Learning from Experience in the Stock Market

New evidence suggests that individuals "learn from experience," meaning they learn from events occurring during their own lifetimes as opposed to the entire history of events. Moreover, they weigh more heavily the more recent events compared to events occurring in the more distant past. This paper analyzes the implications of such learning for stock pricing in a model with finitely-lived agents. Individuals learn about the rate of change of the stock price and of dividends using a weighted decreasing-gain algorithm. Information is dispersed across age cohorts with older agents having larger information sets than younger ones. In the model, the stock price exhibits stochastic fluctuations around the rational expectations equilibrium due to successive waves of optimism and pessimism. We demonstrate how this heterogeneous-beliefs model can be approximated by an economy with a representative agent who updates his beliefs following a constant-gain learning scheme. The aggregate gain parameter of the approximation is a nonlinear function of the survival rate and of the individual gain parameters.