嘉宾介绍：湖南大学工商管理学院副教授，博士生导师。2006年毕业于华中科技大学数学系，2009年硕士毕业于山东大学，2014年博士毕业于香港中文大学师从李端教授学习金融优化。 现任湖南大学工商管理学院金融科技（Fintech）MBA项目主任。研究方向：金融风险管理、 风险度量、动态投资组合。主持国家自然科学基金青年项目一项，省自科青年项目一项，参与国家级以上项目多项。曾在SIAM Journal on Control and Optimization与 Quantitative Finance等主流杂志发表论文多篇。为香港中文大学，香港城市大学访问学者，副研究员等。中国运筹学会金融工程与金融风险管理分会常务理事，副秘书长。
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讲座介绍：This paper introduces an end-to-end online portfolio decision model within the framework of direct reinforcement learning, seamlessly integrating the multi-factor model and mean-variance (MV) portfolio optimization. Recognizing that classical methods, which separate estimation and portfolio optimization into a two-step scheme, may accumulate estimation errors jeopardizing overall performance, our approach unifies these steps into a performance-oriented online decision process. This integration is achieved by tuning the neural network parameters directly with respect to the reward function, designed as a combination of the prediction error and realized MV utility. Specifically, we employ a neural network to estimate future returns and generate the factor loading matrix, enabling the computation of inputs for the MV portfolio optimization model. Implementing the resulting portfolio further provides the realized utility. The network parameters are optimized with respect to the updated reward using the gradient method. We develop an online updating scheme for computing the gradient in backpropagation by providing explicit formulas for MV portfolio derivatives through the portfolio optimization layer. Utilizing real market data, we evaluate the proposed method against several benchmark portfolios in out-of-sample tests. The experiments demonstrate that our approach not only outperforms these benchmarks across various performance metrics but is also transparent to factor analysis, a favorable trait for practitioners.
