John D. Lyon (Graduate School of Management, University of California, Davis) Brad M. Barber (Graduate School of Management, University of California, Davis) Chih-Ling Tsai (Graduate School of Management, University of California, Davis)
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. Copyright The American Finance Association 1999.
Download Info
To download:
If you experience problems downloading a file, check if you have the
proper application to
view it first. Information about this may be contained
in the File-Format links below. In case of further problems read
the IDEAS help
file. Note that these files are not on the IDEAS
site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
For technical questions regarding this item, or to correct its listing, contact: (Christopher F. Baum).
Related research
Keywords:
Cited by: (explanations, Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.) This item has more than 25 citations. To prevent cluttering this page, these citations are listed on a separate page.
Did you know? You can create a compilation of all publications of a group of people, say alumni of a program, your students or memers of an association.