Behavioral economics

Behavioral economics and behavioral 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. Behavioral models typically integrate insights from psychology with neo-classical economic theory. Behavioral Finance has become the theoretical basis for technical analysis.^[1]

Behavioral analyses are mostly concerned with the effects of market decisions, but also those of public choice, another source of economic decisions with some similar biases.

History

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 behavior; 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 behavior 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 behavioral 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 behavior. In Mathematical psychology, there is a longstanding interest in the transitivity of preference and what kind of measurement scale utility constitutes (Luce, 2000).

Perhaps the most important paper in the development of the behavioral 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. Further milestones in the development of the field include a well attended and diverse conference at the University of Chicago (see Hogarth & Reder, 1987), a special 1997 edition of the Quarterly Journal of Economics ('In Memory of Amos Tversky') devoted to the topic of behavioral 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 behavioral economics, generalized expected utility theory is similarly motivated by concerns about the descriptive inaccuracy of expected utility theory.

Behavioral economics has also been applied to problems of intertemporal choice. The most prominent idea is that of hyperbolic discounting, 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 behavior is not based on expected utility of on just previous reinforcement experience.

Methodology

At the outset behavioral economics and finance theories were developed almost exclusively from experimental observations and survey responses, though in more recent times real world data has taken a more prominent position. fMRI has also been used to determine which areas of the brain are active during various steps of economic decision making. Experiments simulating market situations such as stock market trading and auctions are seen as particularly useful as they can be used to isolate the effect of a particular bias upon behavior; observed market behavior can typically be explained in a number of ways, carefully designed experiments can help narrow the range of plausible explanations. Experiments are designed to be incentive compatible, with binding transactions involving real money the norm.

Key observations

There are three main themes in behavioral finance and economics (Shefrin, 2002):

Heuristics: People often make decisions based on approximate rules of thumb, not strictly rational analyses. See also cognitive biases and bounded rationality.
Framing: The way a problem or decision is presented to the decision maker will affect his action.
Market inefficiencies: There are explanations for observed market outcomes that are contrary to rational expectations and market efficiency. These include mispricings, non-rational decision making, and return anomalies. Richard Thaler, in particular, has described specific market anomalies from a behavioral perspective.

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. Behavioral finance and economics rests as much on social psychology within large groups as on individual psychology. However, some behavioral models explicitly demonstrate that a small but significant anomalous group can also have market-wide effects (eg. Fehr and Schmidt, 1999).

Behavioral finance topics

Key observations made in behavioral finance literature include the lack of symmetry between decisions to acquire or keep resources, called colloquially the "bird in the bush" paradox, and the strong loss aversion or regret attached to any decision where some emotionally valued resources (e.g. a home) might be totally lost. Loss aversion appears to manifest itself in investor behavior as an unwillingness to sell shares or other equity, if doing so would force the trader to realise a nominal loss (Genesove & Mayer, 2001). It may also help explain why housing market prices do not adjust downwards to market clearing levels during periods of low demand.

Applying a version of prospect theory, Benartzi and Thaler (1995) claim to have solved the equity premium puzzle, something conventional finance models have been unable to do.

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 behavior in financial markets.

Behavioural finance models

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

Thaler's model of price reactions to information, with three phases, underreaction-adjustment-overreaction, creating a price trend

One characteristic of overreaction is that the average return of asset prices following a series of announcements of good news is lower than the average return following a series of bad announcements. In other words, overreaction occurs if the market reacts too strongly or for too long (persistent trend) to news that it subsequently needs to be compensated in the opposite direction. As a result, assets that were winners in the past should not be seen as an indication to invest in as their risk adjusted returns in the future are relatively low compared to stocks that were defined as losers in the past.

The stock image coefficient

Criticisms of behavioral finance

Critics of behavioral finance, such as Eugene Fama, typically support the efficient market theory (though Fama may have reversed his position in recent years). They contend that behavioral 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".

