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.
@article{arxiv.2207.13071,
title = {Missing Values Handling for Machine Learning Portfolios},
author = {Andrew Y. Chen and Jack McCoy},
journal= {arXiv preprint arXiv:2207.13071},
year = {2024}
}