Improved methods for tests of long-run abnormal stock returns

We analyze tests for long-run abnormal returns and document that two approa ches yield well-specified test statistics in random samples. The first uses a traditional event study framework and buy-and-hold abnormal returns calc ulated using carefully constructed reference portfolios. Inference is based on either a skewness-adjusted t-statistic or the empirically generated dis tribution of long-run abnormal returns. The second approach is based on cal culation of mean monthly abnormal returns using calendar-time portfolios an d 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.