Consequences of Expanded Audit Reports: Evidence from the Justifications of Assessments in France
Bibliographic record
Abstract
SUMMARY Since 2003, French auditors must disclose justifications of assessments (JOAs) in expanded audit reports. Like critical audit matters recently introduced in the U.S., and key audit matters introduced by international standard setters, the purpose of JOAs is to enhance the informative value of audit reports. Based on French audit reports from 2002 to 2011, we analyze the impact of first-time implementation of JOAs, and the impact of new JOAs in subsequent years, on investors (measured by abnormal returns and abnormal trading volume) and on the audit (measured by audit report lag, abnormal accruals, and audit fees). For both first-time implementation of JOAs and new JOAs in subsequent years, we find no significant market reaction to their disclosure and no significant effect on audit report lag, audit quality, and audit fees. Our results suggest that the French expanded audit report did not have the expected consequences on investors and the audit.
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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 | 0.026 | 0.147 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".