Learning to Be Overconfident

Abstract

We develop a multi-period market model describing both the process by which traders learn about their ability and how a bias in this learning can create overconfident traders. A trader in our model initially does not know his own ability, that is, the probability that he will receive a valid signal in each period. He infers this ability from his successes and failures. In assessing his ability the trader takes too much credit for his successes, i.e. he weighs his successes more heavily than would a true Bayesian agent. This leads him to become overconfident. A trader's expected level of overconfidence increases in the early stages of his career. Then, with more experience, he comes to better recognize his own ability. An overconfident trader trades too aggressively, thereby increasing trading volume and market volatility while lowering his own expected profits. Though a greater number of past successes indicates greater probable ability, a more successful trader may actually have lower expected profits in the next period than a less successful trader due to his greater overconfidence. Since overconfidence is generated by success, overconfident traders are not the poorest traders. Their survival in the market is not threatened. Overconfidence does not make traders wealthier, but the process of becoming wealthy can make traders overconfident.