Efficient Markets Hypothesis: A Critique and Alternative Perspectives, Lecture notes of Economics

Download Efficient Markets Hypothesis: A Critique and Alternative Perspectives and more Lecture notes Economics in PDF only on Docsity! 1 EFFICIENT MARKETS HYPOTHESIS Andrew W. Lo To appear in L. Blume and S. Durlauf, The New Palgrave: A Dictionary of Economics, Second Edition, 2007. New York: Palgrave McMillan. The efficient markets hypothesis (EMH) maintains that market prices fully reflect all available information. Developed independently by Paul A. Samuelson and Eugene F. Fama in the 1960s, this idea has been applied extensively to theoretical models and empirical studies of financial securities prices, generating considerable controversy as well as fundamental insights into the price-discovery process. The most enduring critique comes from psychologists and behavioural economists who argue that the EMH is based on counterfactual assumptions regarding human behaviour, that is, rationality. Recent advances in evolutionary psychology and the cognitive neurosciences may be able to reconcile the EMH with behavioural anomalies. There is an old joke, widely told among economists, about an economist strolling down the street with a companion. They come upon a $100 bill lying on the ground, and as the companion reaches down to pick it up, the economist says, ‘Don’t bother – if it were a genuine $100 bill, someone would have already picked it up’. This humorous example of economic logic gone awry is a fairly accurate rendition of the efficient markets hypothesis (EMH), one of the most hotly contested propositions in all the social sciences. It is disarmingly simple to state, has far-reaching consequences for academic theories and business practice, and yet is surprisingly resilient to empirical proof or refutation. Even after several decades of research and literally thousands of published studies, economists have not yet reached a consensus about whether markets – particularly financial markets – are, in fact, efficient. The origins of the EMH can be traced back to the work of two individuals in the 1960s: Eugene F. Fama and Paul A. Samuelson. Remarkably, they independently developed the same basic notion of market efficiency from two rather different research agendas. These differences would propel the them along two distinct trajectories leading to several other breakthroughs and milestones, all originating from their point of intersection, the EMH. 2 Like so many ideas of modern economics, the EMH was first given form by Paul Samuelson (1965), whose contribution is neatly summarized by the title of his article: ‘Proof that Properly Anticipated Prices Fluctuate Randomly’. In an informationally efficient market, price changes must be unforecastable if they are properly anticipated, that is, if they fully incorporate the information and expectations of all market participants. Having developed a series of linear-programming solutions to spatial pricing models with no uncertainty, Samuelson came upon the idea of efficient markets through his interest in temporal pricing models of storable commodities that are harvested and subject to decay. Samuelson’s abiding interest in the mechanics and kinematics of prices, with and without uncertainty, led him and his students to several fruitful research agendas including solutions for the dynamic asset- allocation and consumption-savings problem, the fallacy of time diversification and log- optimal investment policies, warrant and option-pricing analysis and, ultimately, the Black and Scholes (1973) and Merton (1973) option-pricing models. In contrast to Samuelson’s path to the EMH, Fama’s (1963; 1965a; 1965b, 1970) seminal papers were based on his interest in measuring the statistical properties of stock prices, and in resolving the debate between technical analysis (the use of geometric patterns in price and volume charts to forecast future price movements of a security) and fundamental analysis (the use of accounting and economic data to determine a security’s fair value). Among the first to employ modern digital computers to conduct empirical research in finance, and the first to use the term ‘efficient markets’ (Fama, 1965b), Fama operationalized the EMH hypothesis – summarized compactly in the epigram ‘prices fully reflect all available information’ – by placing structure on various information sets available to market participants. Fama’s fascination with empirical analysis led him and his students down a very different path from Samuelson’s, yielding significant methodological and empirical contributions such as the event study, numerous econometric tests of single- and multi-factor linear asset-pricing models, and a host of empirical regularities and anomalies in stock, bond, currency and commodity markets. The EMH’s concept of informational efficiency has a Zen-like, counter-intuitive flavour to it: the more efficient the market, the more random the sequence of price changes generated by such a market, and the most efficient market of all is one in which price changes are completely random and unpredictable. This is not an accident of nature, but is in fact the direct result of many active