Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach-Reference-Cited by-同舟云学术

Detecting Accounting Fraud in Publicly Traded U.S. Firms Using a Machine Learning Approach

Published:2020-01-19 Issue:1 Volume:58 Page:199-235
ISSN:0021-8456
Container-title:Journal of Accounting Research
language:en
Short-container-title:Journal of Accounting Research

Author:

BAO YANG¹,KE BIN²,LI BIN³,YU Y. JULIA⁴^ORCID,ZHANG JIE⁵

Affiliation:

1. Antai College of Economics and ManagementShanghai Jiao Tong University

2. Department of Accounting, NUS Business SchoolNational University of Singapore

3. Department of Finance, Economics and Management SchoolWuhan University

4. McIntire School of CommerceUniversity of Virginia

5. School of Computer EngineeringNanyang Technological University

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Economics and Econometrics,Finance,Accounting

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/1475-679X.12292

Reference59 articles.

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4. An Empirical Analysis of the Relation Between the Board of Director Composition and Financial Statement Fraud;Beasley M.;The Accounting Review,1996

5. Beasley M. S.;J. V.Carcello; andD. R.Hermanson. “Fraudulent Financial Reporting: 1987–1997: An Analysis of U.S. Public Companies.” Sponsored by the Committee of Sponsoring Organizations of the Treadway Commission (COSO) 1999.

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