The Virtue of Complexity in Machine Learning Portfolios

Abstract

We theoretically characterize the behavior of machine learning portfolios in the high complexity regime, i.e. when the number of parameters exceeds the number of observations. We demonstrate a surprising "virtue of complexity:" Sharpe ratios of machine learning portfolios generally increase with model parameterization, even with minimal regularization. Empirically, we document the virtue of complexity in US equity market timing strategies. High complexity models deliver economically large and statistically significant out-of-sample portfolio gains relative to simpler models, due in large part to their remarkable ability to predict recessions.

Keywords: Portfolio choice, machine learning, random matrix theory, benign overfit, overparameterization

JEL Classification: C3, C58, C61, G11, G12, G14