Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911266119876608 |
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| author | Lwele, Emmanuel Emmanuel, Sabuni Sitali, Sitali Gabriel |
| author_facet | Lwele, Emmanuel Emmanuel, Sabuni Sitali, Sitali Gabriel |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2511_11481 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization Lwele, Emmanuel Emmanuel, Sabuni Sitali, Sitali Gabriel Portfolio Management Computational Engineering, Finance, and Science Econometrics 91G10, 91B84, 68T07 I.2.6; I.2.7; J.4 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. |
| title | Risk-Aware Deep Reinforcement Learning for Dynamic Portfolio Optimization |
| topic | Portfolio Management Computational Engineering, Finance, and Science Econometrics 91G10, 91B84, 68T07 I.2.6; I.2.7; J.4 |
| url | https://arxiv.org/abs/2511.11481 |