Enhanced Optimal Portfolios - A Controlled Integration of Quantitative Predictors

Abstract

The intuitiveness and practicability of mean-variance portfolios largely depends on the accuracy of moment estimates, which are subject to large estimation errors and conditional on time. We propose a model accounting for factor dynamics in a Bayesian setting, in which the impact of estimation accuracy on the posterior distribution is endogenously derived from a linear predictive regression model. Thereby, we capture upside return potential for periods of high factor explained variance whilst constraining downside risk for periods of low predictive quality. Results are robust in a simulation and empirical setting.