Improved Methods for Tests of Long‐Run Abnormal Stock Returns
分析了两种在随机样本中表现良好的长期异常收益率检验方法：一是基于事件研究和精心构建参考组合的买入持有异常收益率，使用偏度调整t统计量或经验分布进行推断；二是基于日历时间组合的月均异常收益率和时间序列t统计量。但两种方法在非随机样本中普遍存在设定错误，提醒研究者谨慎分析长期异常收益率。
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.