报告时间: 12 Jan, Monday, 2025, 10:00-11:30

报告人: Min Dai, Chair Professor, HK Polytechnic University

报告地点: 览秀楼105学术报告厅

报告摘要:  This talk addresses the challenge of developing optimal investment strategies in the presence of transaction costs and uncertain market conditions—an issue of critical importance for portfolio managers and financial decision-makers. We reformulate the classical continuous-time portfolio selection problem as a Dynkin game, a strategic framework that captures the timing of buy and sell decisions under market frictions. To overcome the computational difficulties posed by the discontinuous nature of stopping decisions, we introduce a randomized Dynkin game approach that incorporates entropy regularization to balance exploration and exploitation. Building on this formulation, we develop an interpretable reinforcement learning algorithm capable of learning near-optimal trading policies directly from market data without requiring explicit knowledge of model parameters. Our theoretical analysis establishes convergence guarantees and quantifies the trade-offs involved in the exploration-exploitation balance. Through extensive numerical experiments and empirical tests on simulated and real market data, we demonstrate that our method effectively approximates optimal trading boundaries and outperforms benchmark strategies, offering a practical tool for dynamic portfolio management in realistic trading environments. This work bridges advanced stochastic control theory and modern machine learning, providing actionable insights for managing transaction costs and adapting to evolving market dynamics. This is a joint work with Yuchao Dong and Zhichao Lu.

报告人简介:戴民教授现任香港理工大学讲座教授，曾任新加坡国立大学数量金融中心主任、风险管理研究所副所长。在金融衍生产品定价与对冲、动态投资策略、缺乏流动性的投资组合设计等领域做了很多深入的工作。文章发表在Journal of Finance, Journal of Economic Theory, Management Science, Mathematical Finance, Review of Financial Studies等国际top期刊。目前担任SIAM Journal on Financial Mathematics，Journal of Economic Dynamics & Control等期刊编委。