market participants attempting to profit from their information. Driven by profit opportunities, an army of investors pounce on even the smallest informational advantages at their disposal, and in doing so they incorporate their information 5 Variance bounds tests Another set of empirical tests of the EMH starts with the observation that in a world without uncertainty the market price of a share of common stock must equal the present value of all future dividends, discounted at the appropriate cost of capital. In an uncertain world, one can generalize this dividend-discount model or present-value relation in the natural way: the market price equals the conditional expectation of the present value of all future dividends, discounted at the appropriate risk-adjusted cost of capital, and conditional on all available information. This generalization is explicitly developed by Grossman and Shiller (1981). LeRoy and Porter (1981) and Shiller (1981) take this as their starting point in comparing the variance of stock market prices to the variance of ex post present values of future dividends. If the market price is the conditional expectation of present values, then the difference between the two, that is, the forecast error, must be uncorrelated with the conditional expectation by construction. But this implies that the variance of the ex post present value is the sum of the variance of the market price (the conditional expectation) and the variance of the forecast error. Since volatilities are always non-negative, this variance decomposition implies that the variance of stock prices cannot exceed the variance of ex post present values. Using annual US stock market data from various sample periods, LeRoy and Porter (1981) and Shiller (1981) find that the variance bound is violated dramatically. Although LeRoy and Porter are more circumspect about the implications of such violations, Shiller concludes that stock market prices are too volatile and the EMH must be false. These two papers ignited a flurry of responses which challenged Shiller’s controversial conclusion on a number of fronts. For example, Flavin (1983), Kleidon (1986), and Marsh and Merton (1986) show that statistical inference is rather delicate for these variance bounds, and that, even if they hold in theory, for the kind of sample sizes Shiller uses and under plausible data-generating processes the sample variance bound is often violated purely due to sampling variation. These issues are well summarized in Gilles and LeRoy (1991) and Merton (1987). More importantly, on purely theoretical grounds Marsh and Merton (1986) and Michener (1982) provide two explanations for violations of variance bounds that are perfectly consistent with the EMH. Marsh and Merton (1986) show that if managers smooth dividends – a well-known empirical phenomenon documented in several studies of dividend policy – and if earnings follow a geometric random walk, then the variance bound is violated in theory, in which case the empirical violations may be interpreted as support for this version of the EMH. 6 Alternatively, Michener constructs a simple dynamic equilibrium model along the lines of Lucas (1978) in which prices do fully reflect all available information at all times but where individuals are risk averse, and this risk aversion is enough to cause the variance bound to be violated in theory as well. These findings highlight an important aspect of the EMH that had not been emphasized in earlier studies: tests of the EMH are always tests of joint hypotheses. In particular, the phrase ‘prices fully reflect all available information’ is a statement about two distinct aspects of prices: the information content and the price formation mechanism. Therefore, any test of this proposition must concern the kind of information reflected in prices, and how this information comes to be reflected in prices. Apart from issues regarding statistical inference, the empirical violation of variance bounds may be interpreted in many ways. It may be a violation of EMH, or a sign that investors are risk averse, or a symptom of dividend smoothing. To choose among these alternatives, more evidence is required. Overreaction and underreaction A common explanation for departures from the EMH is that investors do not always react in proper proportion to new information. For example, in some cases investors may overreact to performance, selling stocks that have experienced recent losses or buying stocks that have enjoyed recent gains. Such overreaction tends to push prices beyond their ‘fair’ or ‘rational’ market value, only to have rational investors take the other side of the trades and bring prices back in line eventually. An implication of this phenomenon is price reversals: what goes up must come down, and vice versa. Another implication is that contrarian investment strategies – strategies in which ‘losers’ are purchased and ‘winners’ are sold – will earn superior returns. Both of these implications were tested and confirmed using recent US stock market data. For example, using monthly returns of New York Stock Exchange (NYSE) stocks from 1926 to 1982, DeBondt and Thaler (1985) document the fact that the winners and losers