<scp>PCAOB</scp> inspection deficiencies and future financial reporting quality: Do the types of deficiencies matter?
Bibliographic record
Abstract
Abstract This study examines whether PCAOB inspection reports are useful for signaling the risk of misstatements in future periods and the extent to which different types of audit deficiencies predict future misstatements. We find that, after the inspection report is issued, PCAOB‐identified audit deficiencies are positively associated with future misstatements for the audit firm's entire client portfolio. When we examine different types of deficiencies, we find that an auditor's failure to understand the client's accounting procedures or policies is the most detrimental type of deficiency for future reporting quality. We also examine the deficiency types for Big 4 versus non–Big 4 firms separately. The results show that an auditor's failure to understand the client's accounting procedures or policies is the only deficiency type that is positively associated with future misstatements for Big 4 firms. For non–Big 4 firms, however, future misstatements are predicted by an auditor's failure to understand the client's accounting procedures or policies, inadequate substantive testing, and inadequate going‐concern assessments. Our study has important implications given the concerns raised by auditors regarding the usefulness of PCAOB inspections.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.010 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".