教育部人文社科重点研究基地中央财经大学中国精算研究院学术活动
精算论坛第258期讲座
(2025年4月18日)
讲座主题：Reinforcement learning without a market simulator
摘要：Reinforcement learning (RL) has found applications in portfolio selection and optimal stopping problems in finance and insurance. Unlike stochastic control problems, which rely on a given stochastic model, an RL agent strikes a balance between exploration and exploitation, where exploration involves randomized decision rules. However, the RL framework in the finance and insurance literature usually requires a reliable market simulator during the training procedure. While the construction of a market simulator is largely unknown, this limitation restricts applications to high-frequency trading.
In this talk, I present two examples of RL that do not require a market simulator but use data other than market prices. The first example focuses on learning efficient investment decisions from liquidity spread data. The exploration aspect of RL enables investors to construct a statistical test for rebalancing time that accounts for liquidity (including transaction) costs. Upon rebalancing, we construct a robust investment rule using exploration with KL divergence regularization.
The second example addresses surrendering decisions in variable annuities. The actuarial science literature presents two schools of thought: the optimal surrendering approach based on market information and the surrender intensity approach. Using RL, we demonstrate that the latter can be viewed as an exploratory version of the former. Thus, surrender data can be used to train the RL model for randomized surrender decisions consistent with optimal stopping rules.
This presentation is based on joint research papers with Sixian Zhuang, Ling Wang, and Mei Choi Chiu.
报告人：Hoi Ying Wong
Hoi Ying Wong is a Professor at Department of Statistics and Outstanding Fellow of Faculty of Science at The Chinese University of Hong Kong (CUHK). His research interests include stochastic control theory, big data analytics, machine learning, numerical methods and their applications in finance. He published two books and over 110 articles. His works appear in reputable Mathematical Finance journals (e.g. MF, F&S, SIFIN, JEDC, QF); Operations Research Journals (e.g. Automatica, MOR, EJOR); Actuarial Science and Risk Management Journals (e.g. JRI, IME, SAJ, Risk Analysis); and Applied Mathematics Journals (e.g. SICON, SINUM, AMO, AMC). He served as Associate Editor of SIFIN in 2016-2021,and has served as Associate Editor of International Journal of Theoretical and Applied Finance since 2005. He is a warded the 2018 best paper prize from IMA Journal on Management Mathematics, 2024 Kan Tong Po International Fellow ship by Royal Society UK, and several teaching awards by CUHK. His research was supported by external research grants with cumulative amount of over HK$15 million. He has consulting experience with Hong Kong Monetary Authority, Commercial banks and Fin Tech firms.
讲座时间：2025年4月18日（周五）
上午10:30-12:00
报告地点：沙河二教113
邀 请 人：王玲
