Auditor Expertise and Bank Failure: Do Going Concern Opinions Predict Bank Closure?
Bibliographic record
Abstract
This study investigates how the quality of engagement auditors, assessed using the auditor’s industry expertise and size at both national and state levels, influences the likelihood of going concern opinion (GCO) issuance for U.S. banks from 2002 to 2023. We also examine how auditor quality affects the accuracy of GCOs, specifically regarding Type I (false positive) and Type II (false negative) errors in GCO issuance. Using a dataset of 4992 bank-year observations from 414 unique banks, we analyze the correlations between auditor characteristics and these error types. We find that state-level audit industry experts issue significantly more accurate GCOs, demonstrating lower rates of both Type I and Type II errors compared to their counterparts. National-level experts and larger audit firms primarily show a reduced likelihood of Type II errors, indicating a more conservative approach. Our findings underscore the importance of localized auditor expertise in assessing bank financial health and suggest that enhanced collaboration between auditors and regulators could improve the predictive power of GCOs. These results offer important implications for regulatory policy and emphasize the need for improved audit standards to bolster financial system stability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".