Checked content

Behavioural economics

Related subjects: Economics

About this schools Wikipedia selection


Behavioural economics and behavioural finance are closely related fields which apply scientific research on human and social cognitive and emotional biases to better understand economic decisions and how they affect market prices, returns and the allocation of resources. The fields are primarily concerned with the rationality, or lack thereof, of economic agents. Behavioural models typically integrate insights from psychology with neo-classical economic theory.

Academics are divided between considering Behavioural Finance as supporting some tools of technical analysis by explaining market trends, and considering some aspects of technical analysis as behavioural biases ( representativeness heuristic, self fulfilling prophecy).



During the classical period, economics had a close link with psychology. For example, Adam Smith wrote The Theory of Moral Sentiments, an important text describing psychological principles of individual behaviour; and Jeremy Bentham wrote extensively on the psychological underpinnings of utility. Economists began to distance themselves from psychology during the development of neo-classical economics as they sought to reshape the discipline as a natural science, with explanations of economic behaviour deduced from assumptions about the nature of economic agents. The concept of homo economicus was developed, and the psychology of this entity was fundamentally rational. Nevertheless, psychological explanations continued to inform the analysis of many important figures in the development of neo-classical economics such as Francis Edgeworth, Vilfredo Pareto, Irving Fisher and John Maynard Keynes.

Psychology had largely disappeared from economic discussions by the mid 20th century. A number of factors contributed to the resurgence of its use and the development of behavioural economics. Expected utility and discounted utility models began to gain wide acceptance, generating testable hypotheses about decision making under uncertainty and intertemporal consumption respectively. Soon a number of observed and repeatable anomalies challenged those hypotheses. Furthermore, during the 1960s cognitive psychology began to describe the brain as an information processing device (in contrast to behaviorist models). Psychologists in this field such as Ward Edwards, Amos Tversky and Daniel Kahneman began to compare their cognitive models of decision making under risk and uncertainty to economic models of rational behaviour. In Mathematical psychology, there is a longstanding interest in the transitivity of preference and what kind of measurement scale utility constitutes ( Luce, 2000).

An important paper in the development of the behavioural finance and economics fields was written by Kahneman and Tversky in 1979. This paper, ' Prospect theory: Decision Making Under Risk', used cognitive psychological techniques to explain a number of documented divergences of economic decision making from neo-classical theory. Over time many other psychological effects have been incorporated into behavioral finance, such as overconfidence and the effects of limited attention. Further milestones in the development of the field include a well attended and diverse conference at the University of Chicago, a special 1997 edition of the Quarterly Journal of Economics ('In Memory of Amos Tversky') devoted to the topic of behavioural economics and the award of the Nobel prize to Daniel Kahneman in 2002 "for having integrated insights from psychological research into economic science, especially concerning human judgment and decision-making under uncertainty".

Prospect theory is an example of generalized expected utility theory. Although not commonly included in discussions of the field of behavioural economics, generalized expected utility theory is similarly motivated by concerns about the descriptive inaccuracy of expected utility theory.

Behavioural economics has also been applied to problems of intertemporal choice. The most prominent idea is that of hyperbolic discounting, proposed by George Ainslie (1975), in which a high rate of discount is used between the present and the near future, and a lower rate between the near future and the far future. This pattern of discounting is dynamically inconsistent (or time-inconsistent), and therefore inconsistent with some models of rational choice, since the rate of discount between time t and t+1 will be low at time t-1, when t is the near future, but high at time t when t is the present and time t+1 the near future. As part of the discussion of hypberbolic discounting, has been animal and human work on Melioration theory and Matching Law of Richard Herrnstein. They suggest that behaviour is not based on expected utility rather it is based on previous reinforcement experience.



Key observations

There are three main themes in behavioural finance and economics:

  • Framing: The way a problem or decision is presented to the decision maker will affect his action.

