behavioural-finance - Behavioural Finance Martin Sewell...

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Behavioural Finance Martin Sewell University of Cambridge February 2007 (revised April 2010) Abstract An introduction to behavioural finance, including a review of the major works and a summary of important heuristics. 1 Introduction Behavioural finance is the study of the influence of psychology on the behaviour of financial practitioners and the subsequent effect on markets. Behavioural finance is of interest because it helps explain why and how markets might be inefficient. For more information on behavioural finance, see Sewell (2001). 2 History Back in 1896, Gustave le Bon wrote The Crowd: A Study of the Popular Mind , one of the greatest and most influential books of social psychology ever written (le Bon 1896). Selden (1912) wrote Psychology of the Stock Market . He based the book ‘upon the belief that the movements of prices on the exchanges are dependent to a very considerable degree on the mental attitude of the investing and trading public’. In 1956 the US psychologist Leon Festinger introduced a new concept in social psychology: the theory of cognitive dissonance (Festinger, Riecken and Schachter 1956). When two simultaneously held cognitions are inconsistent, this will produce a state of cognitive dissonance. Because the experience of dissonance is unpleasant, the person will strive to reduce it by changing their beliefs. Pratt (1964) considers utility functions, risk aversion and also risks consid- ered as a proportion of total assets. Tversky and Kahneman (1973) introduced the availability heuristic: ‘a judg- mental heuristic in which a person evaluates the frequency of classes or the prob- ability of events by availability, i.e. by the ease with which relevant instances come to mind.’ The reliance on the availability heuristic leads to systematic biases. 1
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In 1974, two brilliant psychologists, Amos Tversky and Daniel Kahneman, described three heuristics that are employed when making judgments under uncertainty (Tversky and Kahneman 1974): representativeness When people are asked to judge the probability that an object or event A belongs to class or process B, probabilities are evaluated by the degree to which A is representative of B, that is, by the degree to which A resembles B. availability When people are asked to assess the frequency of a class or the probability of an event, they do so by the ease with which instances or occurrences can be brought to mind. anchoring and adjustment In numerical prediction, when a relevant value (an anchor) is available, people make estimates by starting from an initial value (the anchor) that is adjusted to yield the final answer. The anchor may be suggested by the formulation of the problem, or it may be the result of a partial computation. In either case, adjustments are typically insufficient.
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