Evolutionary Selection of Individual Expectations and Aggregate Outcomes in Asset Pricing Experiments

In recent `learning to forecast' experiments with human subjects (Hommes, et al. 2005), three different patterns in aggregate price behavior have been observed: slow monotonic convergence, permanent oscillations and dampened fluctuations. We show that a simple model of individual learning can explain these different aggregate outcomes within the same experimental setting. The key idea of the model is the evolutionary selection among heterogeneous expectation rules, driven by the relative performance of the rules. Out-of-sample predictive power of our switching model is higher compared to the rational or other homogeneous expectations benchmarks. Our results show that heterogeneity in expectations is crucial to describe individual forecasting behavior as well as aggregate price behavior.