  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.
