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Non-Parametric Statistic for Testing Cumulative Abnormal Stock Returns

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  • Seppo Pynnonen

    (Department of Mathematics and Statistics, University of Vaasa, P.O. Box 700, FI-65101 Vaasa, Finland)

Abstract

Due to the non-normality of stock returns, nonparametric rank tests are gaining accceptance relative to parametric tests in financial economics event studies. In rank tests, financial assets’ multiple day cumulative abnormal returns (CARs) are replaced by cumulated ranks. This paper proposes modifications to the existing approaches to improve robustness to cross-sectional correlation of returns arising from calendar time overlapping event windows. Simulations show that the proposed rank test is well specified in testing CARs and is robust towards both complete and partial overlapping event windows.

Suggested Citation

  • Seppo Pynnonen, 2022. "Non-Parametric Statistic for Testing Cumulative Abnormal Stock Returns," JRFM, MDPI, vol. 15(4), pages 1-14, March.
    • Handle: RePEc:gam:jjrfmx:v:15:y:2022:i:4:p:149-:d:777710
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    References listed on IDEAS

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