Missing Values Handling for Machine Learning Portfolios

Andrew Y. Chen; Jack McCoy

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arXiv · 2207.13071

Missing Values Handling for Machine Learning Portfolios

Andrew Y. Chen·Jack McCoy

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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.

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Andrew Y. Chen, Jack McCoy. 2022-07-21. Missing Values Handling for Machine Learning Portfolios. https://arxiv.org/abs/2207.13071

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