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Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization

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Listed:
  • Emmanuel Lwele
  • Sabuni Emmanuel
  • Sitali Gabriel Sitali

Abstract

This paper presents a deep reinforcement learning (DRL) framework for dynamic portfolio optimization under market uncertainty and risk. The proposed model integrates a Sharpe ratio-based reward function with direct risk control mechanisms, including maximum drawdown and volatility constraints. Proximal Policy Optimization (PPO) is employed to learn adaptive asset allocation strategies over historical financial time series. Model performance is benchmarked against mean-variance and equal-weight portfolio strategies using backtesting on high-performing equities. Results indicate that the DRL agent stabilizes volatility successfully but suffers from degraded risk-adjusted returns due to over-conservative policy convergence, highlighting the challenge of balancing exploration, return maximization, and risk mitigation. The study underscores the need for improved reward shaping and hybrid risk-aware strategies to enhance the practical deployment of DRL-based portfolio allocation models.

Suggested Citation

  • Emmanuel Lwele & Sabuni Emmanuel & Sitali Gabriel Sitali, 2025. "Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization," Papers 2511.11481, arXiv.org.
    • Handle: RePEc:arx:papers:2511.11481
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    References listed on IDEAS

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    1. Xiao Zhong & David Enke, 2019. "Predicting the daily return direction of the stock market using hybrid machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 5(1), pages 1-20, December.
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