in one 36-month period tend to reverse their performance over the next 36-month period. Curiously, many of these reversals occur in January (see the discussion below on the ‘January effect’). Chopra, Lakonishok and Ritter (1992) reconfirm these findings after correcting for market risk and the size effect. And Lehmann (1990) shows that a zero-net-investment strategy in which long positions in losers are financed by short positions in winners almost always yields positive returns for monthly NYSE/AMEX stock returns data from 1962 to 7 1985. However, Chan (1988) argues that the profitability of contrarian investment strategies cannot be taken as conclusive evidence against the EMH because there is typically no accounting for risk in these profitability calculations (although Chopra, Lakonishok and Ritter, 1992 do provide risk adjustments, their focus was not on specific trading strategies). By risk-adjusting the returns of a contrarian trading strategy according to the capital asset pricing model, Chan (1988) shows that the expected returns are consistent with the EMH. Moreover, Lo and MacKinlay (1990c) show that at least half of the profits reported by Lehmann (1990) are not due to overreaction but rather the result of positive cross- autocorrelations between stocks. For example, suppose the returns of two stocks A and B are both serially uncorrelated but are positively cross-autocorrelated. The lack of serial correlation implies no overreaction (which is characterized by negative serial correlation), but positive cross-autocorrelations yields positive expected returns to contrarian trading strategies. The existence of several economic rationales for positive cross-autocorrelation that are consistent with EMH suggests that the profitability of contrarian trading strategies is not sufficient evidence to conclude that investors overreact. The reaction of market participants to information contained in earnings announcements also has implications for the EMH. In one of the earliest studies of the information content of earnings, Ball and Brown (1968) show that up to 80 per cent of the information contained in the earnings ‘surprises’ is anticipated by market prices. However, the more recent article by Bernard and Thomas (1990) argues that investors sometimes underreact to information about future earnings contained in current earnings. This is related to the ‘post-earnings announcement drift’ puzzle first documented by Ball and Brown (1968), in which the information contained in earnings announcement takes several days to become fully impounded into market prices. Although such effects are indeed troubling for the EMH, their economic significance is often questionable – while they may violate the EMH in frictionless markets, very often even the smallest frictions – for example, positive trading costs, taxes – can eliminate the profits from trading strategies designed to exploit them. Anomalies Perhaps the most common challenge to the EMH is the anomaly, a regular pattern in an asset’s returns which is reliable, widely known, and inexplicable. The fact that the pattern is regular and reliable implies a degree of predictability, and the fact that the regularity is 10 opportunities, A and B: A yields a sure profit of $240,000, and B is a lottery ticket yielding $1 million with a 25 per cent probability and $0 with 75 per cent probability. If you had to choose between A and B, which would you prefer? Investment B has an expected value of $250,000, which is higher than A’s payoff, but this may not be all that meaningful to you because you will receive either $1 million or zero. Clearly, there is no right or wrong choice here; it is simply a matter of personal preferences. Faced with this choice, most subjects prefer A, the sure profit, to B, despite the fact that B offers a significant probability of winning considerably more. This behaviour is often characterized as ‘risk aversion’ for obvious reasons. Now suppose you are faced with another two choices, C and D: C yields a sure loss of $750,000, and D is a lottery ticket yielding $0 with 25 per cent probability and a loss of $1 million with 75 per cent probability. Which would you prefer? This situation is not as absurd as it might seem at first glance; many financial decisions involve choosing between the lesser of two evils. In this case, most subjects choose D, despite the fact that D is more risky than C. When faced with two choices that both involve losses, individuals seem to be ‘risk seeking’, not risk averse as in the case of A versus B. The fact that individuals tend to be risk averse in the face of gains and risk seeking in the face of losses can lead to some very poor financial decisions. To see why, observe that the combination of choices A and D is equivalent to a single lottery ticket yielding $240,000 with 25 per cent probability and $760,000− with 75 per cent probability, whereas the combination of choices B and C is equivalent to a single lottery ticket yielding $250,000 with 25 per cent probability and $750,000− with 75 per cent probability. The B and C combination has the same probabilities of gains and losses, but the gain is $10,000 higher and the loss is $10,000 lower. In