mlsanityData to 14 Oct 2026

Deep Reinforcement Learning in Factor Investment

arXiv 2509.16206 ↗First posted 30 Jul 2025cs.CE cs.LGcited by 0

On the map: Deep reinforcement learning methods → Adversarial robustness and RL in finance

Abstract (arXiv)

Deep reinforcement learning has shown promise in trade execution, yet its use in low-frequency factor portfolio construction remains under-explored. A key obstacle is the high-dimensional, unbalanced state space created by stocks that enter and exit the investable universe. We introduce Conditional Auto-encoded Factor-based Portfolio Optimisation (CAFPO), which compresses stock-level returns into a small set of latent factors conditioned on 94 firm-specific characteristics. The factors feed a DRL agent implemented with both PPO and DDPG to generate continuous long-short weights. On 20 years of U.S. equity data (2000--2020), CAFPO outperforms equal-weight, value-weight, Markowitz, vanilla DRL, and Fama--French-driven DRL, delivering a 24.6% compound return and a Sharpe ratio of 0.94 out of sample. SHAP analysis further reveals economically intuitive factor attributions. Our results demonstrate that factor-aware representation learning can make DRL practical for institutional, low-turnover portfolio management.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Adversarial robustness and RL in finance, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 34 references that are in the map. First posted on arXiv 30 Jul 2025.

Builds on

7 of 34 references.

References · 7

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015Continuous control with deep reinforcement learningcited by 3,407
  3. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 128
  4. 2018Practical Deep Reinforcement Learning Approach for Stock Tradingcited by 49
  5. 2016Cryptocurrency Portfolio Management with Deep Reinforcement Learningcited by 32
  6. 2019Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimizationcited by 22
  7. 2021Deep Reinforcement Learning for Active High Frequency Tradingcited by 21

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