Abstract
ABSTRACTResearch in both economics and psychology suggests that when agents predict the next value of a random series they frequently exhibit two types of biases, which are called the gambler's fallacy (GF) and the hot hand fallacy (HHF). The GF is to expect a negative correlation in a process that is in fact random. The HHF is more or less the opposite of this—to believe that another heads is more likely after a run of heads. The evidence for these fallacies comes largely from situations where they are not punished (lotteries, casinos, and laboratory experiments with random returns). In many real-world situations, such as in financial markets, succumbing to fallacies is costly, which gives an incentive to overcome them. The present study is based on high-frequency data from a market maker in the foreign exchange market. Trading behavior is only partly explained by the rational exploitation of past patterns in the data. There is also evidence of the GF: a tendency to sell the dollar after it has risen persistently or strongly.
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