other words, B and C is formally equivalent to A and D plus a sure profit of $10,000. In light of this analysis, would you still prefer A and D? A common response to this example is that it is contrived because the two pairs of investment opportunities were presented sequentially, not simultaneously. However, in a typical global financial institution the London office may be faced with choices A and B and the Tokyo office may be faced with choices C and D. Locally, it may seem as if there is no right or wrong answer – the choice between A and B or C and D seems to be simply a matter of personal risk preferences – but the globally consolidated financial statement for the entire institution will tell a very different story. From that perspective, there is a right and wrong answer, and the empirical and experimental evidence suggests that most individuals tend to select the wrong answer. Therefore, according to the behaviouralists, quantitative models of 11 efficient markets – all of which are predicated on rational choice – are likely to be wrong as well. Impossibility of efficient markets Grossman and Stiglitz (1980) go even farther – they argue that perfectly informationally efficient markets are an impossibility for, if markets are perfectly efficient, there is no profit to gathering information, in which case there would be little reason to trade and markets would eventually collapse. Alternatively, the degree of market inefficiency determines the effort investors are willing to expend to gather and trade on information, hence a non- degenerate market equilibrium will arise only when there are sufficient profit opportunities, that is, inefficiencies, to compensate investors for the costs of trading and information gathering. The profits earned by these attentive investors may be viewed as ‘economic rents’ that accrue to those willing to engage in such activities. Who are the providers of these rents? Black (1986) gave us a provocative answer: ‘noise traders’, individuals who trade on what they consider to be information but which is, in fact, merely noise. The supporters of the EMH have responded to these challenges by arguing that, while behavioural biases and corresponding inefficiencies do exist from time to time, there is a limit to their prevalence and impact because of opposing forces dedicated to exploiting such opportunities. A simple example of such a limit is the so-called ‘Dutch book’, in which irrational probability beliefs give rise to guaranteed profits for the savvy investor. Consider, for example, an event E , defined as ‘the S&P 500 index drops by five per cent or more next Monday’, and suppose an individual has the following irrational beliefs: there is a 50 per cent probability that E will occur, and a 75 per cent probability that E will not occur. This is clearly a violation of one of the basic axioms of probability theory – the probabilities of two mutually exclusive and exhaustive events must sum to 1 – but many experimental studies have documented such violations among an overwhelming majority of human subjects. These inconsistent subjective probability beliefs imply that the individual would be willing to take both of the following bets 1B and 2B : B1 = $1 if $1 otherwise E⎧ ⎨−⎩ , B2 = $1 if $3 otherwise cE⎧ ⎨ −⎩ where cE denotes the event ‘not E ’. Now suppose we take the opposite side of both bets, placing $50 on 1B and $25 on 2B . If E occurs, we lose $50 on 1B but gain $75 on 2B , yielding a profit of $25. If cE occurs, we gain $50 on 1B and lose $25 on 2B , also yielding a 12 profit of $25. Regardless of the outcome, we have secured a profit of $25, an ‘arbitrage’ that comes at the expense of the individual with inconsistent probability beliefs. Such beliefs are not sustainable, and market forces – namely, arbitrageurs such as hedge funds and proprietary trading groups – will take advantage of these opportunities until they no longer exist, that is, until the odds are in line with the axioms of probability theory. (Only when these axioms are satisfied is arbitrage ruled out. This was conjectured by Ramsey, 1926, and proved rigorously by de Finetti, 1937, and Savage, 1954.) Therefore, proponents of the classical EMH argue that there are limits to the degree and persistence of behavioural biases such as inconsistent probability beliefs, and substantial incentives for those who can identify and exploit such occurrences. While all of us are subject to certain behavioural biases from time to time, according to EMH supporters market forces will always act to bring prices back to rational levels, implying that the impact of irrational behaviour on financial markets is generally negligible and, therefore, irrelevant. But this last conclusion relies on the assumption that market forces are sufficiently powerful to overcome any type of behavioural bias, or equivalently that irrational beliefs are not so pervasive as to overwhelm the capacity of arbitrage capital dedicated to taking advantage of such irrationalities. This is an empirical issue that cannot be settled theoretically, but must be tested through careful measurement and statistical analysis. The classic reference by Kindleberger (1989) – where a number