Implications of Heuristics in Financial Decision Making

Abstract

Rationality is the only factor that differentiates us as "Homo Economicus‟ from Homo sapiens. It is on which theories and concepts of economics and finance are built upon. Neoclassical economics stresses that people are rational in making decisions. That is why rational preferences are assumed to be complete and transitive. Most important foundation work on this aspect "expected utility theory‟ was developed by John Van Neuman and Oskar Mor Genstern in 1944 gives a conceptual framework of how people should act in making decisions. But empirical evidence has proved that people are not always rational in considering all the available information on an opportunity before deciding on it. After all, theory is a way to explain an observation. The most important breakthrough came in the year 1979 by Daniel Kahneman and Amos Tversky, who came out with a theory opposite to expected utility theory, called "Prospect Theory‟. Prospect theory's "value function‟ is analogs to the utility function of expected utility theory. Another key building block of prospect theory is the use of decision weights rather than probabilities. Practically, it is not possible to have a decision maker with unlimited cerebral RAM, who would consider all relevant information and come up with a choice under all circumstances. Ease of processing and information overloaded leads to Heuristics or Shortcuts. There are two types of heuristics. Type 1 is autonomic, non-cognitive, economize on effort and Type 2 is purely cognitive in nature. In a series of articles, Daniel Kahneman and Amos Tversky identified three key heuristics named Representativeness, Availability, Anchoring. This paper studies the impact of demographic variables in determining these heuristics and also the level of impact of heuristics on investment performance. In addition to the three heuristic variables mentioned overconfidence and Gambler‟s fallacy are also included in the study. Data are collected through questionnaires. The collected data then analyzed using statistical tools like ANOVA and Regression to know the relationship and impact of the Heuristics.

Keywords: Behavioral Bias, Heuristics, Overconfidence, Gambler's Fallacy, Representativeness, Availability