Heuristics: Stock and Anchoring Heuristic Refers

Topics: Stock, Heuristics, Daniel Kahneman Pages: 2 (462 words) Published: September 28, 2013
Heuristics are simple and efficient rules which people often use to form judgements and make decisions. It usually involves focusing on one aspect of a complex problem and ignoring others. This can lead people to be bias, ignore logic or rational choice theory. Heuristics can be categorized as:

Representativeness
Representativeness heuristic refers to the behaviour when people look for a pattern in a series of random events. It often lead people to stereotype and draw conclusions based on a few indicators where the results can be quite different.

For example in a stock market, people will often make decisions by comparing stocks of a similar industry. If companies in the industry have an overall poor performance, people will judge the companies as unexciting and have no prospects. Thus, any stocks of that industry will be assumed to be of poor performance. This may not be true and this stereotyping of information will lead to errors when other relevant factors are not considered.

Anchoring
Anchoring heuristic refers to the tendency to accept and rely on the first piece of information received before making a decision. That first piece of information forms the initial judgement and sets the tone for everything that follows.

For example, people tend to look at past records when dealing with financial instruments in the stock market. Information such as the industry average or past quarter results are often looked at first and this is where the anchoring of information sets in. Judgement has already been passed and this can lead to inaccurate predictions. The anchoring basically overrides any potential information that may be more relevant in making decisions. Thus, resulting in poorly made decisions.

Adjustment bias
Adjustment bias and anchoring are closely related. Values are often estimated by adjusting the initial values till a final value is reached. Hence, adjustments are often bias towards the initial values.

An example will be dealing...
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