of speculative bubbles, financial panics, manias, and market crashes are described in detail – suggests that the forces of irrationality can overwhelm the forces of arbitrage capital for months and, in several well- known cases, years. So what does this imply for the EMH? The current state of the EMH Given all of the theoretical and empirical evidence for and against the EMH, what can we conclude? Amazingly, there is still no consensus among economists. Despite the many advances in the statistical analysis, databases, and theoretical models surrounding the EMH, the main result of all of these studies is to harden the resolve of the proponents of each side of the debate. One of the reasons for this state of affairs is the fact that the EMH, by itself, is not a well-defined and empirically refutable hypothesis. To make it operational, one must specify additional structure, for example, investors’ preferences or information structure. But then a test of the EMH becomes a test of several auxiliary hypotheses as well, and a rejection of 15 financial agents compete and adapt, but they do not necessarily do so in an optimal fashion (see Farmer and Lo, 1999; Farmer, 2002; Lo, 2002; 2004; 2005). This evolutionary approach is heavily influenced by recent advances in the emerging discipline of ‘evolutionary psychology’, which builds on the seminal research of E.O. Wilson (1975) in applying the principles of competition, reproduction, and natural selection to social interactions, yielding surprisingly compelling explanations for certain kinds of human behaviour, such as altruism, fairness, kin selection, language, mate selection, religion, morality, ethics and abstract thought (see, for example, Barkow, Cosmides and Tooby, 1992; Gigerenzer, 2000). ‘Sociobiology’ is the rubric that Wilson (1975) gave to these powerful ideas, which generated a considerable degree of controversy in their own right, and the same principles can be applied to economic and financial contexts. In doing so, we can fully reconcile the EMH with all of its behavioural alternatives, leading to a new synthesis: the adaptive markets hypothesis (AMH). Students of the history of economic thought will no doubt recall that Thomas Malthus used biological arguments – the fact that populations increase at geometric rates whereas natural resources increase at only arithmetic rates – to arrive at rather dire economic consequences, and that both Darwin and Wallace were influenced by these arguments (see Hirshleifer, 1977, for further details). Also, Joseph Schumpeter’s view of business cycles, entrepreneurs and capitalism have an unmistakeable evolutionary flavour to them; in fact, his notions of ‘creative destruction’ and ‘bursts’ of entrepreneurial activity are similar in spirit to natural selection and Eldredge and Gould’s (1972) notion of ‘punctuated equilibrium’. More recently, economists and biologists have begun to explore these connections in several veins: direct extensions of sociobiology to economics (Becker, 1976; Hirshleifer, 1977); evolutionary game theory (Maynard Smith, 1982); evolutionary economics (Nelson and Winter, 1982); and economics as a complex system (Anderson, Arrow and Pines, 1988). And publications like the Journal of Evolutionary Economics and the Electronic Journal of Evolutionary Modeling and Economic Dynamics now provide a home for research at the intersection of economics and biology. Evolutionary concepts have also appeared in a number of financial contexts. For example, Luo (1995) explores the implications of natural selection for futures markets, and Hirshleifer and Luo (2001) consider the long-run prospects of overconfident traders in a competitive securities market. The literature on agent-based modelling pioneered by Arthur et al. (1997), in which interactions among software agents programmed with simple heuristics are simulated, relies heavily on evolutionary dynamics. And at least two prominent 16 practitioners have proposed Darwinian alternatives to the EMH. In a chapter titled ‘The Ecology of Markets’, Niederhoffer (1997, ch. 15) likens financial markets to an ecosystem with dealers as ‘herbivores’, speculators as ‘carnivores’, and floor traders and distressed investors as ‘decomposers’. And Bernstein (1998) makes a compelling case for active management by pointing out that the notion of equilibrium, which is central to the EMH, is rarely realized in practice and that market dynamics are better explained by evolutionary processes. Clearly the time is now ripe for an evolutionary alternative to market efficiency. To that end, in the current context of the EMH we begin, as Samuelson (1947) did, with the theory of the individual consumer. Contrary to the neoclassical postulate that individuals maximize expected utility and have rational expectations, an evolutionary perspective makes considerably more modest claims, viewing individuals as organisms that have been honed, through generations of natural selection, to maximize the survival of their genetic material (see, for example, Dawkins, 1976). While such a reductionist approach can quickly degenerate into useless generalities – for example, the molecular biology of economic behaviour – nevertheless, there are valuable insights to be gained from the broader biological perspective. Specifically, this perspective implies that behaviour is not necessarily intrinsic and exogenous, but evolves by natural selection and depends on the particular environmental through which selection occurs. That is, natural selection operates not only upon genetic material but also upon social and cultural norms in homo sapiens; hence Wilson’s term ‘sociobiology’. To operationalize this perspective within an economic context, consider the idea of ‘bounded rationality’ first espoused by Nobel-prize-winning economist Herbert Simon. Simon (1955) suggested that individuals are hardly capable of the kind of optimization that neoclassical economics calls for in the standard theory of consumer choice. Instead, he argued that, because optimization is costly and humans are naturally limited in their computational abilities, they engage in something he called ‘satisficing’, an alternative to optimization in which individuals make choices that are merely satisfactory, not necessarily optimal. In other words, individuals are bounded in their degree of rationality, which is in sharp contrast to the current orthodoxy – rational expectations – where individuals have unbounded rationality (the term ‘hyper-rational expectations’ might be more descriptive). Unfortunately, although this idea garnered a Nobel Prize for Simon, it had relatively little impact on the economics profession. (However, his work is now receiving greater attention, thanks in part to the growing behavioural literature in economics and finance. See, for example, Simon, 1982; 17 Sargent, 1993; A. Rubinstein, 1998; Gigerenzer and Selten, 2001.) Apart from the sociological factors discussed above, Simon’s framework was commonly dismissed because of one specific criticism: what determines the point at which an individual stops optimizing and reaches a satisfactory solution? If such a point is determined by the usual cost–benefit calculation underlying much of microeconomics (that is, optimize until the marginal benefits of the optimum equals the marginal cost of getting there), this assumes the optimal solution is known, which would eliminate the need for satisficing. As a result, the idea of bounded rationality fell by the wayside, and rational expectations has become the de facto standard for modelling economic behaviour under uncertainty. An evolutionary perspective provides the missing ingredient in Simon’s framework. The proper response to the question of how individuals determine the point at which their optimizing behaviour is satisfactory is this: such points are determined not analytically but through trial and error and, of course, natural selection. Individuals make choices based on past experience and their ‘best guess’ as to what might be optimal, and they learn by receiving positive or negative reinforcement from the outcomes. If they receive no such reinforcement, they do not learn. In this fashion, individuals develop heuristics to solve various economic challenges, and, as long as those challenges remain stable, the heuristics will eventually adapt to yield approximately optimal solutions to them. If, on the other hand, the environment changes, then it should come as no surprise that the heuristics of the old environment are not necessarily suited to the new. In such cases, we observe ‘behavioural biases’ – actions that are apparently ill-advised in the context in which we observe them. But rather than labelling such behaviour ‘irrational’, it should be recognized that suboptimal behaviour is not unlikely when we take heuristics out of their evolutionary context. A more accurate term for such behaviour might be ‘maladaptive’. The flopping of a fish on dry land may seem strange and unproductive, but under water the same motions are capable of propelling the fish away from its predators. By coupling Simon’s notion of bounded rationality and satisficing with evolutionary dynamics, many other aspects of economic behaviour can also be derived. Competition, cooperation, market-making behaviour, general equilibrium, and disequilibrium dynamics are all adaptations designed to address certain environmental challenges for the human species, and by viewing them through the lens of evolutionary biology we can better understand the apparent contradictions between the EMH and the presence and persistence of behavioural biases. Specifically, the adaptive markets hypothesis can be viewed as a new version of the 20 the population after suffering a certain level of losses. The new paradigm of the AMH is still under development, and certainly requires a great deal more research to render it ‘operationally meaningful’ in Samuelson’s sense. However, even at this early stage it is clear that an evolutionary framework is able to reconcile many of the apparent contradictions between efficient markets and behavioural exceptions. The former may be viewed as the steady-state limit of a population with constant environmental conditions, and the latter involves specific adaptations of certain groups that may or may not persist, depending on the particular evolutionary paths that the economy experiences. More