Equilibrium stock return dynamics under alternative rules of learning about hidden states

Abstract

We examine the properties of equilibrium stock returns in an economy in which agents need to learn the hidden state of the endowment process. We consider Bayesian and suboptimal learning rules, including near-rational learning, conservatism, representativeness, optimism, and pessimism. Bayesian learning produces realistic variation in the conditional equity risk premium, return volatility, and Sharpe ratio. Alternative learning behaviors alter significantly the level and variation of the conditional return moments. However, when agents are allowed to be conscious of their learning mistakes and to price assets accordingly, the properties of returns under Bayesian and alternative learning rules are virtually indistinguishable.