Explainable Deep Reinforcement Learning for Portfolio Management: An Empirical Approach
This addresses the black-box nature of DRL for portfolio managers, but it is incremental as it builds on existing explainability methods.
The paper tackles the problem of understanding deep reinforcement learning (DRL) strategies in portfolio management by proposing an empirical approach to explain them, revealing that DRL agents show stronger multi-step prediction power than machine learning methods on Dow Jones 30 stocks from 2009 to 2021.
Deep reinforcement learning (DRL) has been widely studied in the portfolio management task. However, it is challenging to understand a DRL-based trading strategy because of the black-box nature of deep neural networks. In this paper, we propose an empirical approach to explain the strategies of DRL agents for the portfolio management task. First, we use a linear model in hindsight as the reference model, which finds the best portfolio weights by assuming knowing actual stock returns in foresight. In particular, we use the coefficients of a linear model in hindsight as the reference feature weights. Secondly, for DRL agents, we use integrated gradients to define the feature weights, which are the coefficients between reward and features under a linear regression model. Thirdly, we study the prediction power in two cases, single-step prediction and multi-step prediction. In particular, we quantify the prediction power by calculating the linear correlations between the feature weights of a DRL agent and the reference feature weights, and similarly for machine learning methods. Finally, we evaluate a portfolio management task on Dow Jones 30 constituent stocks during 01/01/2009 to 09/01/2021. Our approach empirically reveals that a DRL agent exhibits a stronger multi-step prediction power than machine learning methods.