Error, Noise, and Bias of Auditors’ Going Concern Opinions and the Role of Machine Learning

30 Pages Posted: 15 Dec 2021

See all articles by Chanyuan (Abigail) Zhang

Chanyuan (Abigail) Zhang

Rutgers, The State University of New Jersey - Accounting & Information Systems

Yu Gu

Rutgers, The State University of New Jersey - Accounting & Information Systems

Miklos Vasarhelyi

Rutgers, The State University of New Jersey - Accounting & Information Systems

Date Written: December 2021

Abstract

This paper studies the error, bias, and noise of auditors’ Going Concern Opinion (GCO) and Machine Learning (ML) models in predicting firm default. We find that advanced ML models can significantly reduce the error, bias, and noise of default predictions compared to GCO, consistent with the theory in Kahneman, Sibony, & Sunstein (2021). Following Kahneman et al. (2021), we also explore the value of diversity in improving prediction quality. To that end, we construct four “artificial auditors” representing Big4 auditors. We find that the consensus from these artificial auditors can significantly reduce the prediction error compared to GCO. Our study adds to the accounting literature by examining the quality of GCO and the mechanism through which ML improves default prediction from the angle of prediction error, bias, and noise.

Keywords: Going Concern Opinion, Machine Learning, Prediction Error, Prediction Bias, Prediction Noise

JEL Classification: M41, M42

Suggested Citation

Zhang, Chanyuan (Abigail) and Gu, Yu and Vasarhelyi, Miklos, Error, Noise, and Bias of Auditors’ Going Concern Opinions and the Role of Machine Learning (December 2021). Available at SSRN: https://ssrn.com/abstract=3984462 or http://dx.doi.org/10.2139/ssrn.3984462

Chanyuan (Abigail) Zhang (Contact Author)

Rutgers, The State University of New Jersey - Accounting & Information Systems ( email )

1 Washington Pl
Newark, NJ 07102
United States

Yu Gu

Rutgers, The State University of New Jersey - Accounting & Information Systems ( email )

1 washington park
newark, NJ 07102
United States

Miklos Vasarhelyi

Rutgers, The State University of New Jersey - Accounting & Information Systems ( email )

96 New England Avenue, #18
Summit, NJ 07901-1825
United States

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