28.2 RISK

28.3 INVESTMENT HORIZON

28.4 SKILL

28.5 COSTS

28.6 NOTES

Chapter 29 - Takeaways for long-horizon investors

29.1 KEY TAKEAWAYS FROM THEORY

29.2 EMPIRICAL RETURN SOURCES

29.3 MY TAKE ON KEY DEBATES

29.4 KNOW THYSELF: LARGE LONG-HORIZON INVESTORS’ NATURAL EDGES

29.5 INSTITUTIONAL PRACTICES

29.6 NOTES

Appendix A - World wealth

Appendix B - Data sources and data-series construction

Bibliography

Index

For other titles in the Wiley Finance Series please see www.wiley.com/finance

This edition first published 2011

© 2011 Antti Ilmanen

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ISBN 978-1-119-99072-7

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Typeset in 10/12pt Times

Foreword

The first time I met Antti Ilmanen I thought he was insane. I was right. But, I was to discover quickly he was insane in a good way. The incident in question was at a new Ph.D. student mixer, about as exciting as it sounds, where seconds after being introduced to Antti he asked me “who do you rate in the top-five academics in finance and why?” He had his own answers on a set of Post-it notes. By the way, Antti has become famous for these Post-it notes. Suffice it to say if he consults the Post-its, and says you said something 17 years ago that turned out to be disproved in the latest Journal of Finance paper, just assume he’s right and move on.

Getting back to my story, he couldn’t understand how I couldn’t name five. Truth be told I probably could have named maybe three academics in total, and as to justifying my choices with “why?”, I was stuck at zero. I had not chosen finance because, like Antti, I was already obsessed with it. I chose it as my dad, a lawyer, forbade me to go to law school, I had some mild ability at mathematics, I recognized that finance might be both intellectually interesting and provide a good living, and finally I chose a Ph.D. as being a professor seemed like a nice life (little did I know the Sirens of Wall Street would soon call me away . . .) Antti looked at me with a “what the heck are you doing in the Ph.D. program” confused stare, and I politely ran away from the very intense, very finance-obsessed young Finn. Now, 20+ years later, and after countless similar incidents, I no longer run away (he finds me anyway), but he’s still intense and obsessed with finance. Over the years, and specifically through this book, those traits of his have made us all better off.

One more aside before my foreword begins in earnest. I briefly considered not writing this foreword in protest or as a rearguard action. Antti gives away a lot in this book. Perhaps few true secrets (though there are some!), as this is more a textbook than original research. But he makes much of what has become called “quantitative finance” easily (OK, 300+ dense pages, in original format, of “easily”) accessible in one place. Long term, that can’t be good for people like me who make their livings from this stuff being at least a bit secret. But being skilled at game theory I quickly determined that if I didn’t write the foreword somebody else would, so refusal would not accomplish much. With that strategy shot down, I very fleetingly considered having him killed, but this seemed to entail too much “tail risk” (see Chapters 15 and 19) and, anyway, is at least somewhat morally ambiguous. Besides, Antti’s just the type to have stashed a Post-it at his lawyer with a note “in the event of my untimely . . .” So in the end I decided to smile and write the foreword, and just resolve to work harder (along with many others at my firm and others throughout the field) to give Antti material for a sequel 20+ years from now!

Now let’s talk about the title subject.

Expected returns are how much you make on average over time on an investment or strategy. Risk is the possibility that, over any time horizon, you never get your expectation, as once again the world interferes with a perfectly good plan. Risk can make you fail to reach your expectations because of the simple uncertainty around expectations it brings, or because, as in 2008 for many, bad realizations call into question long-term survival or at least the ability to stick with a plan. Still, expected returns are incredibly important. If you survive and stick to a good plan, they add up nicely.

Antti has chosen to write a book on expected return in the Age of Risk. Risk, and particularly risk in the sense of survival, is all the rage in these days following a major financial crisis. This wide-ranging discussion of risk is right and proper. If some investors took too much risk and that caused or exacerbated the financial crisis, that’s a pretty important event to learn from. If some investors thought risk was simple and all figured out, and got a black swan dropped on their heads, it is important to learn from that too. However, it’s not the whole story.

While perhaps differing from the vast majority of risk-focused researchers and authors these days, I’d argue that the study of expected returns deserves more of our attention, even just after one of the biggest “left tails” in history. For one thing, as scary as the world got, some of our much maligned simple “normal” models actually did OK for all assets other than highly structured and/or levered ones, or over the very short term (see http://www.dimensional.com/famafrench/2009/05/how-unusual-was-the-stock-market-of-2008.html). For another thing, in a theme Antti returns to again and again in his book, expected returns don’t have to stay the same throughout time. Part of what we see as “risk” materializing during events like the 2008 financial crisis, and the 1999–2000 tech bubble, are more accurately seen as investors misunderstanding or misestimating expected returns. In both those cases the extreme dénouement that followed occurred, at least according to many (and me), because before the disaster, investors were willing to accept far too low an expected return, first on stocks and specifically on technology stocks, and then a decade later on credit and real estate. In both the theory and the reality of these globe-shaking events, the discussions of risk and expected return are intimately linked.

