Missing values handling for machine learning portfolios

Andrew Y. Chen and Jack McCoy

Journal of Financial Economics, 2024, vol. 155, issue C

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.

Keywords: Stock market predictability; Stock market anomalies; Missing values; Machine learning (search for similar items in EconPapers)
JEL-codes: G0 G1 (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jfinec:v:155:y:2024:i:c:s0304405x24000382

DOI: 10.1016/j.jfineco.2024.103815

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