Interactive preference analysis: A reinforcement learning framework

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.