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A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited

De Nard, Gianluca; Zhao, Zhao (2022). A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited. International Review of Economics and Finance, 80:654-676.

Abstract

Many researchers seek factors that predict the cross-section of stock returns. In finance, the key is to replicate anomalies by long–short portfolios based on their firm characteristics, with microcap biases alleviated via New York Stock Exchange (NYSE) breakpoints and value-weighted returns. In econometrics, the key is to include a covariance matrix estimator of stock returns for (mimicking) the portfolio construction. This paper marries these two strands of literature in order to test the zoo of cross-sectional anomalies by injecting size controls, basically NYSE breakpoints and value-weighted returns, into efficient sorting. We propose to use a covariance matrix estimator for ultra-high dimensions (up to 5,000) taking into account large, small and microcap stocks. We demonstrate that using a nonlinear shrinkage estimator of the covariance matrix substantially enhances the power of tests for cross-sectional anomalies: On average, -statistics more than double. Furthermore, the proposed revisited efficient sorting method computes even highly significant factor portfolios net of transaction costs.

Keywords: Anomalies, cross-section of returns, efficient sorting, large dimensions, Markowitz portfolio selection, nonlinear shrinkage

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Finance
Dewey Decimal Classification:330 Economics
Scopus Subject Areas:Social Sciences & Humanities > Finance
Social Sciences & Humanities > Economics and Econometrics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:July 2022
Deposited On:02 Dec 2022 06:18
Last Modified:28 Oct 2024 02:39
Publisher:Elsevier
ISSN:1059-0560
OA Status:Green
Free access at:Related URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1016/j.iref.2022.02.049
Official URL:https://doi.org/10.1016/j.iref.2022.02.049
Related URLs:http://dx.doi.org/10.2139/ssrn.3560178
Other Identification Number:merlin-id:22982
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