【明理讲堂2024年第60期】

报告题目：On Adaptive Online Mean-Variance Portfolio Selection Problems

时间：2024年10月19日上午9：00-10：30

地点：主楼317

报告人：Ching Wai Ki

报告人简介：

Prof. Ching Wai Ki is a Professor at the Department of Mathematics, The University of Hong Kong. He obtained his B. Sc. and M. Phil. in Mathematics from The University of Hong Kong and his Ph.D. in Systems Engineering and Engineering Management from The Chinese University of Hong Kong. He received 2013 Higher Education Outstanding Scientific Research Output Awards (Second Prize) from the Ministry of Education, China (2014), Distinguished Alumni Award, Faculty of Engineering, The Chinese University of Hong Kong (2017), 2019 Higher Education Outstanding Scientific Research Output Awards (Second Prize), Hunan Province, China (2019), Outstanding Research Student Supervisor Award, The University of Hong Kong (2020) and he was World's Top 2% Most-cited Scientists (2021,2022, 2023) by Stanford University. His research interests are Matrix Computations and Stochastic Modeling for Quantitative Finance and Bioinformatics. He is an author/editor of over 400 publications including over 250 journal papers, 5 edited journal special issues, 6 books and over 110 book chapters and conference proceedings.

报告内容简介：

Online portfolio selection is attracting a lot of attention due to its efficiency and practicability in deriving optimal investment strategies in real investment activities where the market information is constantly renewed in a very short period. One key issue in online portfolio is to predict the future returns of risky assets accurately given historical data and provide optimal investment strategies for investors in a short time. In the existing online portfolio selection studies, the historical return data of one risky asset is used to estimate its future return. In this talk, we incorporate the peer impact into the return prediction where the predicted return of one risky asset not only depends on its past return data but also the other risky assets in the financial market, which gives a more accurate prediction. An adaptive moving average method with peer impact (AOLPI) is proposed, in which the decaying factors can be adjusted automatically in the investment process. In addition, an adaptive mean-variance (AMV) model is applied in online portfolio selection where the variance is employed to measure the investment risk and the covariance matrix can be linearly updated in the investment process. The adaptive online moving average mean-variance (AOLPIMV) algorithm is proposed to provide flexible investment strategies for investors with different risk preferences. Numerical experiments are presented to validate the effectiveness and advantages of AOLPIMV.