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Improved methods for tests of long-run abnormal stock returns (1999)
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| Venue: | Journal of Finance |
| Citations: | 355 - 12 self |
BibTeX
@ARTICLE{Lyon99improvedmethods,
author = {John D. Lyon and Brad M. Barber and Chih-ling Tsai and Raghu Rau and Jay Ritter and René Stulz and Brett Trueman and Ralph Walkling},
title = {Improved methods for tests of long-run abnormal stock returns},
journal = {Journal of Finance},
year = {1999},
pages = {165--201}
}
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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 skewnessadjusted 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. COMMONLY USED METHODS TO TEST for long-run abnormal stock returns yield misspecified test statistics, as documented by Barber and Lyon ~1997a! and Kothari and Warner ~1997!. 1 Simulations reveal that empirical rejection levels routinely exceed theoretical rejection levels in these tests. In combination, these papers highlight three causes for this misspecification. First, the
Keyphrases
long-run abnormal stock return long-run abnormal return random sample nonrandom sample well-specified test statistic misspecified test statistic theoretical rejection level second approach buy-and-hold abnormal return simulation reveal mean monthly abnormal return commonly used method test calendar-time portfolio traditional event study framework generated distribution reference portfolio empirical rejection level