A specific example of this criticism is found in some attempted explanations of the equity premium puzzle. It is argued that the puzzle simply arises due to entry barriers (both practical and psychological) which have traditionally impeded entry by individuals into the stock market, and that returns between stocks and bonds should stabilize as electronic resources open up the stock market to a greater number of traders (See Freeman, 2004 for a review). In reply, others contend that most personal investment funds are managed through superannuation funds, so the effect of these putative barriers to entry would be minimal. In addition, professional investors and fund managers seem to hold more bonds than one would expect given return differentials.

Quantitative behavioral finance

Quantitative behavioral finance is a new discipline that uses mathematical and statistical methodology to understand behavioral biases in conjunction with valuation. Some of this endeavor has been lead by Gunduz Caginalp (Professor of Mathematics and Editor of Journal of Behavioral 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 behavioral 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 behavioral effects together with the non-classical assumption of the finiteness of assets.
3. Forecasting based on these methods.
4. Studies of experimental asset markets and use of models to forecast experiments.

Behavioral economics topics

Models in behavioral economics are typically addressed to a particular observed market anomaly and modify standard neo-classical models by describing decision makers as using heuristics and being affected by framing effects. In general, economics sits within the neoclassical framework, though the standard assumption of rational behaviour is often challenged.

Critical conclusions of behavioral economics

Critics of behavioral economics typically stress the rationality of economic agents (see Myagkov and Plott (1997) amongst others). They contend that experimentally observed behavior is inapplicable to market situations, as learning opportunities and competition will ensure at least a close approximation of rational behavior.

Others note that cognitive theories, such as prospect theory, are models of decision making, not generalized economic behavior, 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 behavioral 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 behavior 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. Behavioral 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 behavioral 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 behavioral 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.^{[verification needed]}

Key figures in behavioral economics

Key scholars in behavioral finance

References

^ Kirkpatrick, Charles D.; Dahlquist, Julie R. (2007). Technical Analysis, The Complete Resource for Market Technicians. pp. p. 49. {{cite book}}: |pages= has extra text (help)CS1 maint: multiple names: authors list (link)

Camerer, C. F.; Loewenstein, G. & Rabin, R. (eds.) (2003) Advances in Behavioral Economics
Barberis, N.; A. Shleifer; R. Vishny (1998) ``A Model of Investor Sentiment Journal of Financial Economics 49, 307-343.
Daniel, K.; D. Hirshleifer; A. Subrahmanyam, (1998) ``Investor Psychology and Security Market Over- and Underreactions Journal of Finance 53, 1839-1885.
Lawrence A. Cunningham, Behavioral Finance and Investor Governance, 59 Washington & Lee Law Review (2002)
Kahneman, D. & Tversky, A. 'Prospect Theory: An Analysis of Decision under Risk,' Econometrica, XVLII (1979), 263–291
Kirkpatrick, Charles D.; Dahlquist, Julie R. (2007) Technical Analysis, The Complete Resource for Financial Market Technicians
Luce, R Duncan (2000). Utility of Gains and Losses: Measurement-theoretical and Experimental Approaches. Lawrence Erlbaum Publishers, Mahwah, New Jersey.
Rabin, Matthew; 'Psychology and Economics,' Journal of Economic Literature, American Economic Association, vol. 36(1), pages 11-46, March 1998.
Shefrin, Hersh (2002) Beyond Greed and Fear: Understanding behavioral finance and the psychology of investing. Oxford Universtity Press
Shleifer, Andrei (1999) Inefficient Markets: An Introduction to Behavioral Finance, Oxford University Press
Shlomo Benartzi; Richard H. Thaler 'Myopic Loss Aversion and the Equity Premium Puzzle' (1995) The Quarterly Journal of Economics, Vol. 110, No. 1.

External links

[1] Kirkpatrick, Charles D.; Dahlquist, Julie R. (2007). Technical Analysis, The Complete Resource for Market Technicians. pp. p. 49. {{cite book}}: |pages= has extra text (help)CS1 maint: multiple names: authors list (link)

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