Auditor Response to Estimated Misstatement Risk: A Machine Learning Approach

What is it about?

In this paper, we investigate whether misstatement risk estimated using advanced machine learning techniques, hereafter referred to as estimated misstatement risk (EMR), approximates auditors' risk assessments in practice. We find that auditors price EMR and that 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 suggest that the documented association between audit fees and EMR primarily reflects a risk premium.

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