Characteristic-Sorted Portfolios: Estimation and Inference

Matias Cattaneo, Richard Crump, Max Farrell and Ernst Schaumburg

No 788, Staff Reports from Federal Reserve Bank of New York

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 (search for similar items in EconPapers)
JEL-codes: C12 C14 (search for similar items in EconPapers)
Date: 2016-08-01
New Economics Papers: this item is included in nep-ecm
Note: Revised October 2016.
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.newyorkfed.org/research/staff_reports/sr788.html Summary (text/html)
https://www.newyorkfed.org/medialibrary/media/rese ... orts/sr788.pdf?la=en Full text (application/pdf)

Related works:
Journal Article: Characteristic-Sorted Portfolios: Estimation and Inference (2020)
Working Paper: Characteristic-Sorted Portfolios: Estimation and Inference (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:fip:fednsr:788

Ordering information: This working paper can be ordered from

Access Statistics for this paper

More papers in Staff Reports from Federal Reserve Bank of New York Contact information at EDIRC.
Bibliographic data for series maintained by Gabriella Bucciarelli ().