The reinforcement learning Kelly strategy

被引：1

作者：

Jiang, R. ^{[1
]}

Saunders, D. ^{[1
]}

Weng, C. ^{[1
]}

机构：

[1] Univ Waterloo, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada

来源：

QUANTITATIVE FINANCE | 2022年 / 22卷 / 08期

关键词：

Kelly criterion; Fractional Kelly strategy; Portfolio selection; Reinforcement learning; CONTINUOUS-TIME;

D O I：

10.1080/14697688.2022.2049356

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：1445 / 1464

页数：20

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