Research 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 gambler's fallacy is to expect a negative correlation in a process which is in fact random. The hot hands fallacy 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 behaviour is only partly explained by the rational exploitation of past patterns in the data, but there is also evidence of the gambler's fallacy: a tendency to sell the dollar after it has risen persistently or strongly.
Gambler's Fallacy Hot hand Fallacy Foreign Exchange Market