Journal of Financial Economics Vol. 155 2024
Missing values handling for machine learning portfolios
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.
- DOI
- 10.1016/j.jfineco.2024.103815
- Volume
- 155
- Pages
- 103815
- Language
- en
- Sources
- bibtex:phds-export.bib openalex crossref