specific implications may be derived through a combination of deductive and inductive inference – for example, theoretical analysis of evolutionary dynamics, empirical analysis of evolutionary forces in financial markets, and experimental analysis of decision-making at the individual and group level. For example, one implication is that, to the extent that a relation between risk and reward exists, it is unlikely to be stable over time. Such a relation is determined by the relative sizes and preferences of various populations in the market ecology, as well as institutional aspects such as the regulatory environment and tax laws. As these factors shift over time, any risk–reward relation is likely to be affected. A corollary of this implication is that the equity risk premium is also time-varying and path-dependent. This is not so revolutionary an idea as it might first appear – even in the context of a rational expectations equilibrium model, if risk preferences change over time, then the equity risk premium must vary too. The incremental insight of the AMH is that aggregate risk preferences are not immutable constants, but are shaped by the forces of natural selection. For example, until recently US markets were populated by a significant group of investors who had never experienced a genuine bear market – this fact has undoubtedly shaped the aggregate risk preferences of the US economy, just as the experience since the bursting of the technology bubble in the early 2000s has affected the risk preferences of the current population of investors. In this context, natural selection determines who participates in market interactions; those investors who experienced substantial losses in the technology bubble are more likely to have exited the market, leaving a markedly different population of investors. Through the forces of natural selection, history matters. Irrespective of whether prices fully reflect all available information, the particular path that market prices have taken over the past few years influences current aggregate risk preferences. Among the three fundamental components of any market equilibrium – prices, probabilities, and preferences – preferences is clearly the most fundamental and least understood. Several large bodies of research have 21 developed around these issues – in economics and finance, psychology, operations research (also called ‘decision sciences’) and, more recently, brain and cognitive sciences – and many new insights are likely to flow from synthesizing these different strands of research into a more complete understanding of how individuals make decisions (see Starmer, 2000, for an excellent review of this literature). Simon’s (1982) seminal contributions to this literature are still remarkably timely and their implications have yet to be fully explored. Conclusions Many other practical insights and potential breakthroughs can be derived from shifting our mode of thinking in financial economics from the physical to the biological sciences. Although evolutionary ideas are not yet part of the financial mainstream, the hope is that they will become more commonplace as they demonstrate their worth – ideas are also subject to ‘survival of the fittest’. No one has illustrated this principal so well as Harry Markowitz, the father of modern portfolio theory and a Nobel laureate in economics in 1990. In describing his experience as a Ph.D. student on the eve of his graduation, he wrote in his Nobel address (Markowitz, 1991, p. 476): . . . [W]hen I defended my dissertation as a student in the Economics Department of the University of Chicago, Professor Milton Friedman argued that portfolio theory was not Economics, and that they could not award me a Ph.D. degree in Economics for a dissertation which was not Economics. I assume that he was only half serious, since they did award me the degree without long debate. As to the merits of his arguments, at this point I am quite willing to concede: at the time I defended my dissertation, portfolio theory was not part of Economics. But now it is. In light of the sociology of the EMH controversy (see, for example, Lo, 2004), the debate is likely to continue. However, despite the lack of consensus in academia and industry, the ongoing dialogue has given us many new insights into the economic structure of financial markets. If, as Paul Samuelson has suggested, financial economics is the crown jewel of the social sciences, then the EMH must account for half the facets. Andrew W. Lo See also asset price anomalies; bounded rationality; financial market anomalies; information 22 economics; rational expectations I thank John Cox, Gene Fama, Bob Merton, and Paul Samuelson for helpful discussions. Bibliography Anderson, P. Arrow, K. and Pines, D.eds. 1988. The Economy as an Evolving Complex System. Reading, MA: Addison-Wesley Publishing Company. Arthur, B. Holland, J. LeBaron, B. Palmer R. and P. Tayler. 1997. Asset pricing under endogenous expectations in an artificial stock market. In The Economy as an Evolving Complex System II, ed. B. Arthur, S. Durlauf, and D. Lane Reading, MA: Addison Wesley. Ball, R. and Brown, P. 1968. 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