Emotional traders

conditions.³²

Furthermore, in a controversial experiment Shiv, Loewenstein, Bechara, Damasio, and Damasio (2000) find that emotionally impaired subjects are more willing to gamble for high stakes than non-impaired people, as they do not experience the unpleasant feeling of loss. Even more surprising is the fact that people with brain damages generally make better financial decisions than those with normal IQs.

In sum, these studies, although somewhat scarce, reinforce the experimental evidence and speak for the manifestation of emotions in real financial markets. Our model in Section 2.2 adopts a theoretical perspective and attempts to show the important role that emotions can play in financial decision making. In particular, we are interested in the use of emotions as an analytical toolbox employed to establish trading strategies that appear to be as good as – if not better than – rational ones, in order to ensure survival in competitive environments.

To this end, we design a market where three main categories of traders confront with each other by trading one risky asset. Two of these categories are common to market microstructure models: therational tradersand thenoise traders. The former form beliefs by combining prior and current information in a balanced Bayesian way, and attempt to maximize expected utility of wealth. The latter act randomly, driven by exogenous reasons such as liquidity needs. In addition, we introduce a new category of market participants, denoted as emotional traders, who follow their intuition in both forming beliefs with respect to future price evolutions and formulating periodical demands. Our rational and emotional traders typify the two-system logic addressed above in this section: While rational traders are characterized by the deliberate rule-based System 2, the associative and intuitional System 1 governs emotional decisions.

Although our setting is theoretical and could merely be tested by means of numerical simulations, we believe that it does not lie so far from reality. First, the three trader categories resemble real markets; There we can find professional traders who dispose of sufficient resources and motivation in order to make decisions in a way approaching the rational type, as well as trades impelled by exogenous reasons, similar to the random actions of the noise traders in our model. Moreover, some market participants may speculate on public information in an intuitive and affect-driven way. The motivation for the existence of a trader category such as our emotional traders relies on the evidence

performers seem less emotionally affected. In addition, no correlation between trader profiles and certain personality traits (such as extraversion, agreeableness, conscientiousness, neuroticism, openness) could be detected.

presented at the beginning of this section.

Second, further assumptions of our model, such as the belief formation and the general

“logic” followed by emotional traders in shaping their demands (i.e. affect and intuition), also receive support from neurobiology and psychology. For instance, the fact that beliefs form by the superposition of past and current evidence is not only a theoretical idealization that underlies the Bayes rule. Damasio (1996) argues that the acquisition of somatic-marker signaling – that we know from the above comments in this section to guide decision making – occurs in the prefrontal cortices. This area receives signals regarding existing and incoming knowledge of external world, innate biological preferences, and the changes of body states as a consequence of this knowledge and of these preferences. In other words, prior and current information stemming from both outside and inside world is combined here and generates somatic markers. Recall however that our mind does not work on real information, but on individual representations of reality. They form exactly in these prefrontal cortices and are categorized in the perspective of personal relevance. In addition, Damasio (1996) considers a matter of individuality the extent to which decision making depends on real somatic states or symbols of them. Hence, it is plausible to assume that some individuals rely more than others on their mind images of reality in forming beliefs. Also, triggering activities from the brain – specifically from “as if” body states generated in emotional areas – can unconsciously bias the cognitive processes. According to the same author this biasing can yet be for the better, as the chances of potentially negative decisions are reduced and deliberation time gained. Damasio (1996) denotes this covert mechanism as intuitionand it becomes the guiding principle of our emotional traders.

Third, in the same context of belief formation, the possibility that, in reality, some traders may overemphasize the current evidence on the account of their affect-driven be-havior is further supported by the remark in Slovic, Finucane, Peters, and MacGregor (2002) that the precision of the affective meaning of a stimulus influences the ability to use information and hence its evaluability. Thus, impressions with a more intense affec-tive load receive a higher weight in impression formation, judgment, and decision making.

Also, as previously mentioned in this section, emotions represent the key ingredient in the formation and change of beliefs.³⁹ Finally, the case when emotional traders can put excessive weight on past elements of beliefs is confirmed – among others – by the remark

39The relation between emotions and beliefs is exhaustively studied in Frijda, Manstead, and Bem (2000a).

in Sloman (2002) that reasoning can be affected by the so called “belief bias”. Specifi-cally, a-priori formed beliefs can inhibit logic responses that account for current evidence.

These two opposed tendencies to over- or underweight prior relative to current evidence correspond further to the heuristics ofrepresentativenessandconservatism, respectively.⁴⁰ They both become frequently manifest in real decision situations.⁴¹ Note that, since rep-resentativeness stands for an extensive concept with multiple manifestations, we will use in Section 2.2 the denomination of impulsiveness for the specific manifestation of rep-resentativeness which is the neglect of prior relative to current information. Also, as noted by Kirchler and Maciejovsky (2002), both heuristics can be used simultaneously in real decision situations, depending on the framing of the decision problem (see Hoffrage (2004)). In other words, putting the same situation in a different light can make the same person to act either overconfidently or conservatively.

As discussed by Slovic, Finucane, Peters, and MacGregor (2002), relying on emotions can have negative consequences. This occurs either in response to unknown stimuli, for which the experiential system is not prepared, or when other people try to manipulate affective reactions. The former situation receives support in our hypothetical setting:

When an excessive emphasis is put on new evidence, emotional traders are mostly worse off than their rational peers (and even than pure noise traders). This is reasonable to expect when, for instance, emotional traders are confronted with totally new situations, for which they have developed no intuition, so that “following their noses” cannot be very helpful. However, we do not account for manipulative actions, as our rational traders do not attempt to change perceptions; They simply adapt to them, while the rest of the traders manifest no concern with other groups’ actions.

40The representativeness is one of the three basic heuristics of Kahneman and Tversky’s program.

According to Tversky and Kahneman (1992), it occurs when judgments of probability are replaced by judgments of similarity, so that objects are ascribed to categories on the basis of the correspondence in terms of few – and often irrelevant – features of the respective category. In particular, Tversky and Kahneman (1974) argue that representativeness entails insensitivity to prior outcomes, to sample size, to predictability, misconception of chance, of regression, and illusion of validity.

41Teigen (2004) notes that the representativeness heuristic can be easily employed due to its minimal requirements concerning the amount of cognitive resources. Moreover, it applies to a wide range of situations where the objective probabilities cannot be calculated, and mostly offers the correct result for simple problems. Therefore, in practice, people often tend to rely on representativeness instead of probability judgements. In contrast, Wallsten (1972) argues that conservative people tend to misinterpret information, to aggregate it in a wrong way or to manifest response biases against the use of very small or very large probabilities.