So why so much more discussion of risk than expected return these days? Well, obviously the magnitude of recent events makes all of us greatly value the chance we could have avoided that pain. But again, a careful study of expected return might have easily, perhaps even more easily, led to the same protection by avoiding certain investments on the ground that their expected returns were too low. Frankly, I think a lot of the answer is that discussing risk is inherently sexier than discussing expected returns. Good forecasts of expected return add up over the long term, but don’t matter for squat next week. Risk can kill you, or make you a hero, in the time it takes you to say “flash crash”. Would you rather write about a tsunami or erosion? More specifically, from a safe perch, say the hindsight of authoring a book, which event would be more dramatic to narrate? The analogy, forced though it may be, continues to work as erosion, or expected return, might be mind-numbingly boring at any one moment, but it has a heck of a lot to say about how the future will be shaped.

For perspective, you want to see a black swan? Try making nothing on your equities in your final 20 years until retirement because you bought them at an extremely high price (low expected return). That is one pile of ebony long-necked bird. If risk is about surviving the short term, expected return is about whether, after surviving, you think it was worth it.

Here’s another secret. Besides being at least as important, estimating expected returns is much harder than estimating risk. Now, estimating risk is no easy task. But at least finance wears its insecurity on its sleeve here. Are returns “normal” or do we have to worry about “black swans”, “fat tails”, and all that? This worry and effort is worthwhile, but those trying to estimate the expected return on various assets smile at those taking on the comparatively easy task of estimating risk. Basically, you need a heck of a lot more data to estimate expected returns than to estimate risk. In geek-speak, the first moment laughs at and taunts the second moment (which I’m going to pretend for a second is all you need for risk). The second moment gives up its secrets reluctantly, but far more readily than that more covert first moment. To estimate risk we lament (as statisticians not humanitarians) that we don’t get to see a few more disasters. To estimate expected return we lament we don’t get to see a few more centuries.

Now that the motivation behind and the difficulty of Antti’s task is clear, let’s talk about how one might actually try to forecast expected returns. Starting with the very big picture, why is there a positive expected return (let’s make that positive relative to some riskless rate or, in other words, a positive risk premium) on any asset or investment strategy? Well, there are two possible reasons. First, in a rational world you should get paid a positive expected return for bearing a risk others don’t wish to bear. As an aside, Antti repeatedly hammers home the poorly understood point that you should get paid particularly well for bearing risks that hurt you exactly when it is most painful to be hurt, and less or not at all for risks that only bring you harm when all is well. Second, in a world that permits some irrationality, you can also get paid for someone else’s systematic mistakes (or pay for your own).

While this parsing is useful, and Antti makes great use of it throughout the book, he takes it further, examining expected returns along three different dimensions (and asking the rational vs. irrational question for each part):

1. The expected return you get paid for owning any of the major asset classes (stocks, bonds, commodities, etc.).

2. The expected return you get from pursuing well-known strategies such as value investing, momentum investing, and carry-based investing. These are fundamentally different from the returns in (1) as, for instance, in examining value investing you don’t count the expected return from just being long stocks, but rather the extra return that has historically accrued to value stocks vs. the universe of all stocks. These strategies also tend to be far more dynamic than the asset classes in (1), changing their portfolio holdings more often through time.

3. The expected return you get from being willing to take the risk of being exposed to economic or other risks (e.g., shocks to growth, shocks to inflation, liquidity risk, etc.).

These are all connected and overlapping. For instance, passive ownership of equities exposes you more to risk of poor real economic growth than does passive ownership of bonds, which exposes you more to risk of higher-than-expected inflation. Small stocks expose you to more liquidity risk than large ones. The overlapping descriptions go on and on. Antti doesn’t mean for these three ways to look at expected returns to be a clean decomposition, rather he finds use for all three at once (remember, he calls this art and science). He continually stresses that there is no perfect way to examine (or later forecast) expected returns. You want to use every framework that is useful and brings anything new to the study. In this sense his three-way examination succeeds handily.

OK, let’s recap. So there are two ways you can get a positive expected return, by taking a risk you get paid for (roughly, “beta”), or by outsmarting someone else (“alpha”). There are three useful perspectives to examine the source of expected returns: asset classes, systematic strategies, and risk factors such as economic growth and liquidity. Exposure to any of these three can generate a positive return for either of the first two reasons (rational compensation for risk, or outsmarting of others who are less than rational). Now we’re cooking. All we need now—assuming we have identified one or more of the three categories of sources of expected returns (from the overlapping categories of asset class, strategy, and risk factor exposure) and have a theory as to why an investor gets paid this expected return (rational vs. irrational)—is a way to estimate the size of that expected return going forward. Here, you guessed it, Antti gives us not one but three very useful methods. They are again complementary—not competitors. In order from weakest to strongest (Antti’s ordering which I agree with): (1) you can forecast the future from looking purely at how something has done in the past, (2) you can forecast the future based solely on your theory of how the world should work without examining data, (3) you can jump straight to current measures (e.g., the P/E of the stock market, the nominal or real yield on bonds) and use them to forecast. Like Antti, for reasons you’ll have to get from the book, I favor (3), but even using current measures like P/E and yield you still need history and theory. You would like some theory to guide why you think today’s stock market P/E should be related to its future return, and probably would like some evidence that this has historically been an effective tool.

So, remember, there are two reasons you might get paid an expected return, three overlapping ways to parse the different sources of expected returns, and three non-mutually exclusive methods to estimate the expected return going forward, all probably containing part but not all the truth, so all must be strongly considered, but none blindly, and all at once. You got that?

