勷勤数学•专家报告

题      目：Discrete-Time Mean-Variance Strategy Based on Reinforcement Learning

报  告  人： 李迅 教授  (邀请人：杨舟)

                                    香港理工大学

时      间： 6月10日  11：00-12:00

地     点：数科院西楼二楼会议室

报告人简介:

       李迅，香港理工大学教授，博导，主要研究领域为随机控制和金融应用。在《SIAM Journal on Control and Optimization》、《Annals of Applied Probability》、《Journal of Differential Equations》、《IEEE Transactions on Automatic Control》、 《Automatica》、 《Mathematical Finance》等国际期刊上发表多篇论文。

摘      要：

       This work 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.

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