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The reinforcement learning Kelly strategy

Author

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  • R. Jiang
  • D. Saunders
  • C. Weng

Abstract

The full Kelly portfolio strategy's deficiency in the face of estimation errors in practice can be mitigated by fractional or shrinkage Kelly strategies. This paper provides an alternative, the RL Kelly strategy, based on a reinforcement learning (RL) framework. RL algorithms are developed for the practical implementation of the RL Kelly strategy. Extensive simulation studies are conducted, and the results confirm the superior performance of the RL Kelly strategies.

Suggested Citation

  • R. Jiang & D. Saunders & C. Weng, 2022. "The reinforcement learning Kelly strategy," Quantitative Finance, Taylor & Francis Journals, vol. 22(8), pages 1445-1464, August.
    • Handle: RePEc:taf:quantf:v:22:y:2022:i:8:p:1445-1464
    DOI: 10.1080/14697688.2022.2049356
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    Cited by:

    1. Min Dai & Yuchao Dong & Yanwei Jia & Xun Yu Zhou, 2023. "Learning Merton's Strategies in an Incomplete Market: Recursive Entropy Regularization and Biased Gaussian Exploration," Papers 2312.11797, arXiv.org.
    2. Min Dai & Yu Sun & Zuo Quan Xu & Xun Yu Zhou, 2024. "Learning to Optimally Stop Diffusion Processes, with Financial Applications," Papers 2408.09242, arXiv.org, revised Sep 2024.
    3. Yanwei Jia, 2024. "Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty," Papers 2404.12598, arXiv.org.

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