Aggregating Information for Optimal Portfolio Weights

Abstract

I attempt to address an important issue of the portfolio allocation literature – none of the allocation rules from prior studies consistently delivers good performance. I develop an approach that aggregates information from a wide range of sources to make allocation decisions. Specifically, this approach models the optimal portfolio weights as a function of a broad set of portfolio weights implied by prior allocation rules, and determines the relative contribution from each allocation rule through Elastic Net, a machine-learning technique. Out-of-sample tests suggest that my approach consistently achieves good performance, whereas none of the alternative rules can match the consistency.