Basic Cognitive Biases

Cognition refers to any kind of knowledge or opin­ion about oneself on the world. Two cognitions can be either relevant or irrelevant to one another. If they are relevant, then they must be consonant or dissonant (i.e.

1. Heuristics: Heuristics, or rules of thumb, make decision-making easier. But they can sometimes lead to biases, especially when things change. These can lead to suboptimal investment decisions. When faced with N choices for how to invest retirement money, many people allocate using the 1/N rule. If there are three funds, one-third goes into each. If two are stock funds, two-thirds goes into equities. If one of the three is a stock fund, one-third goes into equities (Ritter, 2003, p. 431). In 2001, Benartzi and Thaler showed that some investors follow the 1/N rule when dealing with complex investment plans (Benartzi & Thaler, 2001, p.96).

2. Loss Aversion, Mental Accounting and Framing: As described above, prospect theory shows an abrupt change of the slope at the reference point which leads to a large asymmetry between the value that people give to gains and the value they give to losses. In other words, for people, and consequently for investors, the displeasure that they feel from a loss is greater than the pleasure they feel from a gain. In their 1991 paper, Tversky and Kahneman suggested that in the domain of money, people value a loss roughly twice as much as an equally sized gain (p.1060). This asymmetry in valuation is called loss aversion (Ritter, 2003, p. 431).

One of the leading studies on loss aversion is by Samuelson in his 1963 paper, in which he il­lustrated loss aversion.

Mental accounting is the set of cognitive operations used by individuals and households to organize, evaluate and keep track of financial activities (Thaler, 1999, p.183).

The existence of myopic loss aversion is not only due to loss aversion; it is also tied to another element: mental accounting. Mental accounting is constituted by a wide range of cognitive processes related to the way people analyze and treat the results of transactions and other financial events. Mental accounting is also related to framing. A frame can be defined as the form used to describe a decision problem. It is the decision maker’s subjective conception of the acts, outcomes and contingencies associated with a particular choice. The frame that a decision maker adopts is controlled partially by the formulation of the problem and by the norms, habits, and personal characteristics of the decision maker.

In sum, there are three interrelated elements of mental accounting. The first is the way incomes are framed or experienced. For example, individuals give different weights to money according to the way it is obtained. Money obtained by hard work is more valued than money obtained from the lot­tery, because the latter is considered unexpected and costless (Cornicello, 2004, p. 24).

Another psychological inclination among individuals is to catalogue events into different mental accounts based on superficial attributes. An idea underlying mental accounting is that deci­sion makers have a psychological inclination to separate different types of gambles into different accounts, and to make decisions according to the prospect theory of each account, ignoring the pos­sible interactions among them (Cornicello, 2004, p. 24). This can have the effect ofpeople arbitrarily dividing their investments into separate accounts. For example, many people have a household budget for food, and a household budget for entertaining. At home, where the food budget is present, they will not eat lobster or shrimp because they are much more expensive than fish casserole. But in a restaurant, they will order lobster and shrimp even though the cost is much higher than a simple fish dinner. If they instead ate lobster and shrimp at home, and the simple fish in a restaurant, they could save money.

Finally, the last psychological inclination among individuals is related to the length of time in which mental accounts are evaluated. This could suggest, for example, that people who “balance” their account every week, as opposed to people who balance their account every month, are more likely to spend money won in a lottery during the same week than in the subsequent ones. As time passes, the effect decreases (Cornicello, 2004, p. 24).

Loss aversion is related to other elements, one of which is the so-called endowment effect. The endowment effect was first identified by Thaler in his work dated 1980. Thaler used the term to refer to the finding that randomly assigned owners of an object appear to value the object more than randomly assigned non-owners of the object. In sum people value an object more once their prop­erty right to it has been established (Kahneman, et al., 1991, p.194). Thus, as a result of endowment effect, people assign more weight to a loss than to a foregone gain, and are influenced by loss aver­sion for this reason (Ritter, 2003, p. 433).

Loss aversion makes people suffer a so-called status quo bias. This implies that individuals tend to prefer the status quo to changes which involve losses of some goods, even when these losses are offset by gains of other goods. A status quo bias can also exist without the presence of loss aversion. This can be due to the costs of thinking, transaction costs, or psychological commitments to prior choices. The existence of these two biases are represented well in prospect theory (Corni- cello, 2004, p. 28-29).

