Work

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

Year: 2018

Type: article

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.

Source: Journal of Financial Econometrics

DOI: 10.1093/jjfinec/nby015

Authors: Olivier Ledoit, Michael Wolf, Zhao Zhao

Institutions: University of Zurich, Huazhong University of Science and Technology

Language: English

Topic: Financial Markets and Investment Strategies

Subfield: Finance

Field: Economics, Econometrics and Finance

Domain: Social Sciences

Open Access status: green

OpenAlex ID: W2563940647

FWCI: 3.638

Cites: 92

Cited by: 21

Related to: 10