Ensembles of Portfolio Rules

Abstract

We propose an ensemble framework for combining heterogeneous portfolio rules that cannot be accommodated by previously proposed combination methods. Using our approach, researchers and investors can take advantage of established and ongoing advances in portfolio choice by diversifying the idiosyncratic risks of alternative rules. Our ensemble approach maximizes the utility jointly generated by the candidate portfolio rules, while allowing learning about their time-varying relative performance. Based on out-of-sample evaluations of over forty years, we document substantial utility gains in extensive applications to cross-sections of stocks and to market timing.

Keywords: Portfolio choice, Combination of estimators, Ensemble learning, Estimation risk JEL classifications: G11, C10

JEL Classification: G11, C10