Beyond sorting: A more powerful test for cross-sectional anomalies

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. (2016) substantially enhances the power of tests for cross-sectional anomalies: On average, "Student" t-statistics more than double.