Well, I didn’t promise you this would be simple, did I? In fact, I told you the opposite. Expected returns are interesting precisely because they are hard (and very important)! This means there is no easy answer. But, it also means that effort on this front is potentially highly valuable. Understanding easy but very important problems is also important, but the barriers to entry are low, and getting an advantage from such an easy understanding is usually difficult because the understanding is so widespread. (On the other hand, the investing public’s continued failure to understand basic ideas like diversifying widely, keeping average costs low, and realizing that it is hard to beat the market, etc., makes even this effort worthwhile to the marginal investor.) But, understanding deeply, if imperfectly, what many don’t understand at all or even get backwards (like the common error of thinking expected returns are especially high going forward after realized returns have had an exceptionally good run), well, that is great stuff!

Antti’s book is complicated and dense. The best compliment I can pay it is that it’s not more complicated than it has to be. It chooses to take on a hard question and is honest about this degree of difficulty. It chooses to offer multiple overlapping methods at each point, as there truly is not one certain method, and each brings some value. Another, far weaker, even harmful, book could tell you that forecasting long-term future returns is easy. That all you have to do is listen to the author’s simple formula and prosper (Dow 36,000 anyone?). Antti tells you it’s hard. He says if you follow our best minds, and use the best data we have, you probably have an edge, or at least your mistakes are not silly, but he says it is art and science, and makes no guarantees.

If I can leave you with advice that applies directly to Antti’s book, but is actually far more general, it’s to listen to people who take on difficult problems boldly. Listen to people who are honest that our best hope is improved but not perfect understanding. Listen to people who aren’t afraid to tell you the world is a complicated place, but then are damned talented at making it as simple as possible (but no simpler).

In other words, read this book. Listen to Antti.

Clifford S. Asness

AQR Capital Management LLC

The information set forth herein has been obtained or derived from sources believed by the author to be reliable. However, the author does not make any representation or warranty, express or implied, as to the information’s accuracy or completeness, nor does the author recommend that the attached information serve as the basis of any investment decision. This document has been provided to you solely for information purposes and does not constitute an offer or solicitation of an offer, or any advice or recommendation, to purchase any securities or other financial instruments, and may not be construed as such. This document is intended exclusively for the use of the person to whom it has been delivered by the author, and it is not to be reproduced or redistributed to any other person.

Dedicated to Rory Byrne, in memoriam

Acknowledgments

I have been a student of expected asset returns for over 20 years while wearing many different hats: buy-side bond portfolio manager in the Finnish central bank, Ph.D. scholar at the University of Chicago (UofC), bond research analyst at Salomon Brothers, sell-side strategist and prop trader at Salomon/Citigroup, and hedge fund trader and strategist at Brevan Howard. I have also advised various institutional investors on their long-term investment strategies—most regularly for Norway’s sovereign wealth fund in semiannual expert panel meetings. It is mainly this last experience that has inspired this book.

OK, that was too mildly put. I confess: I have been obsessed with expected returns. The passion for the topic arose in as different places as the Bank of Finland in Helsinki and the UofC campus in Hyde Park.

I earned my finance doctorate at the University of Chicago Business School (now the Booth School of Business) in the early 1990s, with Professors Eugene Fama and Kenneth French as my dissertation chairmen. In many minds this background puts me squarely in the efficient markets’ camp. However, we Chicago finance students were not taught a dogma. Instead, we were given a lifelong desire to learn more about financial markets with the emphasis on an empirical approach: let ideas compete freely and let data be the judge. The EMH paradigm gave a very useful framework for understanding and analyzing markets, but few of us became EMH purists. Indeed, many of the anomalies that challenged the EMH were uncovered by Chicago professors and students. I hope that readers will find my treatment of risk-based and behavioral explanations for expected returns surprisingly balanced.

Fama and French are also among my leading lights when I tackle that perennial question “if your ideas have any investment value, why would you share them?” Evidence abounds that business gains accrue to perceived thought-leaders. Yet, many serious writers publish for less selfish reasons as well. They want to enhance the general investor experience and improve the marketplace.

Chicago also had other superb finance and economics professors and besides faculty we got great guest speakers in the finance workshops. Naturally, I had more active dialogue with fellow students, many of whom became excellent talking-partners and friends over the years. Cliff Asness is of course the best known. I humbly thank him for the characteristically witty and insightful foreword and for making it clear to the reader in the first lines of his foreword that I am insane in a good way.

I have been lucky with bosses: my managers at the Bank of Finland taught me the ropes and assigned me the role of a bridge between academia and practitioners much before I had any expertise to fulfill this role. After my Chicago years, my bosses at Salomon Brothers’ storied Bond Portfolio Analysis Group let me balance research and market roles, always stressing the value of conveying complex ideas as intuitively as possible. At Brevan Howard, the co-CEOs allowed me to take different roles, some outside the core hedge fund business, and recognized the value of this book for institutional investors. It helps that the book’s themes have little to do with Brevan Howard’s core approach of tactical rates trading based on fundamental macro-views with a focus on trade construction and risk management, so I will not be revealing any proprietary trade secrets.

This book has been hugely influenced by my regular meetings in Oslo discussing the long-run investment strategy for Norway’s sovereign wealth fund. One outgrowth of those meetings has been even more inspirational—our trialogue with Knut Kjaer and Andrew Ang about diverse long-horizon investor topics.

I have benefited from the thinking of fellow students, colleagues, customers, and research peers in academia and business. Some of the best sources I have yet to meet personally, but I am a voracious reader—to which this book’s lengthy reference list attests. There are too many people to thank by name, but I make an exception for Rory Byrne to whom this book is dedicated. For several years Rory was my main partner in developing and implementing systematic trading models and always a sensible sounding board. Sadly, Rory succumbed last year to a persistent tumor at the age of 35.

