Auditor Response to Estimated Misstatement Risk: A Machine Learning Approach

We investigate whether misstatement risk estimated using advanced machine learning techniques—hereafter, estimated misstatement risk (EMR)—approximates auditors' risk assessments in practice. We find that auditors price EMR and auditor turnover is more likely to occur when EMR increases, indica...

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Bibliographic Details
Published in:Accounting horizons 2022-03, Vol.36 (1), p.111-130
Main Authors: Hunt, Emily, Hunt, Joshua, Richardson, Vernon J., Rosser, David
Format: Article
Language:English
Subjects:
Audit quality
Auditing
Auditors
Audits
Cognitive style
Fees & charges
Machine learning
Risk assessment
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Summary:We investigate whether misstatement risk estimated using advanced machine learning techniques—hereafter, estimated misstatement risk (EMR)—approximates auditors' risk assessments in practice. We find that auditors price EMR and auditor turnover is more likely to occur when EMR increases, indicating that EMR is associated with auditors' risk assessment. We also find evidence that EMR is positively and significantly associated with audit fees and auditor switching for companies with Big N auditors but not for other companies, suggesting that Big N auditors are more responsive to risks captured by EMR. Additional analyses reveal that companies switching auditors when EMR increases are more likely to engage non-Big N auditors. Surprisingly, we find little evidence that the association between audit quality and EMR differs by auditor type. Our findings are consistent with the notion that the documented association between audit fees and EMR primarily reflects a risk premium in our setting.
ISSN:0888-7993
1558-7975
DOI:10.2308/HORIZONS-19-139