3. Overconfidence: The concept of overconfi­

dence is based on a large body of evidence from cognitive psychological experiments and surveys showing that individuals -men are more overconfident than women- over­estimate their abilities and estimations, as well as the precision of their information.

It is clear that there is a link between rational­ity and overconfidence. Many economists would agree that their definition of rationality should not be taken too literally. According to their definition, individuals have an unlimited ability to observe and process information. In real life, individuals have limited processing abilities, and hence use ambiguous rules to translate the information they receive from the environment into estimates of cash flows and firm valuations. For example, inves­tors -- especially individual investors - could not incorporate the news about antitrust proceedings against Microsoft into concrete views about the future competitiveness of the industry, and how this, in turn, would affect Microsoft’s future cash flows. Instead, they did much of their analysis based on “hunches” or “feelings,” which can be easily influenced by behavioral biases (Daniel & Titman, 2000, p. 4).

Overconfidence is one of the most strongly doc­umented behavioral biases in behavioral finance literature. DeBondt and Thaler found that stocks that experience extremely good performance over a three to five year period tend to be outperformed by prior “losers” during the subsequent three to five years (DeBondt & Thaler, 1985, p. 793; Fama, 1998, p. 285). And they explained these results by investors’ overreaction. They pointed out that investors believe great performance in the past is a proxy for great performance in the future, and as a result investors bid up the prices of past winners without thinking that firms can’t grow forever.

In their summary of the micro foundations of behavioral finance, DeBondt and Thaler (1994) stated that “perhaps the most robust finding in the psychology of judgment is that people are overconfident.” Entrepreneurs, managers, invest­ment bankers, and market professionals, -such as security analysts and economic forecasters- can all exhibit overconfidence bias. Moreover, some evidence suggests that experts tend to be affected more by overconfidence bias than relatively inex­perienced individuals (DeBondt & Thaler,1995, p.24; Rodriguez, 2002, p. 4).

Experimental evidence also suggests that the degree of overconfidence varies according to the situation an individual faces. Overconfidence is generally stronger for more diffuse tasks for which feedback is slow, such as making diagnoses of illnesses, as opposed to more mechanical tasks which provide immediate and conclusive outcome feedback, such as weather forecasting, horserace handicapping, or solving arithmetic problems. While there are clear disadvantages associated with overconfidence, there are also offsetting benefits which suggest that, overconfidence may increase an individual’s chances of passing on their genes. Evolutionary theories suggest that individuals who appear to be the strongest and the smartest are more likely to attract women and reproduce. For similar reasons, being confident may enhance short-term economic survival. Even in the money markets, where results are easy to measure and reward, assuming the past investment performance equal, portfolio managers who appear more confident will attract clients more. The ability to act as smart and strong is therefore a survival psychological inclina­tion which provides a comparative advantage to individuals with overstated opinions of themselves (Daniel & Titman, 2000, p. 4).

In stock exchange markets; evidence indicates that reactions to new information are generally asymmetrical and industry related in transactions. There is a strong reaction to bad news about stocks that performed well in the past, while the reaction to bad news about stocks that performed badly in the past is relatively small. This is seen as important in behavioral explanations of the value anomaly since when past winning stocks are subject to a larger response to negative surprises, they tend to be more volatile than past losing stocks, thereby contradicting the rational relationship between risk and expected return. This shows that investors generally fail to judge correctly when dealing with uncertain outcomes. Overreaction to bad earnings announcements of past winners and under-reaction to good earnings announcements from past losers can be identified as a trading pattern. The failure of investors to alter their beliefs about certain stocks, and to foresee that a good earnings signal from a past losing stock is a sign of more earnings to come, creates opportunities for certain investment strategies, such as the momentum and contrarian, to achieve above average results (Andrikopoulos, 2007, p. 63).

Consequently, an important point in this theory is that individuals can better fool others about the strength of their abilities if they can first fool themselves. At this point, it should not be forgot­ten that financial markets are interactive. In other words, self-confident individuals will appear to be more competent than individuals who are insecure about their own abilities. As a result, individuals who successfully filter information in ways that add to their self-confidence may, in theory, be more successful than individuals who always interpret information rationally (Daniel & Titman, 2000, p. 5).

