FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance
Fuente:
arXiv
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| Autori principali: | , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866912656572547072 |
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| author | Li, Yang Chen, Zhi |
| author_facet | Li, Yang Chen, Zhi |
| contents | Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15883 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance Li, Yang Chen, Zhi Computational Finance Artificial Intelligence Machine Learning Trading and Market Microstructure Traditional stochastic control methods in finance struggle in real world markets due to their reliance on simplifying assumptions and stylized frameworks. Such methods typically perform well in specific, well defined environments but yield suboptimal results in changed, non stationary ones. We introduce FinFlowRL, a novel framework for financial optimal stochastic control. The framework pretrains an adaptive meta policy learning from multiple expert strategies, then finetunes through reinforcement learning in the noise space to optimize the generative process. By employing action chunking generating action sequences rather than single decisions, it addresses the non Markovian nature of markets. FinFlowRL consistently outperforms individually optimized experts across diverse market conditions. |
| title | FinFlowRL: An Imitation-Reinforcement Learning Framework for Adaptive Stochastic Control in Finance |
| topic | Computational Finance Artificial Intelligence Machine Learning Trading and Market Microstructure |
| url | https://arxiv.org/abs/2510.15883 |