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Inefficient Markets: An Introduction to Behavioral Finance (Clarendon Lectures in Economics)

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"Inefficient Markets" is the most thoughtful original treatment of behavioral finance I have found. Unlike most other books on this topic, which either are vapidly light but original or are intellectually rewarding but disjointed compendiums of previously published articles, Shleifer has produced an interesting and intelligent synthesis of behavioral finance. He organizes his materially logically and clearly, covering the central themes of behavioral finance in as unified a manner as the subject permits. He has clearly thought hard about the subject matter, and his book reflects this. Shleifer's writing style is both lucid and academically rigorous, which makes for an enjoyable and informative book.

Shleifer begins by reviewing the theoretical and empirical foundations of the efficient market hypothesis (EMH) and introducing the principal challenges to this hypothesis. His review of the EMH is careful, objective, and respectful. His introduction to the principal challenges to the EMH is written with equal integrity, and his analysis of these challenges is non-dogmatic and relatively conservative. He carefully avoids overstating the conclusions of the academic research. While Shleifer clearly feels that financial markets are NOT efficient, as an academic he also acknowledges the inability of empirical research to PROVE with certainty that financial markets are or are not efficient. It is his careful interpretation of the evidence on both sides of the EMH fence that gives this book its tremendous credibility.

The majority of "Inefficient Markets" covers the two principal building blocks of behavioral finance: the theory of limited arbitrage and the theory of investor sentiment. Shleifer demonstrates that arbitrage is of limited usefulness in relatively competitive markets, much less in more complicated environments, and that financial markets should not be presumed efficient. He then presents a model of investor sentiment that incorporates the results of experimental research into individual decision-making under conditions of uncertainty. Shleifer also provides an extremely clear overview of the DeLong et al. noise trader model, and concludes the book by highlighting some of the unsolved problems in financial economics.

It should be noted, if it is not already clear, that this book is intended for PhD and advanced MBA students (in fact, the book is a core text for the University of Chicago's PhD course in Behavioral Finance). The analysis in the book is both theoretical and rigorous. Nonetheless, the intellectually curious reader with a basic exposure to microeconomics and/or financial economics will find the book rewarding. You are guaranteed to come away from this book knowing more about that opportunities and challenges facing the field of behavioral finance.

For practitioners, or anyone looking for a more general/popular treatment of behavioral finance, I recommend "Beyond Greed and Fear" by Hersh Shefrin.

In this book, Professor Schleifer has collected some of his older papers, and given this collection a fashionable title, by making references both to market inefficiency and behavioral finance.

The truth is that these references don't make too much sense, since he neither gives an introduction to behavioral finance (the study of how people make decisions under financial uncertainty), nor does he provide the reader with convincing arguments on why markets are inefficient.

In chapter 2, he introduces the noise trader hypothesis, based on an article he wrote in the Journal of Finace (JOF) in 1990. The problem with the noise trader hypothesis is that it is hardly a behavioral assumption; specifically it does not explain why people make a biased decision when faced with financial uncertainty. Moreover, the noise trader hypothesis is untestable. In the end, it is just an easy way of getting around some of the existing paradoxes in finance, by assuming that people are stupid - say noisy, in Schleifer's vocabulary.

In chapter 4, he is concerned with the 'limits of arbitrage' (the original title of this paper, published in the JOF in 1997). This paper is definitely worth reading to understand the problems with hedge funds and other arbitrageurs. However, linking the limits of arbitrageurs to 'inefficiency of the market' is erroneous. The very fact that arbitrageurs can not take advantage of what they think are mispriced assets, due to collateral constraints (Schleifer's hypothesis), shows that the market is efficient, since no free money is floating around.

The other chapters can be criticized in similar ways. E.g. chapter 3 on closed end funds has been criticized by his peers (Merton Miller, a.o.) in the JOF of 1993.

Nevertheless, this book is worth reading, since most of the articles Schleifer has written have been influential, placed in their proper context. However, anybody who thinks that he's getting a good introduction to either inefficient market theory or behavioral finance, will feel deceived at the end of this book.

