Historical accounts of financial crises suggest that fear and greed are the common denominators of these disruptive events: periods of unchecked greed eventually lead to excessive leverage and unsustainable asset-price levels, and the inevitable collapse results in unbridled fear, which must subside before any recovery is possible. The cognitive neurosciences may provide some new insights into this boom/bust pattern through a deeper understanding of the dynamics of emotion and human behavior. In this chapter, I describe some recent research from the neurosciences literature on fear and reward learning, mirror neurons, theory of mind, and the link between emotion and rational behavior. By exploring the neuroscientific basis of cognition and behavior, we may be able to identify more fundamental drivers of financial crises, and improve our models and methods for dealing with them.Download (PDF) >
In the wake of the financial crisis of 2007–2009, investors, financial advisers, portfolio managers, and regulators are still at a loss as to how to make sense of its repercussions and where to turn for guidance. The traditional paradigms of modern portfolio theory and the efficient markets hypothesis (EMH) seem woefully inadequate, but simply acknowledging that investor behavior may be irrational is cold comfort to individuals who must decide how to allocate their assets among increasingly erratic and uncertain investment alternatives. The reason for this current state of confusion and its strange dynamics is straightforward: Many market participants are now questioning the broad framework in which their financial decisions are being made. Without a clear and credible narrative of what happened, how it happened, why it happened, whether it can happen again, and what to do about it, their only response is to react instinctively to the most current crisis, which is a sure recipe for financial ruin. In this article, I describe how the adaptive markets hypothesis—an alternative to the EMH that reconciles the apparent contradiction between behavioral biases and the difficulty of outperforming passive investment vehicles—can make sense of both the current market turmoil and the emergence and popularity of the EMH in the decades leading up to the recent crisis.Download (PDF) >
We propose to study market efficiency from a computational viewpoint. Borrowing from theoretical computer science, we define a market to be efficient with respect to resources S (e.g., time, memory) if no strategy using resources S can make a profit. As a first step, we consider memory-m strategies whose action at time t depends only on the m previous observations at times t – m,…t – 1. We introduce and study a simple model of market evolution, where strategies impact the market by their decisions to buy or sell. We show that the effect of optimal strategies using memory m can lead to “market conditions” that were not present initially, such as (1) market bubbles and (2) the possibility for a strategy using memory m′ > m to make a bigger profit than was initially possible. We suggest ours as a framework to rationalize the technological arms race of quantitative trading firms.Download (PDF) >
We propose a single evolutionary explanation for the origin of several behaviors that have been observed in organisms ranging from ants to human subjects, including risk-sensitive foraging, risk aversion, loss aversion, probability matching, randomization, and diversification. Given an initial population of individuals, each assigned a purely arbitrary behavior with respect to a binary choice problem, and assuming that offspring behave identically to their parents, only those behaviors linked to reproductive success will survive, and less reproductively successful behaviors will disappear at exponential rates. When the uncertainty in reproductive success is systematic, natural selection yields behaviors that may be individually sub-optimal but are optimal from the population perspective; when reproductive uncertainty is idiosyncratic, the individual and population perspectives coincide. This framework generates a surprisingly rich set of behaviors, and the simplicity and generality of our model suggest that these derived behaviors are primitive and nearly universal within and across species.Download (PDF) >
The battle between proponents of the Efficient Markets Hypothesis and champions of behavioral finance has never been more pitched, and little consensus exists as to which side is winning or the implications 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 coexist 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.Download (PDF) >
The rationality of financial markets has been one of the most hotly contested issues in the history of modern financial economics. In this paper, we extend the findings by Lo and Repin (2002) and investigate the role of emotional mechanisms in financial decision-making using a different sample of subjects and a different method for gauging emotional response. In particular, we recruited 80 volunteers from a five-week on-line training program for day-traders offered by Linda Bradford Raschke, a well known professional futures trader (see J. Schwager, 1994). Subjects were asked to fill out surveys that recorded their psychological profiles before and after their training program, and during the course of the program (involving live trading through their own personal accounts) subjects were asked to fill out surveys at the end of each trading day which were designed to measure their emotional state and their trading performance for that day. We find a clear link between emotional reactivity and trading performance as measured by normalized profits-and-losses.Download (PDF) >
In this article, the current state of the controversy surrounding the Efficient Markets Hypothesis (EMH) is reviewed and a new perspective that reconciles the two opposing schools of thought is proposed. The proposed reconciliation, which is called the Adaptive Markets Hypothesis (AMH), is based on an evolutionary approach to economic interactions, as well as some recent research in the cognitive neurosciences that has been transforming and revitalizing the intersection of psychology and economics. Although some of these ideas have not yet been fully articulated within a rigorous quantitative framework, long time students of the EMH and seasoned practitioners will no doubt recognize immediately the possibilities generated by this new perspective. Only time will tell whether its potential will be fulfilled.Download (PDF) >
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 motion in the decision-making 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 statistically significant differences in mean electrodermal responses during transient market events relative to no-event control periods, and statistically significant mean changes in cardiovascular variables during periods of heightened market volatility relative to normal-volatility control periods. We also observe significant differences in these physiological responses across the 10 traders that may be systematically related to the traders’ levels of experience.Download (PDF) >
Most economic theories are based on the premise that individuals maximize their own self-interest and correctly incorporate the structure of their environment into all decisions, thanks to human intelligence. The influence of this paradigm goes far beyond academia—it underlies current macroeconomic and monetary policies, and is also an integral part of existing financial regulations. However, there is mounting empirical and experimental evidence, including the recent financial crisis, suggesting that humans do not always behave rationally, but often make seemingly random and suboptimal decisions.
Here we propose to reconcile these contradictory perspectives by developing a simple binary-choice model that takes evolutionary consequences of decisions into account as well as the role of intelligence, which we define as any ability of an individual to increase its genetic success. If no intelligence is present, our model produces results consistent with prior literature and shows that risks that are independent across individuals in a generation generally lead to risk-neutral behaviors, but that risks that are correlated across a generation can lead to behaviors such as risk aversion, loss aversion, probability matching, and randomization. When intelligence is present the nature of risk also matters, and we show that even when risks are independent, either risk-neutral behavior or probability matching will occur depending upon the cost of intelligence in terms of reproductive success. In the case of correlated risks, we derive an implicit formula that shows how intelligence can emerge via selection, why it may be bounded, and how such bounds typically imply the coexistence of multiple levels and types of intelligence as a reflection of varying environmental conditions.
Rational economic behavior in which individuals maximize their own self interest is only one of many possible types of behavior that arise from natural selection. The key to understanding which types of behavior are more likely to survive is how behavior affects reproductive success in a given population’s environment. From this perspective, intelligence is naturally defined as behavior that increases the probability of reproductive success, and bounds on rationality are determined by physiological and environmental constraints.Available Here >
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.Download (PDF) >