A most emphatic thank-you goes to Laurence Siegel, Knut Kjaer, Matti Ilmanen, and Victor Haghani who carefully read the manuscript (or evolving versions of it) and greatly improved the book. I am also grateful to Andrew Ang, Cliff Asness, William Bernstein, Francis Breedon, Alistair Byrne, Adrian Eterovic, Kenneth French, Pal Haugerud, Susan Hudson-Wilson, Doug Huggins, Ray Iwanowski, Matthew James, Robert Kosowski, John Liew, Thomas Maloney, Mikko Niskanen, Lasse Heje Pedersen, Jonas Rinné, Rudi Schadt, Matti Suominen, Etienne Varloot, and Gerlof de Vrij, for their helpful comments on parts of my manuscript. I also thank my editors Pete Baker, Aimee Dibbens, Vivienne Wickham and their colleagues at Wiley for all their help, as well as Neil Shuttlewood, whose company OPS Ltd managed the project. Of course, the responsibility for any errors is mine. In a work of this size, some errors are bound to creep in. I welcome any comments on them as well as other feedback ([email protected]). I credit my data sources separately in Appendix B.

Finally, I thank my family, the source of the most precious expected and realized returns. The family in Finland: My mother and siblings as well as my late father who taught me the love of reading through example and who urged me to read in a way that advances my knowledge systematically (“try to position each new piece of knowledge in its correct place in the star sky”). My Nordic background and values likely helped counterbalance any free market ideology picked up at UofC or material interests in a banking and hedge fund career. And the family in Bad Homburg: My rocks, dear Annette as well as Kukka and Akseli, I am hugely grateful for your understanding during this long project. I just hope I can be as supportive when it is your turn.

Antti Ilmanen

Bad Homburg, November 2010

Abbreviations and acronyms

AMArithmetic MeanATMAt The Money (option)AUMAssets Under ManagementBEIBreak-Even InflationBFBehavioral FinanceB/PBook/Price, book-to-market ratioBRPBond Risk Premium, term premiumB-SBlack–ScholesC-P BRPCochrane–Piazzesi Bond Risk PremiumCAPMCapital Asset Pricing ModelCAYConsumption wealth ratioCBCentral BankCCWCovered Call WritingCDOCollateralized Debt ObligationCDSCredit Default SwapCFCash FlowCFNAIChicago Fed National Activity IndexCFOChief Financial OfficerCMDCommodity (futures)CPIyoyConsumer Price Inflation year on yearCRBCommodity Research BureauCRPCredit Risk Premium (over Treasury bond)CRRAConstant Relative Risk AversionCTACommodity Trading AdvisorDDMDividend Discount ModelDJ CSDow Jones Credit SuisseDMSDimson–Marsh–StauntonD/PDividend/Price (ratio), dividend yieldDRDiversification ReturnE( )Expected (conditional expectation)EMHEfficient Markets HypothesisE/PEarnings/Price ratio, earnings yieldEPSEarnings Per ShareERPEquity Risk PremiumERPBEquity Risk Premium over Bond (Treasury)ERPCEquity Risk Premium over Cash (Treasury bill)FForward price or futures priceFFFama–FrenchFIFixed IncomeFoFFund of FundsFXForeign eXchangeGGrowth rateGARCHGeneralized AutoRegressive Conditional HeteroskedasticityGCGeneral Collateral repo rate (money market interest rate)GDPGross Domestic ProductGMGeometric Mean, also compound annual returnGPGeneral PartnerGSCIGoldman Sachs Commodity IndexHHolding-period returnHFHedge FundHFRHedge Fund ResearchHMLHigh Minus Low, a value measure, also VMGHNWIHigh Net Worth IndividualHPAHouse Price Appreciation (rate)HYHigh Yield, speculative-rated debtIGInvestment Grade (rated debt)ILLIQMeasure of a stock’s illiquidity: average absolute daily return over a month divided by dollar volumeIPOInitial Public OfferingIRInformation RatioIRPInflation Risk PremiumISMBusiness confidence indexITMIn The Money (option)JGBJapanese Government BondK-W BRPKim–Wright Bond Risk PremiumLIBORLondon InterBank Offered Rate, a popular bank deposit rateLPLimited PartnerLSVLakonishok–Shleifer–VishnyLtALimits to ArbitrageLTCMLong-Term Capital ManagementMAMoving AverageMBS(fixed rate, residential) Mortgage-Backed SecuritiesMIT-CREMIT Center for Real EstateMOMEquity MOMentum proxyMSCIMorgan Stanley Capital InternationalMUMarginal UtilityNBERNational Bureau of Economic ResearchNCREIFNational Council of Real Estate Investment FiduciariesOASOption-Adjusted (credit) SpreadOTMOut of The Money (option)PPriceP/BPrice/Book (valuation ratio)P/EPrice/Earnings (valuation ratio)PEPrivate EquityPEHPure Expectations HypothesisPTProspect TheoryrExcess returnRReal (rate)REReal EstateREITsReal Estate Investment TrustsRWHRandom Walk HypothesisSSpot price, spot rateSBRPSurvey-based Bond Risk PremiumSDFStochastic Discount FactorSMBSmall Minus Big, size premium proxySRSharpe RatioSWFSovereign Wealth FundTEDTreasury–Eurodollar (deposit) rate spread in money marketsTIPSTreasury Inflation-Protected Securities, real bondsUIPUncovered Interest Parity (hypothesis)VaRValue at RiskVCVenture CapitalVIXA popular measure of the implied volatility of S&P 500 index optionsVMGValue Minus Growth, equity value premium proxyWDRAWealth-Dependent Risk AversionXCash flowYYieldYCYield Curve (steepness), term spreadYTMYield To MaturityYTWYield To Worst

Disclaimer

Antti Ilmanen is a Senior Portfolio Manager at Brevan Howard, one of Europe’s largest hedge fund managers.

