Not logged in.

Contribution Details

Type Working Paper
Scope Discipline-based scholarship
Title Efficient sorting: a more powerful test for cross-sectional anomalies
Organization Unit
  • Olivier Ledoit
  • Michael Wolf
  • Zhao Zhao
  • English
Institution University of Zurich
Series Name Working paper series / Department of Economics
Number 238
ISSN 1664-7041
Number of Pages 51
Date 2018
Abstract Text Many researchers seek factors that predict the cross-section of stock returns. The standard methodology sorts stocks according to their factor scores into quantiles and forms a corresponding long-short portfolio. Such a course of action ignores any information on the covariance matrix of stock returns. Historically, it has been difficult to estimate the covariance matrix for a large universe of stocks. We demonstrate that using the recent DCC-NL estimator of Engle et al. (2017) substantially enhances the power of tests for cross-sectional anomalies: On average, `Student' t-statistics more than double.
Official URL
Related URLs
PDF File Download from ZORA
Export BibTeX
Keywords Cross-section of returns, dynamic conditional correlations, GARCH, Markowitz portfolio selection, nonlinear shrinkage, Portfoliomanagement, Aktienrendite, Matrizenrechnung, Schätzung, Prognosemodell, Kovarianzmatrix, Portfolio Selection, t-Test
Additional Information Revised version ; Former titles: Beyond sorting: a more powerful test for cross-sectional anomalies ; Efficient weighting: a more powerful test for cross-sectional anomalies