A Review of Reinforcement Learning in Financial Applications
Fuente:
arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910705515495424 |
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| author | Bai, Yahui Gao, Yuhe Wan, Runzhe Zhang, Sheng Song, Rui |
| author_facet | Bai, Yahui Gao, Yuhe Wan, Runzhe Zhang, Sheng Song, Rui |
| contents | In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications.
This approach has shown great potential to solve decision-making tasks in finance.
In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods.
Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges.
Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_12746 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Review of Reinforcement Learning in Financial Applications Bai, Yahui Gao, Yuhe Wan, Runzhe Zhang, Sheng Song, Rui Computational Finance Artificial Intelligence Machine Learning In recent years, there has been a growing trend of applying Reinforcement Learning (RL) in financial applications. This approach has shown great potential to solve decision-making tasks in finance. In this survey, we present a comprehensive study of the applications of RL in finance and conduct a series of meta-analyses to investigate the common themes in the literature, such as the factors that most significantly affect RL's performance compared to traditional methods. Moreover, we identify challenges including explainability, Markov Decision Process (MDP) modeling, and robustness that hinder the broader utilization of RL in the financial industry and discuss recent advancements in overcoming these challenges. Finally, we propose future research directions, such as benchmarking, contextual RL, multi-agent RL, and model-based RL to address these challenges and to further enhance the implementation of RL in finance. |
| title | A Review of Reinforcement Learning in Financial Applications |
| topic | Computational Finance Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2411.12746 |