Parameter Uncertainty in Asset Allocation
Campbell R. Harvey
Duke University - Fuqua School of Business; National Bureau of Economic Research (NBER)
Pennsylvania State University, University Park
Merrill W. Liechty
Drexel University - Department of Decision Sciences
December 17, 2009
We revisit an investment experiment that compares the performance of an investor using Bayesian methods for determining portfolio weights with an investor that uses the Monte Carlo based resampling approach advocated in Michaud (1998). Markowitz and Usmen (2003) showed that the Michaud investor always won. However, in the original experiment, the Bayes investor 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 investor to use a more standard algorithm. Our results sharply contrast with those of the original experiment. The final part of our paper proposes a new investment experiment that is much more relevant for the average investor - a one-period ahead asset allocation. We examine in detail why this is the best comparison to make, and why the Bayes investor always wins.
Number of Pages in PDF File: 46
Keywords: Bayesian decision problem, parameter uncertainty, optimal portfolios, utility function maximization, resamplingworking papers series
Date posted: December 18, 2009 ; Last revised: March 18, 2010
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