The behavior of risky asset prices in a symmetric least squares learning environment

Abstract

This paper examines the dynamics of asset prices in a heterogeneous market. Traders are made up of learners who possess limited information and use limited models for predicting the future. The market also includes “noise” traders in the sense of Black, along with liquidity traders. Learners revise their prediction equations using least squares learning as defined by Marcet and Sargent. We derive the equilibrium price process and show how convergence is obtained. The price process is shown to have a number of interesting properties that are consistent with propositions outlined by Black. Numerical calculations for several examples illuminate how learning takes place in the model.