Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

Speaker：Li Xun, Professor, Hong Kong Polytechnic University

Host：Zeng Yan, Professor, Lingnan College

Time and Date：15:00, March 12, 2026 (Thursday)

Venue：Wong Bing Lai Conference Room (203), Lingnan Hall

Language：English + Chinese

Abstract：

This talk studies a discrete-time mean-variance model based on reinforcement learning. Compared with its continuous-time counterpart in Wang-Zhou (2020), the discrete-time model makes more general assumptions about the asset's return distribution. Using entropy to measure the cost of exploration, we derive the optimal investment strategy, whose density function is also Gaussian type. Additionally, we design the corresponding reinforcement learning algorithm. Both simulation experiments and empirical analysis indicate that our discrete-time model exhibits better applicability when analyzing real-world data than the continuous-time model. (Joint with Xiangyu Cui, Yun Shi, and Si Zhao.) 

Profile:

Xun Li received the B.S., M.S. degrees in 1992, 1995, respectively, from the Department of Mathematics, Shanghai University of Science and Technology, and the Department of Mathematics, Shanghai University, China. He completed his Ph.D. degree in 2000 from the Department of Systems Engineering and Engineering Management at the Chinese University of Hong Kong, and he stayed with the same department as a postdoctoral research fellow until 2001. From 2001 to 2003, he was a postdoctoral fellow in the Mathematical and Computational Finance Laboratory at the University of Calgary. From 2003 to 2007, he was a visiting fellow in the Department of Mathematics at the National University of Singapore. He joined the Department of Applied Mathematics at the Hong Kong Polytechnic University as Assistant Professor in 2007, Associate Professor in 2013, and is currently Professor. His main research areas are stochastic control and applied probability with financial applications, and he has published in journals such as SIAM Journal on Control and Optimization, Annals of Applied Probability, Finance and Stochastics, IEEE Transactions on Automatic Control, Automatica, Journal of Differential Equations, Mathematical Finance and Quantitative Finance.