Deep Parametric Portfolio Policies

Abstract

We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP) generate 43-102 basis points higher monthly certainty equivalent returns compared to linear policies. Looking beyond expected returns, non-linear portfolio policies better capture the complex relationship between investor preferences and firm characteristics but the benefits of using complex models vary with investor preferences. Results hold across different utility functions and remain robust to transaction costs and short-selling restrictions.

Keywords: Portfolio Choice, Machine Learning, Expected Utility

JEL Classification: G11, G12, C58, C45