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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2509.23062 |
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| _version_ | 1866918149650120704 |
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| author | Zhang, Haoran Zhang, Wenhao Wu, Xianping |
| author_facet | Zhang, Haoran Zhang, Wenhao Wu, Xianping |
| contents | This paper addresses the problem of dynamic asset allocation under uncertainty, which can be formulated as a linear quadratic (LQ) control problem with multiplicative noise. To handle exploration exploitation trade offs and induce sparse control actions, we introduce Tsallis entropy as a regularization term. We develop an entropy regularized policy iteration scheme and provide theoretical guarantees for its convergence. For cases where system dynamics are unknown, we further propose a fully data driven algorithm that estimates Q functions using an instrumental variable least squares approach, allowing efficient and stable policy updates. Our framework connects entropy-regularized stochastic control with model free reinforcement learning, offering new tools for intelligent decision making in finance and automation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_23062 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization Zhang, Haoran Zhang, Wenhao Wu, Xianping Optimization and Control This paper addresses the problem of dynamic asset allocation under uncertainty, which can be formulated as a linear quadratic (LQ) control problem with multiplicative noise. To handle exploration exploitation trade offs and induce sparse control actions, we introduce Tsallis entropy as a regularization term. We develop an entropy regularized policy iteration scheme and provide theoretical guarantees for its convergence. For cases where system dynamics are unknown, we further propose a fully data driven algorithm that estimates Q functions using an instrumental variable least squares approach, allowing efficient and stable policy updates. Our framework connects entropy-regularized stochastic control with model free reinforcement learning, offering new tools for intelligent decision making in finance and automation. |
| title | Data-Driven Long-Term Asset Allocation with Tsallis Entropy Regularization |
| topic | Optimization and Control |
| url | https://arxiv.org/abs/2509.23062 |