Time: 10:00-11:00, Monday, October 19, 2026

Venue: Guoshun Campus, Room 303, Starr Building

Speaker: Assistant Professor Weichen Wang, Faculty of Business and Economics, The University of Hong Kong

Moderator: Associate Professor Xuening Zhu, Department of Statistics and Data Science, School of Management, Fudan University

Topic: On Reference-Regulated Multiperiod Mean-Variance Portfolio Optimization in High Dimensions

Abstract: The multiperiod mean-variance (MV) portfolio optimization serves as a vital expansion of Markowitz's static MV portfolio selection framework. Just like its static counterpart, the multiperiod MV portfolio remains susceptible to estimation errors. We propose a reference-regulated multiperiod mean-variance (RRMV) framework that penalizes deviations from a reference policy. Therefore, this new optimization successfully combines the advantages of dynamic strategies and reference portfolios. A key contribution of this paper is the characterization of the out-of-sample Sharpe ratio under high-dimensional asymptotics with estimation errors in both the mean vector and the covariance matrix. We show how the reference penalty and the investment horizon jointly affect the optimized portfolio performance, and how regularization operates differently from the single-period portfolio optimization. Extensive simulation and real data studies demonstrate that the proposed framework improves the stability and out-of-sample Sharpe ratios of multiperiod policies significantly.

Bio: Weichen Wang is an Assistant Professor in the Department of Innovation and Information Management, Faculty of Business and Economics, The University of Hong Kong. He received his PhD from the Department of Operations Research and Financial Engineering at Princeton University. He previously worked at the quantitative hedge fund Two Sigma Investments, during which he served as a visiting lecturer for a financial econometrics course at Princeton University, and he received his bachelor's degree in Mathematics and Physics from Tsinghua University. His research interests include econometric factor models, high-dimensional statistical inference, robust statistics, rank inference, and machine learning applications in financial data. His work has been published in the Annals of Statistics, the Journal of the American Statistical Association, the Journal of Machine Learning Research, Operations Research, and the Journal of Econometrics.