Examining the History of Speculative Frenzies

From the infamous Dutch tulip hysteria of the 17th century to the Great Depression to the dot-com crash, wild bouts of speculation have occasionally inflated financial bubbles that "soaked investors when they burst". Numerous economists and sociologists have attempted to explain these phenomena using the standard finance theory of expected utility, which has been accepted as a normative model of rational choice2, and widely applied as a descriptive model of economic behaviour.

Kahneman and Tversky

However, with the emergence of behavioral finance, the psychological and sociological issues that impact the decision-making process of individuals, groups and organizations are also investigated.

Kahneman and Tversky (as part of the revolutionary study of behavioural fiance , in the paper Prospect Theory: An Analysis of Decision Under Risk) has attributed market manias partly to investors' ''illusion of control.''

Kahneman explained the basics of the dot-com bomb to The Financial Times: ''A high percentage of investors knew it was a bubble and still invested because they thought they could get out in time.

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'' Why did so few heed the alarms? Overview Prospect theory allows one to describe how people make choices in situations where they have to decide between alternatives that involve risk. Starting from empirical evidence, the theory describes how individuals evaluate potential losses and gains.

This theory holds that there are persistent biases motivated by psychological factors that influence people's choices under conditions of uncertainty. Prospect theory considers preferences as a function of "decision weights", and it assumes that these weights do not always match with probabilities.

The theory describes the decision making processes as consisting of two stages, editing and evaluating. In the editing, possible outcomes and of the decision are ordered following some heuristic.

In particular, people decide which outcomes they see as basically identical and they set a reference point and consider lower outcomes as losses and larger ones as gains. In evaluation, people behave as if they would compute a value (utility) based on the potential outcomes and their respective probabilities, and then choose the alternative characterized by the highest utility. Graphically, the value function is

1. defined on deviations from the reference point;
2. generally concave for gains and concave for losses;
3. steeper for losses than for gains.

The S-shape value function is is asymmetric, which implies given the same variation in absolute value, there is a bigger impact of losses than of gains. In contrast to "Expected Utility Theory" (which focuses on the expectations of investor benefits), it measures losses and gains, but not absolute wealth. The function w is called a probability weighting function and expresses that people tend to overreact to small probability events, but under react to medium and large probabilities.

Evaluation and Analysis of Prospect Theory

Although Prospect Theory has become a defining moment in behavioral finance, there have been numerous critiques, questions and the evolution of this theory which arise due to the indefinite nature of investor behavior. Thus, in order to have a greater understanding of the prospect theory and its significance to investor decisions, the development of the theory and its criticisms must be closely examined. Critiques: It has been argued that Prospect Theory do not consider that the decision maker's choice and preferences are revised internally or externally in a series of sensitivity analysis before decisions are made.

It also does not analyse nor show what causes such sensitivity analysis process and how the process affect and determine final decisions. This is due to the fact that Prospect Theory were derived from experiments that did not involve studies of effects of sensitivity analysis. Furthermore, in some financial environments, decisions and processes are automated, and apart from this initial level of decisions, the interactions between human beings and automated decision systems produce another level of decision making, which cannot be analysed by Prospect Theory.

This is primarily because in automated decision systems, "preferences" and "prospects" are replaced by predetermined sensitivity analyses and parameters that are typically not subject to human whims and changes in perception. Therefore it is conceivable that in many automated decision systems, risk will not be stated in terms of probabilities or weights, but in terms of ranges of outcomes and distributions of outcomes.