Improved Methods for Tests of Long-Run Abnormal Stock Returns

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...

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Bibliographic Details
Published in:The Journal of finance (New York) 1999-02, Vol.54 (1), p.165-201
Main Authors: Lyon, John D., Barber, Brad M., Tsai, Chih-Ling
Format: Article
Language:English
Subjects:
Economic models
Financial portfolios
Judgment samples
Null hypothesis
P values
Portfolio performance
Random number sampling
Random sampling
Rates of return
Significance level
Skewed distribution
Statistical bias
Statistical methods
Statistics
Stock prices
Studies
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Summary: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.
ISSN:0022-1082
1540-6261
DOI:10.1111/0022-1082.00101