The views expressed in this book are the author’s own views and are not the views of any of the Brevan Howard group of affiliated companies.

The information and opinions contained in this document are for information and discussion purposes only, do not constitute a financial promotion and do not purport to be full or complete. Whilst the author has used his best efforts to ensure the accuracy or completeness of any information contained herein, no reliance may be placed for any purpose on the information or opinions contained in this book or their accuracy or completeness. No representation, warranty or undertaking, express or implied, is given as to the accuracy or completeness of the information or opinions contained in this book and no liability is accepted for the accuracy or completeness of any such information or opinions.

This book does not constitute or form part of any offer to issue or sell, or any solicitation of any offer to subscribe or purchase, any shares or any other interests nor shall it or the fact of its distribution form the basis of, or be relied on in connection with, any contract thereof. This book is not intended to constitute, and should not be construed as, investment advice.

Part I

Overview, historical returns, and academic theories

1. Introduction

2. Whetting the appetite: Historical averages and forward-looking returns

3. The historical record: The past 20 years in a longer perspective

4. Road map to terminology

5. Rational theories on expected return determination

6. Behavioral finance

7. Alternative interpretations for return predictability

1

Introduction

• This book covers the general topic of expected returns on investments. The traditional paradigm among institutional investors focuses too much on historical performance and too narrowly on asset class allocation. This book argues that investment decision making should be broadened beyond the asset class perspective and a wider set of inputs should be used for assessing expected returns.

• The book considers in detail a wide range of return sources: major asset classes (stocks, bonds, and alternative investments) and strategy styles (value, carry, momentum, and so forth) as well as risk factors (such as growth, inflation, and liquidity).

• The main inputs—beyond discretionary views—for investors to judge expected returns are (1) historical performance, (2) theories, and (3) forward-looking indicators. A better understanding of these inputs and a better balance among them is needed.

• Well-known evidence of historical asset returns include the significant long-run outperformance of stocks over bonds (less so in the 19th and 21st century than in the 20th century) as well as more moderate rewards for bearing interest rate risk and credit risk.

• Less familiar historical findings include the pervasive success of value, carry, and momentum strategy styles in several markets as well as the tenuous relation between volatility and average returns. This book highlights the low long-run returns of the most volatile assets within each asset class, a finding that may reflect risk-seeking (“lottery-playing)” behavior by investors, or that may be explained by leverage constraints.

• Finance theories have changed dramatically over the past 30 years, away from the restrictive theories of the single-factor CAPM, efficient markets, and constant expected returns. Current academic views are more diverse, less tidy, and more realistic. Expected returns are now commonly seen as driven by multiple factors. Some determinants are rational (risk and liquidity premia), others irrational (psychological biases such as extrapolation and overconfidence). Expected returns on all factors may vary over time.

• A central insight from academic finance theories is that required asset returns have little to do with an asset’s standalone volatility and more to do with when losses can be expected to occur. Investors should require high-risk premia for assets that fare poorly in bad times, whereas safe haven assets (that fare well in bad times and less well in good times) can justify low or even negative risk premia. Strategies that resemble selling financial catastrophe insurance—steady small gains punctuated by infrequent but large losses—warrant especially high risk premia because their losses are so highly concentrated in the worst times.

• Forward-looking indicators such as valuation ratios have a better track record in forecasting future asset class returns than rearview mirror measures. The practice of using historical average returns as best estimates of future returns is dangerous when expected returns vary over time. Recall stock markets in 1999–2000.

• Long-run expected returns for any investment tend to be especially high following adverse events. For example, equity markets tend to have predictably higher returns after recessions, and nominal bonds after high inflations.

• Investors can try to boost expected returns by taking risks that produce attractive rewards for all market participants (beta risks) and/or by skillful active management (alpha) which may involve exploiting regularities and market inefficiencies. This book offers a comprehensive guide for smart harvesting of beta risk premia, covering both long-run exposures to traditional and alternative betas as well as tactical beta timing. However, I concede from the outset that the magic of view-based alpha generation cannot be conveyed in a book.

• Two visual aids—an elephant and a cube—help the reader keep “the big picture” in mind through the book.

• Although I present large amounts of empirical evidence about historical returns and forward-looking indicators, as well as numerous theories in an attempt to make sense of the data, I believe it is important to stress humility. Hindsight bias makes us forget how difficult forecasting is, especially in highly competitive financial markets. Expected returns are unobservable and our understanding of them is limited. Even the best experts’ forecasts are noisy estimates of prospective returns.

It was six men of Hindostan, To learning much inclined, Who went to see the elephant (Though all of them were blind); That each by observation Might satisfy his mind.

—J.G. Saxe (to be continued)

The traumatic housing and credit crisis that began in 2007 challenged many beliefs about investing and financial markets. The aftershocks of the crisis are still felt in many markets and economies (not least in public finances), but it is no longer necessary or advisable to view the future solely through the prism of this crisis. Instead, this book surveys the landscape of expected returns and long-term investment prospects based on lessons learned over decades.

