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

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  • 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.

Suggested Citation

  • Alon Brav, 2000. "Inference in Long‐Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings," Journal of Finance, American Finance Association, vol. 55(5), pages 1979-2016, October.
    • Handle: RePEc:bla:jfinan:v:55:y:2000:i:5:p:1979-2016
    DOI: 10.1111/0022-1082.00279
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