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Inference in Long‐Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings
Author(s) -
Brav Alon
Publication year - 2000
Publication title -
the journal of finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 18.151
H-Index - 299
eISSN - 1540-6261
pISSN - 0022-1082
DOI - 10.1111/0022-1082.00279
Subject(s) - initial public offering , horizon , inference , statistical inference , econometrics , bayesian inference , event (particle physics) , bayesian probability , economics , computer science , mathematical economics , mathematics , statistics , artificial intelligence , finance , physics , geometry , quantum mechanics
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.