The Reinforcement Learning Kelly Strategy
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Date
2022-03
Authors
Jiang, Ruihong
Saunders, David
Weng, Chengguo
Advisor
Journal Title
Journal ISSN
Volume Title
Publisher
Taylor & Francis
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.
Description
This is an Accepted Manuscript of an article published by Taylor & Francis in Quantitative Finance on 24 March 2022, available online: https://doi.org/10.1080/14697688.2022.2049356
Keywords
Kelly criterion, fractional Kelly strategy, portfolio selection, reinforcement learning