Do Client Bankruptcies Preceded by Clean Audit Opinions Damage Auditor Reputation?<sup><b>*</b></sup>
Bibliographic record
Abstract
ABSTRACT There is a maintained assumption within the accounting literature that client bankruptcies preceded by clean audit opinions (Type II going concern opinion (GCO) errors) damage an auditor's reputation. Consistent with this view, the PCAOB proposes that stakeholders may use Type II GCO errors as indicators of low audit quality. This study examines audit committee and investor responses to Type II GCO errors. I find no evidence that audit offices with Type II GCOs are more likely to be dismissed, have lower subsequent audit fees, or have a lower likelihood of being selected for new audit engagements. These findings are consistent with audit committees not using Type II GCO errors as indicators of low auditor quality. Using event study analysis, I find evidence of modest incremental negative investor responses for clients of audit offices with Type II GCO errors. However, these negative investor responses are found only during the financial crisis period of 2008–2010 and are observed only within windows of 30 days or less. Given this limited evidence that stakeholders do respond to Type II GCO errors, I examine whether stakeholders should respond to Type II GCO errors. I find that audit office Type II GCO errors are positively associated with subsequent restatements, an established measure of low audit quality. Taking the results as a whole, I do not find that audit offices incur substantial reputational costs for Type II GCO errors. However, the negative investor response and the positive association with restatements provide some evidence that Type II GCO errors may serve as indicators of low audit quality.
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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 | 0.009 | 0.107 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.024 | 0.003 |
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".