Reinforcement learning with intrinsic affinity for personalized prosperity management

Charl Maree () and Christian W. Omlin ()
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Charl Maree: University of Agder
Christian W. Omlin: University of Agder

Digital Finance, 2022, vol. 4, issue 2, No 13, 262 pages

Abstract: Abstract The purpose of applying reinforcement learning (RL) to portfolio management is commonly the maximization of profit. The extrinsic reward function used to learn an optimal strategy typically does not take into account any other preferences or constraints. We have developed a regularization method that ensures that strategies have global intrinsic affinities, i.e., different personalities may have preferences for certain asset classes which may change over time. We capitalize on these intrinsic policy affinities to make our RL model inherently interpretable. We demonstrate how RL agents can be trained to orchestrate such individual policies for particular personality profiles and still achieve high returns.

Keywords: AI in banking; Personalized financial services; Explainable AI; Reinforcement learning; Policy regularization; Intrinsic affinity; Robo-advising (search for similar items in EconPapers)
JEL-codes: C10 C30 C32 C40 C45 C50 C51 C52 C53 C54 C58 D10 D14 D31 D53 D91 E22 E37 G11 G41 (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s42521-022-00068-4

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