Nonparametric rank tests for event studies Academic Article

abstract

• Because stock prices are not normally distributed, the power of nonparametric rank tests dominate parametric tests in event study analyses of abnormal returns on a single day. However, problems arise in the application of nonparametric tests to multiple day analyses of cumulative abnormal returns (CARs) that have caused researchers to normally rely upon parametric tests. In an effort to overcome this shortfall, this paper proposes a generalized rank (GRANK) testing procedure that can be used on both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived, and their empirical properties are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to popular parametric tests. 2011.

authors

• Kolari, James

published proceedings

• JOURNAL OF EMPIRICAL FINANCE

author list (cited authors)

• Kolari, J. W., & Pynnonen, S.

citation count

• 127

complete list of authors

• Kolari, James W||Pynnonen, Seppo

publication date

• December 2011

publisher

• Elsevier  Publisher

published in

• Journal of Empirical Finance  Journal

keywords

• Abnormal Returns
• Event Study
• Rank Test
• Standardized Returns

Digital Object Identifier (DOI)

• 10.1016/j.jempfin.2011.08.003

start page

• 953

end page

• 971

volume

• 18

issue

• 5

URL

• http%3A%2F%2Fdx.doi.org%2F10.1016%2Fj.jempfin.2011.08.003