Inference in Long‐Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings
提出一种贝叶斯方法，克服了长期事件研究中异常收益非正态且非独立的难题，优于其他常用检验方法，并用该方法检验了首次公开发行的长期回报，发现Fama-French三因子模型与数据不一致，而特征模型则无法被拒绝。
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.