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Interactive preference analysis: A reinforcement learning framework
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PRE
AI
DOI：10.1016/j.ejor.2024.06.033.png)
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摘要
En 中文
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.
Keyword:
Decision analysis
Reinforcement learning
Investor preference derivation
Multi-armed bandit
Fintech
期刊
IF:
6
论文数:
2.2W
被引数:
7.1W
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