Interactive preference analysis: A reinforcement learning framework

Xiao Hu, Siqin Kang, Long Ren and Shaokeng Zhu

European Journal of Operational Research, 2024, vol. 319, issue 3, 983-998

Abstract: Automated investment managers are increasingly popular in personal wealth management due to their cost effectiveness, objectivity, and accessibility. However, it still suffers from several dilemmas, e.g., cold start, over-specialization, and black boxes. To solve these issues, we develop an online reinforcement learning framework based on the multi-armed bandit algorithm to offer personalized investment advice. We provide a comprehensive theoretical procedure for developing this framework. This framework not only enables us to capture the evolving preferences of investors effectively but also has a strong explainability power to provide more implications regarding why one financial product is preferred. We further evaluate our basic model through a large-scale, real-world data set from a leading wealth management platform. The results show a stronger effectiveness of the proposed framework compared to other well-recognized benchmark models. Furthermore, we extend our basic model to address the potential agency problem between the robo-advisor and the investors. Another extension is also provided through an optimization scheme to account for the investors’ demands for diversification in multiple aspects.

Keywords: Decision analysis; Reinforcement learning; Investor preference derivation; Multi-armed bandit; Fintech (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:319:y:2024:i:3:p:983-998

DOI: 10.1016/j.ejor.2024.06.033

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