Chapter 1 lays out the two pillars to his argument: a theory of investor sentiment, and limits to arbitrage. Both are needed, as without systematic deviations from rationality, irrational decisions cancel each other out, making them somewhat uninteresting (excepting increasing volume); without limits to arbitrage such irrational deviations from `true value' are instantly poached by savvy rational investors.

The first model shows that if arbitrage is limited and noise traders have systematic biases, prices can deviate from fundamental value (DeLong et al, 1990a). That is, Shell can deviate from its fundamental value because no one has enough money and time to put the price into equilibrium, and investor sentiment varies between markets and over time. This model also has the interesting implication that less informed investors might earn a higher rate of return on their total portfolios because they irrationally believe they have a more favorable risk-return opportunity and hence invest in securities with a higher return. In effect, their stupidity effectively diminishes their risk aversion, and in the long run allows a lucky few of them to reap the financial rewards that would accrue to the less risk averse (one could call it the `Forrest Gump' effect). As opposed to speculation weeding out the irrational traders and making only the best opinions matter, the irrational can dominate.

The closed fund puzzle is presented in chapter three and highlights some of the problems of this approach (Lee, Thaler and Shleifer, 1991). The issue to be explained is why 1) funds are issued at premiums to net asset value (overpriced) and 2) funds eventually trade at discount to net asset value (underpriced). While the underpricing is addressed through the mechanism outlined in the first model (limited arbitrage and noise traders), the overpricing effect is addressed by assuming that `noise traders' buy up the initial issuance-not very subtle. Clearly if noise traders can be foisted into overpaying or underpaying within models by assumption, it is hard to avoid the inference that this approach can explain everything and thus nothing.

Of course on occasion EMH proponents also try to explain seemingly everything, where exceptions are assumed order-statistics until they are granted statistical significance, at which time they are instantly seen as an efficient proxy of unmeasured risk (e.g., the value and size effect). Yet explanatory greediness is clearly more of a problem to the inefficient markets camp. For example, in addition to the above example, investor sentiment is used to explain both the equity-premium puzzle (i.e., why stock prices are too low on average) and why recent p/e multiples are too high (page 180): if the equity-premium is a `puzzle' it is difficult to also say that our currently historically high p/e multiple is irrational, but if the p/e ratio `should' be lower (around 15) then the equity premium is not a puzzle.

In chapter 4 a nice approach is taken towards professional arbitrage (Shleifer and Vishny, 1997). By modeling it as a principal agent problem, this model captures some relevant issues usually addressed by the Industrial Organization literature. Clearly there is relevance to modeling the situation where hedge fund managers have uncertain skill and investors have to evaluate them. The failure of famed hedge fund LTCM in 1998 was defended, like almost all bankruptcies, as a failure of investor's patience--outsiders are always much quicker at pulling the plug than insiders would prefer. Modeling, in this case, a liquidity constraint, is a highly relevant issue that seems well suited for asymmetric information and principal-agent modeling.

Chapter 5 introduces the model of investor sentiment, that is, why we should expect noise traders to vary systematically in their buy or sell orders (Barberis, Shleifer, and Vishny, 1998). It derives a straightforward and testable hypothesis based on Bayesian updating of a regime-switching model. For earnings or other surprises that continue a trend, overreaction is predicted, for surprises that counter a trend, underreaction is predicted.

In chapter 6, we see the DeLong, Shleifer, Summers, and Waldeman series on noise traders and positive feedback loops (DSSW, 1990b). This sort of model bothers me because it is a bit disingenuous. It puts superficial rigor onto to the simple idea that "given constraints on arbitrage, irrational trend-following investors can make it rational to follow trends, and thus rational traders can be destabilizing." It is not a compelling model because the results are not derived inevitably and subtly from general assumptions and a friction, but instead from assumptions which guarantee the result (e.g., trend-following noise traders and limited arbitrage). Is it at all helpful to take a straightforward idea that can be clearly expressed in a sentence and model this with contrived algebra? Personally I do not think so, though the realist in me understands that without a model to point to, the idea would not be taken as seriously as it has.

