报告地点：行健楼学术活动室665
邀请人：孙海琳教授
摘要：In this talk, we introduce multi-stage distributionally robust portfolio selection (MSDRPS) problems with reinforcement learning moments uncertainty. With uncertainty sets predetermined by fixed reference moments, we derive the closed-form solution of the MSDRPS problem. With multiple prior reference moments, we construct multiple prior uncertainty sets and solve the MSDRPS problem in a dynamic programming approach. When the reference moments information is learnable in a Bayesian rule, we construct two Bayesian reinforcement learning MSDRPS schemes with real investment feedback or reference investment feedback. We propose a two-level decomposition framework to solve the Bayesian reinforcement learning MSDRPS problem using reference investment feedback. Finally, numerical results on newly listed and sub-new stocks examine the practicality and superior performance of the Bayesian reinforcement learning strategy in MSDRPS problems.
个人简介：刘嘉，西安交通大学数学与统计学院科学计算系副教授。本科、硕士、博士均毕业于西安交通大学，期间赴法国巴黎第11大学联合培养。研究兴趣包括随机优化、鲁棒优化等近现代优化方法，强化学习等人工智能方法，及其在金融工程中的应用。他在这些方向取得了一些研究结果，在Mathematics of Operations Research、SIAM Journal on Optimization、European Journal of Operational Research、Quantitative Finance等运筹学、金融学期刊上发表学术论文30余篇，主持国家自然科学基金面上、青年项目，参与国家重点研发计划、国家自然科学基金重大、重点、面上项目以及与深圳证券交易所、华为软件有限公司、上海电气集团、中航工业集团西安航空计算技术研究所等单位合作的横向课题十余项。
