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Type Journal Article
Scope Discipline-based scholarship
Title A large-dimensional test for cross-sectional anomalies:Efficient sorting revisited
Organization Unit
Authors
  • Gianluca De Nard
  • Zhao Zhao
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title International Review of Economics and Finance
Publisher Elsevier
Geographical Reach international
ISSN 1059-0560
Volume 80
Page Range 654 - 676
Date 2022
Abstract Text 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
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Official URL https://doi.org/10.1016/j.iref.2022.02.049
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Digital Object Identifier 10.1016/j.iref.2022.02.049
Other Identification Number merlin-id:22982
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