嘉宾介绍：崔翔宇，中国科学技术大学学士，硕士，香港中文大学博士。现为上海财经大学统计与管理学院，常任教职副教授（已获得终身教职），研究员，博导。管理科学与工程学会金融计量和风险管理研究会秘书长，中国管理现代化研究会管理与决策科学专业委员会理事。主要研究领域包括行为金融，数量金融，风险管理，在Operations Research， INFORMS Journal on Computing，Journal of Econometrics，Mathematical Finance，Journal of Economic Dynamics & Control等国际着名厂厂颁滨/厂颁滨期刊发表论文40篇。
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讲座介绍：We have considered a discrete-time mean-variance model based on reinforcement learning. Compared to its continuous-time counterpart of Wang and Zhou (2020), the discrete-time model makes more general assumptions about the asset distribution. By using entropy to measure the cost of exploration, we derive the optimal investment strategy, which is also Gaussian type. Additionally, we design a corresponding reinforcement learning algorithm. Simulation experiments and empirical analysis indicate that the discrete-time model exhibits better applicability when analyzing real-world data compared to the continuous-time model.
