Continuous-Time Reinforcement Learning for Asset-Liability Management
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866916974209007616 |
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| author | Huang, Yilie |
| author_facet | Huang, Yilie |
| contents | This paper proposes a novel approach for Asset-Liability Management (ALM) by employing continuous-time Reinforcement Learning (RL) with a linear-quadratic (LQ) formulation that incorporates both interim and terminal objectives. We develop a model-free, policy gradient-based soft actor-critic algorithm tailored to ALM for dynamically synchronizing assets and liabilities. To ensure an effective balance between exploration and exploitation with minimal tuning, we introduce adaptive exploration for the actor and scheduled exploration for the critic. Our empirical study evaluates this approach against two enhanced traditional financial strategies, a model-based continuous-time RL method, and three state-of-the-art RL algorithms. Evaluated across 200 randomized market scenarios, our method achieves higher average rewards than all alternative strategies, with rapid initial gains and sustained superior performance. The outperformance stems not from complex neural networks or improved parameter estimation, but from directly learning the optimal ALM strategy without learning the environment. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_23280 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Continuous-Time Reinforcement Learning for Asset-Liability Management Huang, Yilie Machine Learning Artificial Intelligence Optimization and Control Mathematical Finance This paper proposes a novel approach for Asset-Liability Management (ALM) by employing continuous-time Reinforcement Learning (RL) with a linear-quadratic (LQ) formulation that incorporates both interim and terminal objectives. We develop a model-free, policy gradient-based soft actor-critic algorithm tailored to ALM for dynamically synchronizing assets and liabilities. To ensure an effective balance between exploration and exploitation with minimal tuning, we introduce adaptive exploration for the actor and scheduled exploration for the critic. Our empirical study evaluates this approach against two enhanced traditional financial strategies, a model-based continuous-time RL method, and three state-of-the-art RL algorithms. Evaluated across 200 randomized market scenarios, our method achieves higher average rewards than all alternative strategies, with rapid initial gains and sustained superior performance. The outperformance stems not from complex neural networks or improved parameter estimation, but from directly learning the optimal ALM strategy without learning the environment. |
| title | Continuous-Time Reinforcement Learning for Asset-Liability Management |
| topic | Machine Learning Artificial Intelligence Optimization and Control Mathematical Finance |
| url | https://arxiv.org/abs/2509.23280 |