# Missing Values Handling for Machine Learning Portfolios

arXiv: https://arxiv.org/abs/2207.13071 · HTML: https://mlsanity.com/paper/2207.13071/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2022.

## Built on by (most cited first, 1 of 1)

- [Board gender diversity and emissions performance: Insights from panel regressions, machine learning, and explainable AI](https://mlsanity.com/paper/2510.00244/) (2025)

## Abstract (arXiv)

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.