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Improved Methods for Tests of Long‐Run Abnormal Stock Returns
Author(s) -
Lyon John D.,
Barber Brad M.,
Tsai ChihLing
Publication year - 1999
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/0022-1082.00101
Subject(s) - statistic , econometrics , skewness , test statistic , stock (firearms) , statistics , statistical hypothesis testing , inference , normal distribution , mathematics , computer science , engineering , mechanical engineering , artificial intelligence
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.