Bayes vs. Resampling: A Rematch

Abstract

We replay an investment game that compares the performance of a player using Bayesian methods for determining portfolio weights with a player that uses the Monte Carlo based resampling approach advocated in Michaud (1998). Markowitz and Usmen (2003) showed that the Michaud player always won. However, in the original experiment, the Bayes player was handicapped because the algorithm that was used to evaluate the predictive distribution of the portfolio provided only a rough approximation. We level the playing field by allowing the Bayes player to use a more standard algorithm. Our results sharply contrast with those of the original game. The final part of our paper proposes a new investment game that is much more relevant for the average investor - a one-period ahead asset allocation. For this game, the Bayes player always wins.

Keywords: Bayesian decision problem, parameter uncertainty, optimal portfolios, utility function maximization, resampling

JEL Classification: G11, G12, C11