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

By Alon Brav

Abstract

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

Topics: this study and Michael Bradley for access to the SDC database. Krishnamoorthy Narasimhan
Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.199.1674
Provided by: CiteSeerX
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