Working Paper: NBER ID: w10934
Authors: Michael W. Brandt; Amit Goyal; Pedro Santaclara; Jonathan R. Stroud
Abstract: 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.
Keywords: dynamic portfolio choice; return predictability; learning; simulation methods
JEL Codes: G1
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Learning about the parameters of the return generating process (C51) | Negative hedging demand for stocks (G40) |
| Positive return innovation (O35) | Investor revises expectations upward (D84) |
| Investor revises expectations upward (D84) | Reduced stock holdings as a hedge against perceived future risks (G17) |
| Parameter uncertainty and learning (C51) | Reduce stock allocation (G11) |
| Learning (C91) | Qualitatively change the solution to the dynamic portfolio choice problem (D11) |