Understanding Audit Quality: Insights from Audit Professionals and Investors
Bibliographic record
Abstract
Abstract Projects seeking to define, measure, and evaluate audit quality are on the agendas of auditing standards setters as well as audit firms. The Public Company Accounting Oversight Board ( PCAOB ) currently provides information regarding audit quality through the release of inspection reports, and the Board intends to establish and report audit quality indicators. To provide additional perspective on audit quality, we obtain auditors' and investors' views, definitions, and indicators of audit quality. We find that investors' definitions of audit quality focus more on inputs to the audit process than do auditors', and that investors view the number of PCAOB deficiencies as an indicator of overall firm quality. We find a consensus that auditor characteristics may be the most important determinants of audit quality, and that restatements may be the most readily available signal of low audit quality. We relate responses to a general audit quality framework, provide support for archival audit research, and identify additional disclosures that participants suggest could signal audit quality. Taken together, we provide evidence regarding the construct of audit quality in the post‐ SOX environment, evaluate many of the audit quality indicators proposed by the PCAOB , and suggest avenues for future research.
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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 | 0.018 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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, 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".