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
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.