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The Reinforcement Learning Kelly Strategy

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RL_Kelly_Revision_V8.pdf (559.17 KB)

Date

2022-03

Authors

Jiang, Ruihong
Saunders, David
Weng, Chengguo

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

LC Keywords

Citation

URI

https://doi.org/10.1080/14697688.2022.2049356
 http://hdl.handle.net/10012/20094

Collections

Waterloo Research