The traditional paradigm of institutional investing focuses on relatively static asset class allocations that are largely determined by historical performance. We must go back to basics and broaden the traditional paradigm in two ways. The inputs used for assessing expected returns should be better balanced and the idea of what constitutes investments should be challenged beyond the asset class perspective.

Figure 1.1. The elephant: Shining light on expected returns from different directions.

The foremost need for multi-dimensional thinking is on inputs. When investors make judgments about the expected returns of various investments, they should guard against being blinded by past performance and must ensure that they take all or most of the following considerations into account:

• historical average returns;

• financial and behavioral theories;

• forward-looking market indicators such as bond yields; and

• discretionary views.

Figure 1.1 recalls the parable about several blind men and an elephant, as told by the American poet John Godfrey Saxe (1816–1887), each man describing one part of the elephant from his narrow vantage point. One man is feeling the leg and calling it a tree, another touching the ear and naming it a fan, a third mistaking the tail for a rope, and so on. Each man misses the big picture—and so will investors who study expected returns from a single vantage point.

The first approached the elephant,And happening to fallAgainst his broad and sturdy side,At once began to bawl,“Bless me, it seems the elephantIs very like a wall.”

So the challenge is to refine the art of investment decision making in a way that exploits all our knowledge about historical experience, theories, and current market conditions, without being overly dependent on any one of these. This book will summarize the state of knowledge on all these aspects, but it focuses on the first three inputs since these are systematic. I have less to say about discretionary views since these are inherently investor specific. However, the approaches described in this book will help readers form such views. Stated differently, investing involves both art and science; a solid background in the science can improve the artist.

Figure 1.2. The cube: Three perspectives on investments and their expected returns.

Before turning to these inputs, I address the reasonable question “Expected returns on what?” The traditional paradigm has been to break the complex world of investments into simplified groupings called asset classes. Analyzing, and allocating to, asset classes has been the dominant approach but I argue that studying investments from perspectives other than asset classes can enhance our understanding of return sources and our ability to diversify effectively. This book will add to the asset class perspective the complementary viewpoints of strategy styles and risk factors, as in the three-dimensional “cube” in Figure 1.2:

• Starting with the asset class perspective, I will cover all traditional asset classes (equities, government bonds, credits) plus many alternative ones (including real estate, commodities, hedge funds, and private equity). I focus on long-run returns but also review tactical market-timing approaches. A broader mindset naturally leads to questioning the traditional 60/40 portfolio which relies excessively on one source of excess returns (the equity premium) and which therefore has highly concentrated risk (more than 90% of portfolio volatility is due to equities).

• The strategy style perspective is especially important for understanding the profit potential of popular active-trading approaches. Value, carry, momentum, and volatility styles have outperformed buy-and-hold investments in many asset classes. Styles can also offer better diversification opportunities than asset classes.

• Sophisticated investors are increasingly trying to look beyond asset classes and strategies in order to identify the underlying factors driving their portfolio returns. A factor-based approach is also useful for thinking about the primary function of each asset class in a portfolio (stocks for harvesting growth-related premia, certain alternatives for illiquidity premia, Treasuries for deflation hedging, and so on) as well as for diversifying across economic scenarios. Among underlying risk factors, I opt to focus on growth, inflation, illiquidity, and tail risks (volatility, correlation, return asymmetries).

The second, feeling of his tusk,Cried, “Ho! What have we hereSo very round and smooth and sharp?To me, ’tis mighty clearThis wonder of an elephantIs very like a spear.”

How can investors deal with the complexity of multiple inputs and perspectives, let alone with the even more bewildering assortment of novel investment products on offer? This book provides a map to investors, giving a bird’s eye view over a rugged terrain and occasionally zooming in to interesting locations (12 case studies), not unlike Google Earth™. I hope my two visual aids—the elephant and the cube—will help readers keep the forest in sight among the many trees along the way.

Next, Sections 1.1–1.4 give an overview on the four perspectives on “feeling the elephant” (i.e., on considerations for judging expected returns). These themes will be expanded on through the book.

1.1 HISTORICAL PERFORMANCE

Historical average returns are a common starting point for judging expected returns. The idea is that if expected returns are constant over time, long-run average realized return is a good estimate of expected future return. Unexpected news dominates returns in the short run but the effects of such news tend to cancel out in the long run.

Why should you think twice before using historical returns as forecasts of future returns?

• Any sample period may be biased—in the sense of not being representative of market expectations—so that unexpected returns do not neatly cancel out, especially if the sample starts or ends at times of exceptionally high or low market valuations. Windfall capital gains during a benign sample can boost average returns meaningfully even over multiple decades. Bond indices are a prime example, given the downtrend in bond yields since the 1970s and 1980s when a number of the widely used bond index histories start.

• In principle, longer historical windows reduce sample specificity and enable more accurate estimates of average returns. However, distant historical data may be irrelevant due to structural changes, apart from lower data quality. Would we really want to include data from the 1600s even if good-quality returns were available to us?

• Expected returns may vary over time in a cyclical fashion, which makes extrapolation of multi-year performance particularly dangerous. Periods of high realized returns and rising asset valuations—think stock markets in the 1990s—are often associated with falling forward-looking returns.

• For specific funds and strategies, the historical performance data that investors get to see are often upward biased. This bias is due to the voluntary nature of performance reporting and survivorship bias (so that poor performers are left out of databases or are not marketed by the fund manager). A similar caveat applies to simulated “paper” portfolios because backtests may be overfitted and trading costs ignored or understated.

