Improved Methods for Tests of Long‐Run Abnormal Stock Returns

  • Author(s): John D. Lyon, Brad M. Barber, Chih‐Ling Tsai
  • Published: May 06, 2003
  • Pages: 165-201
  • DOI: 10.1111/0022-1082.00101

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.

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