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Inference in Long-Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings

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Author Info
Alon Brav (Duke University, Fuqua School of Business)
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. Copyright The American Finance Association 2000.

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Article provided by American Finance Association in its journal The Journal of Finance.

Volume (Year): 55 (2000)
Issue (Month): 5 (October)
Pages: 1979-2016
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Handle: RePEc:bla:jfinan:v:55:y:2000:i:5:p:1979-2016

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