Recently, Barberis, Shleifer, and Vishny (1998), as well as Daniel, Hirshleifer, and Subrahmanyam (1998) have built models based on extrapolation (seeing patterns in random sequences) and overconfidence to explain security market over- and underreactions, though such models have not been used in the money management industry. These models assume that errors or biases are correlated across agents so that they do not cancel out in aggregate. This would be the case if a large fraction of agents look at the same signal (such as the advice of an analyst) or have a common bias.

More generally, cognitive biases may also have strong anomalous effects in aggregate if there is a social contamination with a strong emotional content (collective greed or fear), leading to more widespread phenomena such as herding and groupthink. Behavioural finance and economics rests as much on social psychology within large groups as on individual psychology. However, some behavioural models explicitly demonstrate that a small but significant anomalous group can also have market-wide effects (eg. Fehr and Schmidt, 1999).

Behavioural finance topics


Among the inefficiencies described by behavioural finance, underreactions or overreactions to information are often cited, as causes of market trends and in extreme cases of bubbles and crashes). Such misreactions have been attributed to limited investor attention, overconfidence / overoptimism, and mimicry ( herding instinct) and noise trading.



Presently, some researchers in experimental finance use experimental method, e.g. creating an artificial market by some kind of simulation software to study people's decision-making process and behaviour in financial markets.

Behavioural finance models

Some financial models used in money management and asset valuation use behavioural finance parameters, for example:

  • The stock image coefficient

Criticisms of behavioural finance

Critics of behavioural finance, such as Eugene Fama, typically support the efficient market theory (though Fama may have reversed his position in recent years). They contend that behavioural finance is more a collection of anomalies than a true branch of finance and that these anomalies will eventually be priced out of the market or explained by appealing to market microstructure arguments. However, a distinction should be noted between individual biases and social biases; the former can be averaged out by the market, while the other can create feedback loops that drive the market further and further from the equilibrium of the " fair price".


Quantitative behavioural finance

Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioural biases in conjunction with valuation. Some of this endeavor has been lead by Gunduz Caginalp (Professor of Mathematics and Editor of Journal of Behavioural Finance during 2001-2004) and collaborators including Vernon Smith (2002 Nobel Laureate in Economics), David Porter, Don Balenovich, Vladimira Ilieva, Ahmet Duran, Huseyin Merdan). Studies by Jeff Madura, Ray Sturm and others have demonstrated significant behavioural effects in stocks and exchange traded funds.

The research can be grouped into the following areas:

  1. Empirical studies that demonstrate significant deviations from classical theories
  2. Modeling using the concepts of behavioural effects together with the non-classical assumption of the finiteness of assets
  3. Forecasting based on these methods

Critical conclusions of behavioural economics


Others note that cognitive theories, such as prospect theory, are models of decision making, not generalized economic behaviour, and are only applicable to the sort of once-off decision problems presented to experiment participants or survey respondents.

Traditional economists are also skeptical of the experimental and survey based techniques which are used extensively in behavioural economics. Economists typically stress revealed preferences over stated preferences (from surveys) in the determination of economic value. Experiments and surveys must be designed carefully to avoid systemic biases, strategic behaviour and lack of incentive compatibility, and many economists are distrustful of results obtained in this manner due to the difficulty of eliminating these problems.

Rabin (1998) dismisses these criticisms, claiming that results are typically reproduced in various situations and countries and can lead to good theoretical insight. Behavioural economists have also incorporated these criticisms by focusing on field studies rather than lab experiments. Some economists look at this split as a fundamental schism between experimental economics and behavioral economics, but prominent behavioral and experimental economists tend to overlap techniques and approaches in answering common questions. For example, many prominent behavioural economists are actively investigating neuroeconomics, which is entirely experimental and cannot be verified in the field.

Other proponents of behavioral economics note that neoclassical models often fail to predict outcomes in real world contexts. Behavioral insights can be used to update neoclassical equations, and behavioural economists note that these revised models not only reach the same correct predictions as the traditional models, but also correctly predict some outcomes where the traditional models failed.

Retrieved from ""