Speaker: 董玉超（同济大学数学科学学院 助理教授）

Time/Date: 2024年4月2日（周二）12:00-13:00

Classroom: 同济大厦A楼505室

TENCENT meeting: 391311121

PW: 620714

Link: https://meeting.tencent.com/dm/JX4cz8TkEBgI

ABSTRACT

We study a dynamic mean-variance portfolio optimization problem under the reinforcement learning framework, where an entropy regularizer is introduced to induce exploration. Due to the time–inconsistency involved in a mean-variance criterion, we aim to learn an equilibrium policy. Under an incomplete market setting, we obtain a semi-analytical, exploratory, equilibrium mean-variance policy that turns out to follow a Gaussian distribution. We then focus on a Gaussian mean return model and propose a reinforcement learning algorithm to find the equilibrium policy. Thanks to a thoroughly designed policy iteration procedure in our algorithm, we prove the convergence of our algorithm under mild conditions, despite that dynamic programming principle and the usual policy improvement theorem failing to hold for an equilibrium policy. Numerical experiments are given to demonstrate our algorithm. The design and implementation of our reinforcement learning algorithm apply to a general market setup.

GUEST BIO

董玉超博士毕业于复旦大学数学科学学院，之后在复旦大学，法国昂热大学，新加坡国立大学从事博士后研究。2021年1月加入同济大学数学科学学院。董玉超博士的研究方向为随机最优控制理论及其在金融数学中的应用。其研究工作发表在包括AMO，SICON,SIAP,MaFi等国际知名期刊上。