报告时间：2022/05/30  9:30-10:30

报告地址：腾讯会议：807-786-181，密码：0530

报告题目：A Synthetic Regression Model for Large Portfolio Allocation

摘要：Portfolio allocation is an important topic in financial data analysis. In this article, based on the mean-variance optimization principle, we propose a synthetic regression model for construction of portfolio allocation, and an easy to implement approach to generate the synthetic sample for the model. Compared with the regression approach in existing literature for portfolio allocation, the proposed method of generating the synthetic sample provides more accurate approximation for the synthetic response variable when the number of assets under consideration is large. Due to the embedded leave-one-out idea, the synthetic sample generated by the proposed method has weaker within sample correlation, which makes the resulting portfolio allocation more close to the optimal one. This intuitive conclusion is theoretically 

confirmed to be true by the asymptotic properties established in this article. We have also conducted intensive simulation studies in this article to compare the proposed method with the existing ones, and found the proposed method works better. Finally, we apply the proposed method to real datasets. The yielded returns look very encouraging.

报告人简介：李高荣，北京师范大学统计学院教授，博士生导师。全国工业统计学教学研究会常务理事、中国现场统计研究会第十一届理事、中国概率统计学会第十一届理事中国工业互联网研究院技术专家委员会专家。主要研究方向是非参数统计、高维统计、统计学习、纵向数据、测量误差数据和因果推断等。迄今为止，在Annals of Statistics, JASA, Journal of Business & Economic Statistics, Statistics and Computing, 《中国科学：数学》和《统计研究》等顶级学术期刊上发表学术论文90多篇。在科学出版社出版3部著作：《纵向数据半参数模型》、《现代测量误差模型》(入选“现代数学基础丛书”系列)和《多元统计分析》(入选“统计与数据科学丛书”系列)。入选北京市属高等学校人才强教深化计划“中青年骨干人才培养计划”，北京市优秀人才培养资助计划和北京工业大学“京华人才”支持计划。主持国家自然科学基金、北京市自然科学基金和北京市教委科技计划面上项目等国家和省部级科研项目10多项。