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Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies

Author

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  • Olivier Ledoit
  • Michael Wolf
  • Zhao Zhao

Abstract

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.

Suggested Citation

  • Olivier Ledoit & Michael Wolf & Zhao Zhao, 2016. "Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies," ECON - Working Papers 238, Department of Economics - University of Zurich, revised May 2018.
    • Handle: RePEc:zur:econwp:238
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    File URL: https://www.zora.uzh.ch/id/eprint/128607/17/econwp238.pdf
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    Cited by:

    1. van Zundert, Jeroen, 2018. "Empirical studies on the cross-section of corporate bond and stock markets," Other publications TiSEM 338205fc-a031-4e06-a636-9, Tilburg University, School of Economics and Management.

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    More about this item

    Keywords

    Cross-section of returns; dynamic conditional correlations; GARCH; Markowitz portfolio selection; nonlinear shrinkage;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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