These concerns notwithstanding, this book presents extensive evidence of long-run realized returns, when possible covering 50-to-100-year histories. Several main findings are familiar to most readers:

• Stock markets have outperformed fixed income markets during the past century in all countries studied. The compound average real return for global equities between 1900 and 2009 is 5.4%, which is 3.7% (4.4%) higher than that of long-term government bonds (short-dated Treasury bills). Stocks’ outperformance over bonds is 0.5% to 0.8% higher for the U.S. than globally and was even more pronounced before the negative returns in 2000s. The experience of the current investor generation has buried the myth that stocks always beat bonds over 20-year or 30-year horizons. (This myth was never true. Many exceptions to it occurred outside the U.S. in the 20th century and inside the U.S. during the 19th century.)

• Among fixed income markets, long-term bonds have outperformed short-dated bonds by 0.5% to 1% and credit-risky corporate bonds have outperformed comparable government bonds by 0.3% to 1% (low end for investment-grade bonds, high end for high-yield bonds). More surprisingly, the reward for bearing interest rate risk or credit risk is most consistent at short maturities. At the back end of the yield curve and at the low end of the credit spectrum there has been scant long-run benefit for additional risk taking.

• Illiquid assets have tended to offer moderately higher long-run returns than their liquid peers. Part of corporate bonds’ excess returns over Treasuries reflects the liquidity disadvantage of corporates, and the same appears true for small-cap stocks over large-cap stocks. Evidence across asset classes is more ambiguous because various reporting biases may overstate published private equity and hedge fund returns. Moreover, average return differences can reflect premia for various risks and not just for illiquidity; disentangling the determinants is quite hard.

Other findings are less widely known:

• Certain active strategy styles have proved profitable in several asset classes, adding several percentage points to annual average returns. The most prominent styles are value (overweighting assets that appear cheap based on some valuation metrics, while underweighting richly valued peers), carry (overweighting high-yielding assets while underweighting low-yielding assets), and momentum (overweighting assets that have outperformed over multiple months while underweighting recent laggards). The rewards from simulated active strategies are often overstated due to overfitting or selection biases. Yet the value, carry, and momentum profits have been so prevalent that there is little doubt that these opportunities really existed in the past. The pertinent question is whether these strategies will remain profitable now that they are so well known. If their profits represent (at least in part) risk premia rather than market inefficiencies, it is less likely that future excess returns will be fully competed away.

• A flipside of the value effect is that “growth assets”—stocks of firms or countries with high past and expected growth rates—often deliver poor long-run returns that can be traced back to their high valuation ratios. One behavioral explanation is that investors over-extrapolate past growth rates and thus overpay for growth assets. No investment is attractive at any price, however fast growing it has been.

• Yield seeking has been profitable in many contexts but more so in cross-country trades than in within-country trades. The worst results are for complex carry products that may contain hidden costs and risks.

• Chasing returns sounds very naive but favoring past winners has not been a bad strategy historically—as long as history lengths to measure past returns are judiciously selected. Details differ across asset classes but most investments exhibit momentum (continuation) tendency over multi-month horizons and a mild reversal tendency over multi-year horizons.

• The empirical relation between volatility and expected returns is tenuous. Volatility and long-term average returns are positively related across asset classes. Moreover, a strategy of writing equity index options earns positive long-run returns, a justifiable reward given the inherent riskiness of effectively selling financial catastrophe insurance. However, the most volatile assets within each asset class—high-volatility stocks, 30-year Treasuries, and CCC-rated corporates—tend to offer low long-run returns and even worse risk-adjusted returns. This surprising pattern may reflect investors’ lottery-seeking bias (overpaying for the hope of jackpot returns) as well as leverage constraints (overpaying for inherently volatile assets that give high bang for the buck for naive return seekers). Avoiding the inherently volatile pockets of each market (lottery tickets) and leveraging up inherently stable assets have boosted returns in the past, even before the leverage restrictions were tightened in the aftermath of the 2008 crisis.

1.2 FINANCIAL AND BEHAVIORAL THEORIES: A BRIEF HISTORY OF IDEAS

The third approached the animal And happening to take The squirming trunk within his hands Then boldly up and spake: “I see,” quoth he, “the elephant Is very like a snake.”

Finance is a field in which academics and investment practitioners have had a huge influence on each other, as highlighted in Peter Bernstein’s books Capital Ideas and its sequel Capital Ideas Evolving. The theory of finance evolved dramatically during the last half-century, contemporaneously with the period during which investing grew into a big professional business. Until the 1950s finance theory hardly existed: the focus was on predicting cash flows, not on risk or required returns. Then, three decades of pioneering research brought about an academic consensus view that relatively simple theories employing highly restrictive assumptions—the single-factor CAPM, the efficient market hypothesis, constant risk premia—could explain asset prices and expected returns:

• A starting point in finance is that investors set prices so that an asset’s cost equals its expected benefits. Aggregating across investors, each asset’s market price equals the expected sum of its future cash flows discounted to the present value (i.e., expected cash flows are divided by 1 + Discount rate).

• Asset-pricing theory focuses on the determinants of discount rates or required returns. In market equilibrium, an asset’s expected return equals the required return that rational investors together demand. Risk-averse investors do not use the riskless rate for discounting, unless the cash flow being discounted is itself riskless; the discount rate also reflects the required compensation for the riskiness of an asset’s expected future cash flows. This compensation in turn reflects both the amount of risk and the intensity of investor aversion toward risk.

