Missing Data in Asset Pricing Panels

Joachim Freyberger; Bjoern Hoeppner; Andreas Neuhierl; Michael Weber

doi:10.1093/rfs/hhae003

Review of Financial Studies Vol. 38 No. 3 2025

Missing Data in Asset Pricing Panels

Joachim Freyberger¹; Bjoern Hoeppner¹; Andreas Neuhierl²; Michael Weber³

¹ University of Bonn · ² Olin School of Business Washington University in St. Louis · ³ Booth School of Business, the University of Chicago , USA, CEPR, and NBER

Abstract

We propose a simple and computationally attractive method to deal with missing data in in cross-sectional asset pricing using conditional mean imputations and weighted least squares, cast in a generalized method of moments (GMM) framework. This method allows us to use all observations with observed returns; it results in valid inference; and it can be applied in nonlinear and high-dimensional settings. In simulations, we find it performs almost as well as the efficient but computationally costly GMM estimator. We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.

DOI: 10.1093/rfs/hhae003
Volume: 38
Issue: 3
Pages: 760-802
Language: en
Sources: bibtex:phds-export.bib openalex crossref

Missing Data in Asset Pricing Panels

Abstract

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