Inference in long-horizon event studies: A Bayesian approach with application to initial public offerings

Published

Journal Article

Statistical inference in long-horizon event studies has been hampered by the fact that abnormal returns are neither normally distributed nor independent. This study presents a new approach to inference that overcomes these difficulties and dominates other popular testing methods. I illustrate the use of the methodology by examining the long-horizon returns of initial public offerings (IPOs). I find that the Fama and French (1993) three-factor model is inconsistent with the observed long-horizon price performance of these IPOs, whereas a characteristic-based model cannot be rejected.

Full Text

Duke Authors

Cited Authors

  • Brav, A

Published Date

  • January 1, 2000

Published In

Volume / Issue

  • 55 / 5

Start / End Page

  • 1979 - 2016

International Standard Serial Number (ISSN)

  • 0022-1082

Digital Object Identifier (DOI)

  • 10.1111/0022-1082.00279

Citation Source

  • Scopus