Feature configuration effects in DRL portfolio management: a risk-focused evaluation under market stress
研究了深度强化学习代理能否有效利用金融特征信息进行风险感知的投资组合管理，通过对比四种特征配置，发现BARRA系统性风险信息能显著改善最大回撤，而技术指标无显著效果。
This study investigates whether deep reinforcement learning (DRL) agents can effectively use financial feature information for risk-aware portfolio management. We design a controlled experimental framework that compares four feature configurations: BARRA-derived systematic risk information, technical indicators, their combination, and a no-feature baseline. Using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm across 800 independently trained agents on randomly sampled 30-stock S&P 500 portfolios, we evaluate out-of-sample performance during the volatile 2022 market and assess statistical significance via paired permutation tests. The BARRA-derived information provides significant downside protection, improving maximum drawdown by 0.71% (p = 0.02) relative to the baseline, while technical indicators do not offer significant benefit alone or in combination. These results indicate that DRL agents can leverage systematic risk information to manage tail risk, and that targeted feature selection based on financial theory may be more effective than indiscriminate feature augmentation.