Research / BFI Working PaperMar 31, 2023

Prediction When Factors are Weak

Stefano Giglio, Dacheng Xiu, Dake Zhang

In macroeconomic forecasting, principal component analysis (PCA) has been the most prevalent approach to the recovery of factors, which summarize information in a large set of macro 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 high- lights the role of weak factors in predicting inflation, industrial production growth, and changes in unemployment.

More Research From These Scholars

BFI Working Paper Jul 7, 2021

Test Assets and Weak Factors

Stefano Giglio, Dacheng Xiu, Dake Zhang
Topics:  Financial Markets
BFI Working Paper Jul 24, 2023

Financial Machine Learning

Bryan T. Kelly, Dacheng Xiu
Topics:  Technology & Innovation, Financial Markets
BFI Working Paper May 14, 2019

Predicting Returns with Text Data

Dacheng Xiu, Zheng Tracy Ke, Bryan Kelly
Topics:  Financial Markets