• According to the Capital Asset Pricing Model (CAPM), an asset’s amount of risk is fully captured by its (equity) market beta, while general investor risk aversion determines the size of the market risk premium. Each asset’s expected return in excess of a common riskless rate equals the product of the asset’s beta (sensitivity to market movements) and the common market risk premium. The difference in expected returns across assets reflects only differences in the betas of the assets. Investors can boost long-run returns by bearing more beta risk: by holding higher beta stocks and by shifting allocations from fixed income (with beta near zero) to equities (with beta near one).

• Given the assumption of constant expected returns, the long-run average of realized returns is a good estimate of expected return, even if realized and expected returns can differ sharply over any short time window. Attempts to time the market were deemed a particularly wasteful form of active trading because moving from equities to cash implies forfeiting the large and presumably constant equity premium.

Such restrictive theories did not prove sufficient to explain real market behavior. As new evidence accumulated, both academic and investor opinions evolved:

• In recent decades, high-beta stocks and high-volatility stocks gave no return advantage, perhaps the reverse. Value and momentum tilts were more consistent return enhancers among equities, while related carry and trend strategies fared well in other asset markets.

• The realized premium of equities over bonds turned out to be slim compared with earlier histories, and views on the future equity premium were trimmed down.

• Several very large boom–bust cycles made the idea of constant risk premia less credible and that of market timing more acceptable.

• After the cult of equity busted around the year 2000, alternative assets, carry trades, and harvesting illiquidity premia became the preferred ways to boost returns. All these approaches resulted in dramatic losses in 2008.

• Just when investors learned to value conservatism, junk bonds and speculative stocks rallied by at least 100% in the year ensuing the crash bottom in March 2009.

So where are we now? Current academic views are more diverse, less tidy, and more realistic than they used to be. Between 1980 and 2010, empirical and theoretical work added flesh to the core models by incorporating multiple risk factors, time-varying expected returns, liquidity effects and other market frictions, as well as investor irrationality. The field is increasingly seeking help from outside finance, economics, and statistics by turning to psychology, biology, physics, and even philosophy.

Greater realism is welcome but is no panacea. Theories can only enhance our understanding if they simplify the messy real world. In that spirit, I highlight some core ideas that apply to all investments.

Complex reality with multiple return drivers, both rational and irrational

If investors want to earn expected returns higher than the riskless rate, the most reliable way is to bear risks that markets reward with a premium. A less reliable way is to pursue active management, in which successful investors (skillful or lucky) reap gains at the expense of their less successful peers.

The simple story of a single risk factor, constant expected returns, and fully rational investors is outdated. Even in a more complex world with several drivers of required returns, equity market beta—or more generally sensitivity to economic growth—remains the most important risk source. Beyond equity market beta, exposures to inflation, illiquidity, and tail risks (such as volatility) influence many assets and are rewarded.

The interrelations between factors matter. Portfolio diversification is more effective with independent or, even better, with negatively correlated return sources. However, many return sources tend to be positively correlated—and especially so during a systemic crisis such as that of 2008. I will present return analysis separately for numerous factors or return sources but I already note that these can overlap with each other or be empirically correlated (I show some evidence at the beginning of Chapter 16).

The rewards may have rational or irrational origins. Numerous behavioral stories explain the high returns of certain asset types or trading strategies as being due to mispricing rather than fair compensation for risk. I present many examples of irrationality, emphasizing investors’ extrapolation as well as over- and underreaction tendencies as drivers behind the long-run outperformance of value and momentum strategies.

Expected return differentials across assets depend less on volatility . . .

Many investors view the risk–reward tradeoff as being determined through an asset’s standalone risk. Stocks are more volatile than bonds and thus deserve higher long-run returns. Likewise, long bonds are more volatile than short bonds and thus deserve higher long-run returns. This idea points in the right direction—that risk influences expected returns—but misses the fact that all volatility is not equal.

Each asset reflects a bundle of underlying risk factors and some idiosyncratic risk. Diversifiable (idiosyncratic) risks should not earn any reward; only systematic risks (those that cannot be eliminated through diversification) can be expected to be rewarded with a premium. The reward for volatility can differ depending on which of several systematic factors is the source of the volatility. Overall, the theoretical relation between volatility and expected return is quite ambiguous (and, as already noted, the empirical relation is tenuous).

. . . and more on the typical timing of losses

Although there are a number of different risk factors, one key rational theme explains what drives financial risk premia. Assets or factors that perform poorly in “bad times”—think of recessions and financial crises—warrant high required returns. Conversely, safe haven assets (such as long-term government bonds, at least since the late 1990s) that smooth portfolio returns in bad times deserve a low risk premium. This central insight in modern academic finance about asset risk premia implies that certain assets and/or strategies deserve high long-term returns (and offer especially juicy returns in good times) exactly because they tend to give a terrible performance just when it hurts investors most. Many practitioners and policymakers had to wait until 2008 to learn this lesson, the hard way.

Figure 1.3. Asset class performance in the long run vs. in bad times.

Sources: Bloomberg, Ken French’s website, Citigroup, Barclays Capital, S&P GSCI, MIT-CRE.

For illustration, Figure 1.3 scatterplots 50-year average real returns of various U.S. asset classes on a simple proxy of “bad times performance”—average losses in arguably the three worst years (1974, 1981, 2008) for financial markets and the global economy during this half-century. The empirical relation is hardly linear but the assets with the best long-run performance—small-cap stocks—also gave the worst losses in bad times. At the other end, Treasury bills and bonds with low long-run returns were the best safe haven assets in bad times (more so in deflationary 2008 than in inflationary years).