  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.
