Reinforcement learning with intrinsic affinity for personalized prosperity management
Journal article, Peer reviewed
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Date
2022Metadata
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Original version
Maree, C. & Omlin, C. W. P. (2022). Reinforcement learning with intrinsic affinity for personalized prosperity management. Digit Finance, 4, 241–262. https://doi.org/10.1007/s42521-022-00068-4Abstract
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.