It is stressed throughout the book that risky arbitrage makes taking advantage of pervasive irrationality difficult. Yet if irrationality is a systemic and pervasive phenomenon, then there exist hundreds of scenarios like mispriced Shell, overvalued Amazon, undepriced closed funds, overvalued currencies, overvalued IPOs, etc. Surely over several years these positions, somewhat independent, should make significant abnormal risk-adjusted returns. As is more probable, there are not hundreds of such situations, but perhaps a handful, and with the rational markets assumption ignored hundreds more opportunities appear to exist but actually do not (e.g., the small firm effect). Be sure to monitor Thaler's funds (UBRLX and UBVLX) and Sheifer's fund (LSVEX).

The material in this book is necessary for anyone who is responsible for managing money or involved with a business entity doing trading in financial markets. Increasingly this is "everyone."

The good news is that the conclusions and punchlines are presented clearly and simply. A person who does not want to follow the math (there is a lot of it) can skip to the arguments and conclusions and get an enormous amount of valuable information. The bad news is that little effort has been expended to make the math attractive. A person wishing to slog through the math will have to be prepared to sit down with pencil & paper & patience. Some editing by someone who cared would have made this book much more attractive to the average student. Martin Baxter & Andrew Rennie's "Financial Calculus" gives a good example of how this can be done.

I found this less interesting than I had hoped. It seemed written for economists, not the intelligent reading public. The book spends little time introducing accessible economic puzzles that might interest the general reader. Instead, it spends its time refuting established economic principles using vocabulary and phrases from economic academia.

I wish the book had proposed some alternative economic principles to replace those the author attacks. I don't quibble with the arguments, they just didn't have much impact.

For a better intro to behavioral finance, try Thaler's 'The Winner's Curse.' For something more positive, check out 'The Economy as an Evolving Complex System.'

Inefficient Markets by Harvard economist Andrei Shleifer provides a strong argument against the Efficient Market Hypothesis (EMH) in its various forms and an introduction to Behavioral Finance. Shleifer's main points are summarized below.

1. The EMH comes in three forms. The Weak Form states that an investor can not achieve returns above the market averages based on the analysis of historical stock price patterns (Technical Analysis). The Semi-Strong Form states that all publicly available news is reflected in stock prices almost instantaneously and that an investor can not beat the market averages by diligently tracking company earnings and other events (Fundamental Analysis). Finally, the Strong Form says that an investor can not beat the market even by using information that is not available to the public (Insider Trading). The Strong Form can be dismissed by considering the number of corporate executives currently under indictment or serving time for insider trading. Evidence against the Semi-Strong and Weak Forms can be found in the Small Stock Effect (small stocks outperform the market) and January Effect (the market does best in January) which seemed to hold until they were widely publicized but have presumably been negated since then by arbitrage. Additional evidence against the EMH can be found in the less than perfect correlation between the price movements of Royal Dutch and Shell Transport and Trading shares which jointly own the Royal Dutch Shell enterprise in a fixed 60%/40% ratio. Furthermore, the prevalence of a 10% to 20% discount in the share price of closed end funds relative to their net asset values suggests that the market is less than efficient.

2. In Chapters 2-4, Shleifer demonstrates the limits of arbitrage in maintaining efficient markets. He develops a mathematical model for predicting the returns of arbitrageurs (who accurately perceive the values of stocks) and noise traders (who incorrectly perceive the same values). His Noise Trader Model explains how noise traders can sometimes achieve higher returns than arbitrageurs based on the "hold more" and "create space" effects. The "hold more" effect is based on the community of noise traders egging each other on as was seen in the technology bubble that burst in 2000. The "create space" effect says that the wider the range of incorrect perceptions held by noise traders, the less effective arbitrageurs will be in bring stock prices back to their correct values. Shleifer uses the Noise Trader Model to make additional predictions about the market behavior of closed end funds and shows that, unlike the EMH, it accurately models such phenomena as the rise in share price to the underlying net asset value upon liquidation or reorganization as an open end fund. Finally, he shows that professional arbitrageurs, such as hedge fund operators, are forced to adopt more conservative tactics than individual arbitrageurs by their need to retain clients and funding.

