The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913832709914624 |
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| author | Li, Yu Wu, Yuhan Zhang, Shuhua |
| author_facet | Li, Yu Wu, Yuhan Zhang, Shuhua |
| contents | In this paper, we study the continuous-time multi-asset mean-variance (MV) portfolio selection using a reinforcement learning (RL) algorithm, specifically the soft actor-critic (SAC) algorithm, in the time-varying financial market. A family of Gaussian portfolio selections is derived, and a policy iteration process is crafted to learn the optimal exploratory portfolio selection. We prove the convergence of the policy iteration process theoretically, based on which the SAC algorithm is developed. To improve the algorithm's stability and the learning accuracy in the multi-asset scenario, we divide the model parameters that influence the optimal portfolio selection into three parts, and learn each part progressively. Numerical studies in the simulated and real financial markets confirm the superior performance of the proposed SAC algorithm under various criteria. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07537 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning Li, Yu Wu, Yuhan Zhang, Shuhua Mathematical Finance In this paper, we study the continuous-time multi-asset mean-variance (MV) portfolio selection using a reinforcement learning (RL) algorithm, specifically the soft actor-critic (SAC) algorithm, in the time-varying financial market. A family of Gaussian portfolio selections is derived, and a policy iteration process is crafted to learn the optimal exploratory portfolio selection. We prove the convergence of the policy iteration process theoretically, based on which the SAC algorithm is developed. To improve the algorithm's stability and the learning accuracy in the multi-asset scenario, we divide the model parameters that influence the optimal portfolio selection into three parts, and learn each part progressively. Numerical studies in the simulated and real financial markets confirm the superior performance of the proposed SAC algorithm under various criteria. |
| title | The Exploratory Multi-Asset Mean-Variance Portfolio Selection using Reinforcement Learning |
| topic | Mathematical Finance |
| url | https://arxiv.org/abs/2505.07537 |