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

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, Ledoit, and Wolf (2017) substantially enhances the power of tests for cross-sectional anomalies: On average, “Student” t-statistics more than double.

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