Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns

We propose a novel approach to optimizing portfolios with large numbers of assets. We model directly the portfolio weight in each asset as a function of the asset's characteristics. The coefficients of this function are found by optimizing the investor's average utility of the portfolio's return over the sample period. Our approach is computationally simple, easily modified and extended, produces sensible portfolio weights, and offers robust performance in and out of sample. In contrast, the traditional approach of first modeling the joint distribution of returns and then solving for the corresponding optimal portfolio weights is not only difficult to implement for a large number of assets but also yields notoriously noisy and unstable results. Our approach also provides a new test of the portfolio choice implications of equilibrium asset pricing models. We present an empirical implementation for the universe of all stocks in the CRSP-Compustat dataset, exploiting the size, value, and momentum anomalies.