Benefits and Costs of Auditor's Assurance: Evidence from the Review of Quarterly Financial Statements
Bibliographic record
Abstract
Abstract Even though there is a worldwide consensus as to the necessity of an audit of annual financial statements for public companies, there is divergence of views as to the review of interim financial statements. While some jurisdictions make it mandatory (e.g., Australia, France, United States), others allow the review without requiring it (e.g., Canada, United Kingdom). Using a sample of companies listed in Canada, we examine the costs associated with these reviews and the benefits they generate in terms of improvement in the quality of interim financial statements for the years 2004 and 2005. Controlling for the decision to purchase the reviews, we find that audit fees are 18 percent higher for firms with interim reviews and, contrary to many regulators' assumption, we find no evidence that this cost increase is proportionally higher for smaller firms. Regarding the benefits of interim reviews, we find no significant association between either accruals‐ or nonaccruals‐based measures of earnings management and the fact that the interim statements are reviewed by the auditor, neither in the interim reports nor in those of the fourth quarter. The results suggest that auditors' involvement with interim reports may not be as effective as previously thought at controlling the quality of interim financial statements.
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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 | 0.037 | 0.355 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".