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Missing values handling for machine learning portfolios

Author

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  • Chen, Andrew Y.
  • McCoy, Jack

Abstract

We characterize the structure and origins of missingness for 159 cross-sectional return predictors and study missing value handling for portfolios constructed using machine learning. Simply imputing with cross-sectional means performs well compared to rigorous expectation-maximization methods. This stems from three facts about predictor data: (1) missingness occurs in large blocks organized by time, (2) cross-sectional correlations are small, and (3) missingness tends to occur in blocks organized by the underlying data source. As a result, observed data provide little information about missing data. Sophisticated imputations introduce estimation noise that can lead to underperformance if machine learning is not carefully applied.

Suggested Citation

  • Chen, Andrew Y. & McCoy, Jack, 2024. "Missing values handling for machine learning portfolios," Journal of Financial Economics, Elsevier, vol. 155(C).
    • Handle: RePEc:eee:jfinec:v:155:y:2024:i:c:s0304405x24000382
    DOI: 10.1016/j.jfineco.2024.103815
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    More about this item

    Keywords

    Stock market predictability; Stock market anomalies; Missing values; Machine learning;
    All these keywords.

    JEL classification:

    • G0 - Financial Economics - - General
    • G1 - Financial Economics - - General Financial Markets

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