Long-Run Performance Evaluation: Correlation and Heteroskedasticity-Consistent Tests

42 Pages Posted: 19 Apr 2004

See all articles by Narasimhan Jegadeesh

Narasimhan Jegadeesh

Emory University - Department of Finance

Jason J. Karceski

LSV Asset Management

Date Written: April 19, 2004

Abstract

Although much work has been done on evaluating long-run equity abnormal returns, the statistical tests used in the literature are misspecified when event firms come from nonrandom samples. Specifically, industry clustering or overlapping returns in the sample contribute to test misspecification. We propose a new test of long-run performance that uses the average long-run abnormal return for each monthly cohort of event firms, but weights these average abnormal returns in a way that allows for heteroskedasticity and autocorrelation. Our tests work well in random samples and in samples with industry clustering and with overlapping returns, without a reduction in power compared to the methodologies of Lyon, Barber and Tsai (1999).

Keywords: Long-run performance, statistical test

JEL Classification: G14

Suggested Citation

Jegadeesh, Narasimhan and Karceski, Jason J., Long-Run Performance Evaluation: Correlation and Heteroskedasticity-Consistent Tests (April 19, 2004). Available at SSRN: https://ssrn.com/abstract=532503 or http://dx.doi.org/10.2139/ssrn.532503

Narasimhan Jegadeesh

Emory University - Department of Finance ( email )

Atlanta, GA 30322-2710
United States

Jason J. Karceski (Contact Author)

LSV Asset Management ( email )

155 N Wacker Dr.
Chicago, IL 60654
United States
352-246-7674 (Phone)

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