Improved Methods for Tests of Long-Run Abnormal Stock Returns

Authors


  • Graduate School of Management, University of California, Davis. This paper was previously entitled “Holding Size while Improving Power in Tests of Long-Run Abnormal Stock Returns.” We have benefited from the suggestions of John Affleck-Graves, Peter Bickel, Alon Brav, Sandra Chamberlain, Arnold Cowan, Masako Darrough, Eugene Fama, Ken French, Peter Hall, Inmoo Lee, Tim Loughran, Michael Maher, Terrance Odean, Stephen Penman, N. R. Prabhala, Raghu Rau, Jay Ritter, René Stulz, Brett Trueman, Ralph Walkling, two anonymous reviewers, and seminar participants at the Australian Graduate School of Management, State University of New York-Buffalo, Ohio State, University of California-Berkeley, University of California-Davis, the University of Washington, and 1997 Western Finance Association Meetings. Chih-Ling Tsai was supported by National Science Foundation grant DMS 95–10511. All errors are our own.

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 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.

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