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Missing Financial Data
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AI
DOI：10.1093/rfs/hhae036.png)
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摘要
En 中文
We document the widespread nature and structure of missing observations of firm fundamentals and show how to systematically handle them. Missing financial data affects more than 70% of firms that represent about half of the total market cap. Firm fundamentals have complex systematic missing patterns, invalidating traditional approaches to imputation. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals that exploits both time-series and cross-sectional dependency of data to impute missing values and allows for general systematic patterns of missingness. We document important implications for risk premiums estimates, cross-sectional anomalies, and portfolio construction. (JEL C14, C38, C55, G12)
Keyword:
CROSS-SECTION
FACTOR MODELS
INFERENCE
RISK
EQUILIBRIUM
INFORMATION
REGRESSION
RETURNS
期刊
IF:
5.4
论文数:
2.8K
被引数:
3.0W
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