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金融工程研究中心学术报告：Learning an Optimal Investment Policy with Transaction Costs via a Randomized Dynkin Game
- 来源：
- 学校官网
- 收录时间：
- 2026-03-17 19:21:55
- 时间：
- 2025-01-12 10:00:00
- 地点：
- 览秀楼105学术报告厅
- 报告人：
- Min Dai
- 学校：
- 苏州大学
- 关键词：
- transaction costs, portfolio optimization, Dynkin game, reinforcement learning, stochastic control, entropy regularization
- 简介：
- 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.
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报告介绍：
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.
报告人介绍：
Min Dai is Chair Professor at HK Polytechnic University. He previously served as Director of the Center for Quantitative Finance and Deputy Director of the Risk Management Institute at National University of Singapore. His research focuses on derivative pricing and hedging, dynamic investment strategies, and portfolio design under illiquidity. His papers have been published in top international journals including Journal of Finance, Journal of Economic Theory, Management Science, Mathematical Finance, and Review of Financial Studies. He currently serves on the editorial boards of SIAM Journal on Financial Mathematics and Journal of Economic Dynamics & Control.
