Comparing Deep RL and Traditional Financial Portfolio Methods (ECML PKDD 2023 - MIDAS Slides)

Abstract

Traditional portfolio allocation methods are based on the assumption of known risk tolerance and parametric models of market returns. Deep reinforcement learning (DRL) is a non-parametric machine learning technique that can learn optimal policies for complex sequential decision-making problems. This paper conducts a comprehensive comparative analysis of traditional portfolio allocation methods and DRL. The paper finds that DRL outperforms traditional methods in terms of both risk-adjusted return and Sharpe ratio in a simulation study. The authors argue that DRL is a promising new approach to portfolio management with the potential to outperform traditional methods in a variety of market conditions.

Keywords: deep reinforcement learning, portfolio allocation, risk-adjusted return, Sharpe ratio, financial markets

JEL Classification: G11, C61, C45