Bayes vs. Resampling: A Rematch
Pennsylvania State University, University Park
Campbell R. Harvey
Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER); Duke Innovation & Entrepreneurship Initiative
Merrill W. Liechty
Drexel University - Department of Decision Sciences
Journal of Investment Management, Vol. 6 No. 1, First Quarter 2008
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.
Number of Pages in PDF File: 36
Keywords: Bayesian decision problem, parameter uncertainty, optimal portfolios, utility function maximization, resampling
JEL Classification: G11, G12, C11
Date posted: April 26, 2006
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