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Nonparametric rank tests for event studies

Listed author(s):
  • Kolari, James W.
  • Pynnonen, Seppo
Registered author(s):

    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.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0927539811000624
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    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 18 (2011)
    Issue (Month): 5 ()
    Pages: 953-971

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    Handle: RePEc:eee:empfin:v:18:y:2011:i:5:p:953-971
    DOI: 10.1016/j.jempfin.2011.08.003
    Contact details of provider: Web page: http://www.elsevier.com/locate/jempfin

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