Efficient Sorting: A More Powerful Test for Cross-Sectional Anomalies
提出一种利用DCC-NL协方差矩阵估计器改进的排序检验方法，能显著提升检验股票横截面收益异象的统计功效，使t统计量平均翻倍以上。
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.