Characteristic-Sorted Portfolios: Estimation and Inference

• 成果类型: Article
• 署名作者: Cattaneo, Matias D.; Crump, Richard K.; Farrell, Max H.; Schaumburg, Ernst
• 署名单位: Princeton University; Federal Reserve System - USA; Federal Reserve Bank - New York; University of Chicago
• 刊物名称: REVIEW OF ECONOMICS AND STATISTICS
• ISSN/ISSBN: 0034-6535
• DOI: 10.1162/rest_a_00883
• 发表日期: 2020-07
• 页码: 531-551
• 关键词: cross-section; asset; returns; profitability; MONOTONICITY; performance; regression; stocks; tests; RISK

摘要

Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.