3. In Chapters 5 and 6, Shleifer develops a model of Investor Sentiment based on investors' patterns of psychological underreaction and overreaction. Investors tend to underreact to new information (such as reported earnings) by modifying their perception of a stock's value by less that the new information would suggest and continuing to extrapolate the old stock price trend. If confronted with repeated inputs of new information that consistently points in the same direction, investors tend to overreact by discarding the old model, accepting the recent trend as the new model, and extrapolating it into the future. Finally, he shows how investor sentiments can form a positive feedback trading environment in which arbitrage can actually destabilize the market.

This is a book for serious students of finance. It's not a "Behavioral Finance for Dummies". However, the math does not require more than a year of calculus and a good understanding of calculus-based probability and statistics. Shleifer's writing style is remarkably clear for an academic economist (many of whom I find able to obfuscate the simplest concepts). Overall, Inefficient Markets is a long-overdue reexamination of the theoretical underpinnings of modern finance theory.

As has been admitted by even the staunchest former proponents of financial economics (such as Burton Malkiel), the multi-decades old dominant intellectual field in academic finance has piled up against itself persistent anomalous data. Thus, it is no surprise, as the science of economics advanced, that a new intellectual field would develop to challenge and replace the old. Behavioral finance, which relaxes some of the key assumptions in financial economics, utilizes survey data, and integrates knowledge from psychology to better understand financial markets, is that new intellectual field.

Although still controversial, young economists and financial professionals should become versed in this new field as early as possible: 1) because there is huge room for new research where creative economists can flex their muscle and 2) financial professionals that drop the old adherence to financial economics will have an edge over those that don't. Andrei Shleifer's work is the best introductory work on behavioral finance that I've come across, and I thus strongly recommend it to those who want a quick and easy to understand introduction to this field which is the wave of the future of academic finance (well, I hope).

Robert Stephenson-Padron

MSc student (economics & finance)

University of Navarra, Spain

There is too much maths in the book. However, the comments and interpretation on various models are very interesting. The authour distinguish between arbitrageurs and noise traders. He also give us a theory of substituability which is interesting but inapplicable in reality. Too much theory also with a lot of hypothesis that are not respected in real markets.
I was looking more for a book on investment psychology and I was disappointed.

Beneath my comments, writes Roseblatt:

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In chapter 4, he is concerned with the 'limits of arbitrage' (the original title of this paper, published in the JOF in 1997). This paper is definitely worth reading to understand the problems with hedge funds and other arbitrageurs. However, linking the limits of arbitrageurs to 'inefficiency of the market' is erroneous. The very fact that arbitrageurs can not take advantage of what they think are mispriced assets, due to collateral constraints (Schleifer's hypothesis), shows that the market is efficient, since no free money is floating around.

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No, with the 'limits of arbitrage', the mispricing cannot be corrected quickly. The logic lies like this: suppose A sees the arbitrage opportunity, but due to the "limit", he can only trade certain shares, and makes $10 FREE MONEY. He knows there is still another $10 "on the table", but he cannot take action anymore. At this point, if no one else sees this arbitrage opportunity, the market remains inefficient until another arbitrageir B jumps in and remove that "remaining $10 on the table". Of course A and B made "free money". Shleifer's treatment is perfectly Okay. Roseblatt's rebuttal is illogic and obviously wrong.

However, EMH itself is a hoax. It is not scientific at all. I believe in the early days, financial economics was dominated by people who had little quantitative or science training, therefore, they could only do something like EMH sort of soft libral arts type of research. To me, it is not EMH, it is how quickly the information gets reflected in stock prices; and how big is the limit of arbitrage. Nothing else. I suggest the new generation of high-level finance researchers should totally discard this spurious EMH topic. Period.

Markets are not efficient in part because Investor Sentiment is a strong factor creating momentum (either upward or downward trend, whether sentiment is positive or negative). Also, arbitrage is very weak, as there are no proper securities substitutes, shorting the indexes is too risky. The "Noise Trader Risk" is too great. Meaning equity values may continue to diverge long enough for the arbitrageurs to loose their shirt betting on convergence. The investor type is a very important characteristic to factor. This explains the close end fund puzzle. The discount on closed end fund tracks the fate of small cap stocks. When small cap stocks do poorly, the discount on closed end funds deepens. This is because both investments are dominated by the same type of investors: individuals - small investors. Thus, both investment types are subject to small investors' sentiments.