Asset Pricing: Cross-section of Returns (Conditional)
Paper Session
Saturday, Jan. 7, 2023 8:00 AM - 10:00 AM (CST)
- Chair: Serhiy Kozak, University of Maryland
Missing Data in Asset Pricing Panels
Abstract
Missing data for return predictors is a common problem in cross sectional asset pricing studies. Most papers do not explicitly discuss how they treat missing data but conventional treatments focus on complete cases for all predictors or impute the unconditional mean for the missing predictor. Both methods have undesirable properties - they are either inefficient or lead to biased estimators and incorrect inference. We propose a simple and computationally attractive alternative approach using conditional mean imputations and weighted least squares. This method allows us to use all sample points with observed returns, it results in valid inference, and it can be applied in non-linear and high-dimensional settings. We map our estimator into a GMM framework to study its relative efficiency and find that it performs almost as well as the efficient but computationally costly GMM estimator in many cases. We apply our procedure to a large panel of return predictors and find that it leads to improved out-of-sample predictability.Missing Financial Data
Abstract
Missing data is a prevalent, yet often ignored, feature of company fundamentals. In this paper, we document the structure of missing financial data and show how to systematically deal with it. In a comprehensive empirical study we establish four key stylized facts. First, the issue of missing financial data is profound: it affects over 70% of firms that represent about half of the total market cap. Second, the problem becomes particularly severe when requiring multiple characteristics to be present. Third, firm fundamentals are not missing-at-random, invalidating traditional ad-hoc approaches to data imputation and sample selection. Fourth, stock returns themselves depend on missingness. We propose a novel imputation method to obtain a fully observed panel of firm fundamentals. It exploits both time-series and cross-sectional dependency of firm characteristics to impute their missing values, while allowing for general systematic patterns of missing data. Our approach provides a substantial improvement over the standard leading empirical procedures such as using cross-sectional averages or past observations. Our results have crucial implications for many areas of asset pricing.Discussant(s)
Steven Heston
,
University of Maryland
Markus Pelger
,
Stanford University
Michael Weber
,
University of Chicago
JEL Classifications
- G1 - Asset Markets and Pricing