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Missing Data in Asset Pricing Panels

Author

Listed:
  • Joachim Freyberger
  • Bjoern Hoeppner
  • Andreas Neuhierl
  • Michael Weber

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.

Suggested Citation

  • Joachim Freyberger & Bjoern Hoeppner & Andreas Neuhierl & Michael Weber, 2025. "Missing Data in Asset Pricing Panels," The Review of Financial Studies, Society for Financial Studies, vol. 38(3), pages 760-802.
    • Handle: RePEc:oup:rfinst:v:38:y:2025:i:3:p:760-802.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhae003
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    More about this item

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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