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Predicting the Class and Type of Critical Audit Matters

Zabihollah Rezaee et al.

Journal of Emerging Technologies in Accounting2025https://doi.org/10.2308/jeta-2024-009article
AJG 1ABDC B
Weight
0.50

Abstract

This study examines the predictability of critical audit matters (CAMs) disclosures, differentiating between entity-level and account-level CAMs by using the water cycle algorithm (WCA). Prior studies find no changes in audit value and effort associated with disclosing CAMs. Analyses of CAMs disclosures from a large sample of firms between 2019 and 2022 reveal that the WCA is highly effective in predicting both the categories (account versus entity) and types (primary versus others) of CAMs disclosures. The most frequently mentioned CAMs include allowance for credit losses, business combinations, goodwill, and revenue from customer contracts. The primary predictors of CAMs disclosures are the type of audit firm and reporting quality. The results remain robust compared with support vector machines and logistic regression predictions. These findings are pertinent for regulators, auditors, and researchers aiming to enhance the value relevance of CAMs disclosures in improving audit quality.

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https://doi.org/https://doi.org/10.2308/jeta-2024-009

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@article{zabihollah2025,
  title        = {{Predicting the Class and Type of Critical Audit Matters}},
  author       = {Zabihollah Rezaee et al.},
  journal      = {Journal of Emerging Technologies in Accounting},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.2308/jeta-2024-009},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.