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Improved Methods for Tests of Long-Run Abnormal Stock Returns

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  • John D. Lyon

    (Graduate School of Management, University of California, Davis)

  • Brad M. Barber

    (Graduate School of Management, University of California, Davis)

  • Chih-Ling Tsai

    (Graduate School of Management, University of California, Davis)

Abstract

We analyze tests for long-run abnormal returns and document that two approaches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calculated using carefully constructed reference portfolios. Inference is based on either a skewness-adjusted "t"-statistic or the empirically generated distribution of long-run abnormal returns. The second approach is based on calculation of mean monthly abnormal returns using calendar-time portfolios and a time-series "t"-statistic. Though both approaches perform well in random samples, misspecification in nonrandom samples is pervasive. Thus, analysis of long-run abnormal returns is treacherous. Copyright The American Finance Association 1999.

Suggested Citation

  • John D. Lyon & Brad M. Barber & Chih-Ling Tsai, 1999. "Improved Methods for Tests of Long-Run Abnormal Stock Returns," Journal of Finance, American Finance Association, vol. 54(1), pages 165-201, February.
  • Handle: RePEc:bla:jfinan:v:54:y:1999:i:1:p:165-201
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