Prediction when factors are weak
主讲人：
Dake Zhang（Shanghai Jiao Tong University）
主持老师：
（北大经院）王一鸣、王法
参与老师：
（北大经院）王熙、刘蕴霆、巩爱博
时间：
2025年12月12日（周五）
10:00-11:30
地点（线下）：
北京大学经济学院219会议室
主讲人简介：
Dake Zhang is an Assistant Professor at the Antai College of Economics and Management, Shanghai Jiao Tong University. His research focuses on asset pricing, factor models, and reinforcement learning, with related work published in the Journal of Finance. His research has also been presented at major conferences such as the AFA, WFA, and the NBER Time Series Conference. At SJTU, he teaches courses including Financial Risk Management and Blockchain and Digital Currency. Dake earned his Ph.D. in Econometrics and Statistics from the University of Chicago Booth School of Business, and holds a Master’s degree in Statistics from the University of Chicago and a Bachelor’s degree in Mathematics from Tsinghua University.
报告摘要：
In economic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of predictors. Nevertheless, the theoretical justification of this approach often relies on a convenient and critical assumption that factors are pervasive. To incorporate information from weaker factors, we propose a new prediction procedure based on supervised PCA, which iterates over selection, PCA, and projection. The selection step finds a subset of predictors most correlated with the prediction target, whereas the projection step permits multiple weak factors of distinct strength. We justify our procedure in an asymptotic scheme where both the sample size and the cross-sectional dimension increase at potentially different rates. Our empirical analysis highlights the role of weak factors in predicting inflation, industrial production growth, and changes in unemployment.
