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Cited by 261

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

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

Open access
Brav, Alon·2000·The Journal of Finance
DOI 10.1111/0022-1082.00279
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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.

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Crossref · 10.1111/0022-1082.00279

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Metadata

Authors
Brav, A.
Venue
The Journal of Finance
Publisher
Wiley
Published
2000-10
Volume
55
Issue
5
Pages
1979-2016
ISSN
  • 0022-1082 (ISSN-L)
  • 1540-6261
DOI
10.1111/0022-1082.00279
OpenAlex
W3121518808
Cited by
261
References
7
FWCI
26.50
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Inference in Long‐Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings. https://app.cochat.ai/papers/10.1111/0022-1082.00279
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Brav, A. (2000). Inference in Long‐Horizon Event Studies: A Bayesian Approach with Application to Initial Public Offerings. The Journal of Finance. https://doi.org/10.1111/0022-1082.00279

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.