Missing Values Handling for Machine Learning Portfolios
Fuente:
arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2022
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| _version_ | 1866910294789324800 |
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| author | Chen, Andrew Y. McCoy, Jack |
| author_facet | Chen, Andrew Y. McCoy, Jack |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2207_13071 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Missing Values Handling for Machine Learning Portfolios Chen, Andrew Y. McCoy, Jack Methodology General Finance Applications 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. |
| title | Missing Values Handling for Machine Learning Portfolios |
| topic | Methodology General Finance Applications |
| url | https://arxiv.org/abs/2207.13071 |