[3Win5] Poster session 3

[3Win5-62] Counterfactual Explanations for Interpreting Deep Reinforcement Learning in Portfolio Management

〇Jiali Ma¹, Akihiro Yamamoto¹, Aoba Ito² (1.Kyoto University, 2.Nomura Securities Co., Ltd.)

Keywords:Explainable AI, Counterfactual Explanation, Reinforcement Learning, Portfolio Management

In portfolio management, directly using past stock returns as training data often results in suboptimal performance due to covariate shifts. To address this, deep reinforcement learning (DRL) has emerged as a powerful approach that dynamically adapts to market conditions and optimizes long-term rewards while reducing reliance on short-term price fluctuations. However, the black-box nature of DRL poses a significant challenge to transparency, which is crucial for financial institutions to meet regulatory and accountability requirements. Counterfactual explanations provide a promising solution for enhancing the interpretability of DRL-based investment strategies. However, a key challenge is ensuring that counterfactual scenarios maintain consistency between stock prices and their derived technical indicators. To address this, we propose a novel two-step framework: first, we identify critical stocks that significantly influence the agent’s decisions, and then we systematically adjust their prices to generate realistic counterfactual scenarios. Experimental evaluations demonstrate that the proposed method improves transparency in DRL-driven portfolio management, offering a more interpretable and robust framework for investment decision-making.