Testing Adaptive Expectations Models of a Continuous Double Auction Market against Empirical Facts

It is well known that empirical financial time series data exhibit long memory phenomena: the behaviour of the market at various times in the past continues to exert an influence in the present. One explanation for these phenomena is that they result from a process of social learning in which poorly performing agents switch their strategy to that of other agents who appear to be more successful. We test this explanation using an agent-based model and we find that the stability of the model is directly related to the dynamics of the learning process; models in which learning converges to a stationary steady state fail to produce realistic time series data. In contrast, models in which learning leads to dynamic switching behaviour in the steady state are able to reproduce the long memory phenomena. We demonstrate that a model which incorporates contrarian trading strategies results in more dynamic behaviour in steady state, and hence is able to produce more realistic results.