Missing Financial Data

• 成果类型: Article
• 署名作者: Bryzgalova, Svetlana; Lerner, Sven; Lettau, Martin; Pelger, Markus
• 署名单位: University of London; London Business School; Stanford University; University of California System; University of California Berkeley; National Bureau of Economic Research; Centre for Economic Policy Research - UK
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhae036
• 发表日期: 2024
• 页码: 803
• 关键词: cross-section; factor models; inference; RISK; equilibrium; INFORMATION; regression; returns

摘要

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)