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<p>Return event studies typically involve a large number of event instances. In some applications, however, this number may be very small–sometimes as few as two event instances. In such cases, standard approaches to testing average abnormal returns (AAR) or cumulative average abnormal returns (CAAR) are less effective, or may not apply at all, as they rely on central limit theorems that require large sample sizes. We propose a nonparametric permutation test that remains valid for arbitrarily small numbers of event instances. Its performance is evaluated via Monte Carlo studies, and the method is further illustrated using two empirical applications.</p>

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The permutation test for event studies with a small number of events

  • 期刊论文

  • 发表日期：

    2026年3月24日

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  • Phuong Anh Nguyen,
  • Michael Wolf

摘要

Return event studies typically involve a large number of event instances. In some applications, however, this number may be very small–sometimes as few as two event instances. In such cases, standard approaches to testing average abnormal returns (AAR) or cumulative average abnormal returns (CAAR) are less effective, or may not apply at all, as they rely on central limit theorems that require large sample sizes. We propose a nonparametric permutation test that remains valid for arbitrarily small numbers of event instances. Its performance is evaluated via Monte Carlo studies, and the method is further illustrated using two empirical applications.