4. Representativeness vs. Conservatism: People tend to underweight long-term hap­penings and put too much weight on recent experience. This is sometimes known as the “law of small numbers.” For example, when equity returns have been high for many years, many people begin to believe that high equity returns are “normal.”

Representativeness could be defined as; people tend to judge the probability of an event by finding a ‘comparable known’ event and assuming that the probabilities will be similar. Representative­ness bias was first identified by Kahneman and Tversky (1972, p.430). If things change, people tend to be slow in picking up on the changes. In other words, they anchor onto the ways things have normally been. According to conservatism bias people are slow to adapt to new conditions (Shiller 2002, p.4). The conservatism bias is at war with the representativeness bias. If things change, investors will underreact as a result of conservatism bias. But if there is a long enough pattern, they will adjust and possibly overreact, underweighting the long-term average (Ritter, 2003, p. 433).

5. Disposition Effect: The disposition effect refers to the pattern of people are reluctant in realizing paper losses and hasty in real­izing paper gains. Acoording to Shefrin and Statman people sell winners to early and hold loosers to much. And they named this bias as disposition effect.(Shefrin and Statman,1985 p.777) For example, if some­one buys a stock at $30 whose value drops to $22 before rising to $28, most people will not want to sell until the stock rises above $30. The disposition effect evidences itself in lots of small gains being realized, and a few small losses. In fact, people treat as if they are trying to maximize their taxes. However rational investor neither should be so reluc­tant about losses nor so hasty about gains. It is also possible to find evidence for the disposition effect in aggregate stock trading volume. In a stock market, trading volume tends to grow during a bull market,. If the market then turns back, trading volume tends to fall. The sharp drop of the Japanese stock market by over 80% from the late 1980s to the mid 1990s can be given as an example. The fact that volume tends to fall in bear markets results a decrease in the commis­sions of stock brokerage houses. And this means a high level of systematic risk. As an example, in the U.S., aggregate stock market volume has not dropped since the beginning of the bear market in April 2000. This may be due to increased trading by institutions, since stock trading by individuals has in fact declined. The significant drop in transaction costs associated with the move to decimal­ization and technological advances partly account for this (Ritter, 2003, p. 433).

6. Herding: Do investors “flock together,” or “herd,” when they trade securities? Do some investors follow the lead of others when they trade? (Wermers, 1999, p. 581).

According to Nofsinger and Sias herding can be defined as a group of investors trading in the same direction over a period of time (Nofsinger & Sias, 1999, p. 2263). Herd behavior occurs when many people make the same action in order to mimic the behavior of others. The reason why people’s judgments are similar is partially due to the fact that people react similarly to the same informa­tion. At the same time, the social environment also has a strong influence on people’s judgment. When an individual’s judgement clashes with the judgement of a large group, the individual tends to change his judgement to fit that of the crowd. Because he simply thinks that all the other people could not be wrong, and as a consequence, the propensity of the crowd affects the individual’s decision making process. The individual accepts the large group’s judgement rather than facing the fear of expressing a contrary opinion in front of the group. Psychologists have demonstrated the existence of herding behavior in several experi­ments (Cornicello, 2004, p. 33).

Even if people are completely rational, herding behavior can still exist. People may participate in herding behavior when they take into account the judgment of others, even if they know that everyone else is behaving in a herd-like manner. This results in group behavior that can be defined as irrational since it arises from an information cascade. The information cascade may appear when individuals overweight the signals from the crowd and ignore, or underweight, their private information. Accordingly, they mimic the crowd (Kim & Nofsinger, 2005, p. 239).

Furthermore, when talking about herding and the effect of the crowd upon the individual’s decision-making process, the effect of word-of- mouth enthusiasm should be noted. Word-of- mouth enthusiasm accelerates the effect of herding behavior over the market (Shiller, 2002, p. 14). People generally trust their friends, relatives, and colleagues. Consequently, their suggestions can influence a wide range of individual decisions, including financial decisions. Talking with other people about buying opportunities can have an important influence on investment decisions. Besides, the media has the power to influence the individual’s decisions, but with less power than word-of-mouth (Cornicello, 2004, p. 34).