A Simulation Approach to Dynamic Portfolio Choice with an Application to Learning about Return Predictibility

Michael W. Brandt, Amit Goyal, Pedro Santa-Clara, Jonathan R. Stroud

Duke University, Emory University, UCLA, and University of Pennsylvania

May 2005

We present a simulation-based method for solving discrete-time portfolio choice problems involving non-standard preferences, a large number of assets with arbitrary return distribution, and, most importantly, a large number of state variables with potentially path-dependent or non-stationary dynamics. The method is flexible enough to accommodate intermediate consumption, portfolio constraints, parameter and model uncertainty, and learning. We first establish the properties of the method for the portfolio choice between a stock index and cash when the stock returns are either iid or predictable by the dividend yield. We then explore the problem of an investor who takes into account the predictability of returns but is uncertain about the parameters of the data generating process. The investor chooses the portfolio anticipating that future data realizations will contain useful information to learn about the true parameter values.

The manuscript is available in PDF format.