Explaining the Facts with Adaptive Agents: The Case of Mutual Fund Flows

Abstract

This paper studies portfolio decisions of boundedly rational agents in a financial market. Learning is modelled via a genetic algorithm. Learning as modelled in this paper leads agents to hold too much risk as compared to the optimal portfolio of rational investors. Moreover, learning agent exhibit an asymmetric response after positve and negative returns where the portfolio adjustment is more pronounced after negative returns. It is demonstrated that investors in mutual funds show the same investment patterns as the learning agents in the model. A steady-state version of the model is able to match the mutual fund data closely.

JEL Classification: G11