Characteristic-Sorted Portfolios: Estimation and Inference

48 Pages Posted: 15 Aug 2016 Last revised: 8 Oct 2019

See all articles by Matias D. Cattaneo

Matias D. Cattaneo

University of Michigan at Ann Arbor - Department of Economics

Richard K. Crump

Federal Reserve Banks - Federal Reserve Bank of New York

Max Farrell

University of Chicago - Booth School of Business - Econometrics and Statistics

Ernst Schaumburg

Federal Reserve Banks - Federal Reserve Bank of New York

Date Written: February 1, 2019

Abstract

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 standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.

Keywords: portfolio sorts, nonparametric estimation, partitioning, tuning parameter selection

JEL Classification: C12, C14

Suggested Citation

Cattaneo, Matias D. and Crump, Richard K. and Farrell, Max and Schaumburg, Ernst, Characteristic-Sorted Portfolios: Estimation and Inference (February 1, 2019). FRB of NY Staff Report No. 788, Available at SSRN: https://ssrn.com/abstract=2822686

Matias D. Cattaneo (Contact Author)

University of Michigan at Ann Arbor - Department of Economics ( email )

611 Tappan Street
Ann Arbor, MI 48109-1220
United States
734-763-1306 (Phone)

HOME PAGE: http://www.umich.edu/~cattaneo/

Richard K. Crump

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

Max Farrell

University of Chicago - Booth School of Business - Econometrics and Statistics ( email )

Chicago, IL 60637
United States

Ernst Schaumburg

Federal Reserve Banks - Federal Reserve Bank of New York ( email )

33 Liberty Street
New York, NY 10045
United States

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