The New Palgrave: A Dictionary of Economics, edited by L. Blume and S. Durlauf, Second Edition, 2007. New York: Palgrave McMillan.
The Efficient Markets Hypothesis (EMH) refers to the notion that market prices fully reflects all available information. Developed independently by Paul A. Samuelson and Eugene F. Fama in the 1960's, 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 behavioral economists who argue that the EMH is based on counterfactual assumptions regarding human behavior, i.e., rationality. Recent advances in evolutionary psychology and the cognitive neurosciences may be able to reconcile the EMH with behavioral anomalies.
Journal of Investment Consulting 7 (2005), 21–44.
The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and there is little consensus as to which side is winning or what the implications are for investment management and consulting. In this article, I review the case for and against the Efficient Markets Hypothesis, and describe a new framework—the Adaptive Markets Hypothesis—in which the traditional models of modern financial economics can co-exist alongside behavioral models in an intellectually consistent manner. Based on evolutionary principles, the Adaptive Markets Hypothesis implies that the degree of market efficiency is related to environmental factors characterizing market ecology such as the number of competitors in the market, the magnitude of profit opportunities available, and the adaptability of the market participants. Many of the examples that behavioralists cite as violations of rationality that are inconsistent with market efficiency—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, I show that the Adaptive Markets Hypothesis yields a number of surprisingly concrete applications for both investment managers and consultants.
with Dmitry V. Repin and Brett N. Steenbarger, American Economic Review 95 (2005), 352–359.
We investigate several possible links between psychological factors and trading performance in a sample of 80 anonymous day-traders. Using daily emotional-state surveys over a five-week period as well as personality inventory surveys, we construct measures of personality traits and emotional states for each subject and correlate these measures with daily normalized profits-and-losses records. We find that subjects whose emotional reaction to monetary gains and losses was more intense on both the positive and negative side exhibited significantly worse trading performance. Psychological traits derived from a standardized personality inventory survey do not reveal any specific "trader personality profile", raising the possibility that trading skills may not necessarily be innate, and that different personality types may be able to perform trading functions equally well after proper instruction and practice.
Journal of Portfolio Management 30 (2004), 15–29.
One of the most influential ideas in the past 30 years of the Journal of Portfolio Management is the Efficient Markets Hypothesis, the idea that market prices incorporate all information rationally and instantaneously. However, the emerging discipline of behavioral economics and finance has challenged this hypothesis, arguing that markets are not rational, but are driven by fear and greed instead. Recent research in the cognitive neurosciences suggests that these two perspectives are opposite sides of the same coin. In this article I propose a new framework that reconciles market efficiency with behavioral alternatives by applying the principles of evolution—competition, adaptation, and natural selection—to financial interactions. By extending Herbert Simon's notion of "satisficing'' with evolutionary dynamics, I argue that much of what behavioralists cite as counterexamples to economic rationality—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of individuals adapting to a changing environment via simple heuristics. Despite the qualitative nature of this new paradigm, the Adaptive Markets Hypothesis offers a number of surprisingly concrete implications for the practice of portfolio management.
with Dmitry V. Repin, Journal of Cognitive Neuroscience 14 (2002), 323–339.
A longstanding controversy in economics and finance is whether financial markets are governed by rational forces or by emotional responses. We study the importance of emotion in the decisionmaking process of professional securities traders by measuring their physiological characteristics, e.g., skin conductance, blood volume pulse, etc., during live trading sessions while simultaneously capturing real-time prices from which market events can be detected. In a sample of 10 traders, we find significant correlation between electrodermal responses and transient market events, and between changes in cardiovascular variables and market volatility. We also observe differences in these correlations among the 10 traders which may be systematically related to the traders' levels of experience.
Journal of Psychology and Financial Markets 3 (2002), 76–86.
In this talk, I review the implications of the recent rise and fall of the technology sector for traditional financial theories and their behavioral alternatives. Although critics of the Efficient Markets Hypothesis argue that markets are driven by fear and greed, not fundamentals, recent research in the cognitive neurosciences suggest that these two perspectives are opposite sides of the same coin. I propose a new paradigm for financial economics that focuses more on the evolutionary biology and ecology of markets rather than the more traditional physicists' view. By marrying the principles of evolution to Herbert Simon's notion of "satisficing,'' I argue that much of what behavioralists cite as counter-examples to economic rationality—loss aversion, overconfidence, overreaction, mental accounting, and other behavioral biases—are, in fact, consistent with an evolutionary model of rational agents learning to adapt to their environment via satisficing heuristics.
Journal of Indexes Q3 (2001), 26–35.
Artificial intelligence has transformed financial technology in many ways and in this review article, three of the most promising applications are discussed: neural networks, data mining, and pattern recognition. Just as indexes are meant to facilitate the summary and extraction of information in an efficient manner, sophisticated automated algorithms can now perform similar functions but at higher and more powerful levels. In some cases, artificial intelligence can save us from natural stupidity.
with Nicholas Chan, Blake LeBaron, and Tomaso Poggio
We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six different experimental designs, we investigate a number of features of our agent-based model: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the different types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several findings of human-based experimental markets, however, we also find intriguing differences between agent-based and human-based experiments.
Journal of the American Statistical Association 95 (2000), 629-635.
Ever since the publication in 1565 of Girolamo Cardano's treatise on gambling, Liber de Ludo Aleae (The Book of Games of Chance), statistics and financial markets have become inextricably linked. Over the past few decades many of these links have become part of the canon of modern finance, and it is now impossible to fully appreciate the workings of financial markets without them. This selective survey covers three of the most important ideas of finance—efficient markets, the random walk hypothesis, and derivative pricing models—that illustrate the enormous research opportunities that lie at the intersection of finance and statistics.
with J. Doyne Farmer, Proceedings of the National Academy of Sciences 96 (1999), 9991–9992.
In this review article, we explore several recent advances in the quantitative modeling of financial markets. We begin with the Efficient Markets Hypothesis and describe how this controversial idea has stimulated a number of new directions of research, some focusing on more elaborate mathematical models that are capable of rationalizing the empirical facts, others taking a completely different tack in rejecting rationality altogether. One of the most promising directions is to view financial markets from a biological perspective and, specifically, within an evolutionary framework in which markets, instruments, institutions, and investors interact and evolve dynamically according to the "law" of economic selection. Under this view, financial agents compete and adapt, but they do not necessarily do so in an optimal fashion. Evolutionary and ecological models of financial markets is truly a new frontier whose exploration has just begun.