Expanded Auditor's Report Disclosures and Loan Contracting*
Bibliographic record
Abstract
ABSTRACT Starting in October 2013, auditors of premium‐listed firms in the United Kingdom are mandated to prepare an expanded auditor's report that provides details on audit procedures, risks of material misstatement (RMMs), and materiality thresholds. This regulatory change is important to study, because it aims to increase the informational value of the traditional, highly standardized, pass‐or‐fail auditor's report. We examine whether the disclosures in the expanded auditor's report provide information that is relevant for adopting firms' loan contracting terms in the post‐adoption period. Our results indicate that the introduction of the expanded auditor's report is associated with reduced loan spread and longer maturity for loan facilities of adopting firms relative to non‐adopting UK firms. When we focus on adopting firms in the post‐adoption period, we find that the number of “unique RMMs” mentioned in the auditor's report, but not in the audit committee report, are positively associated with loan spread but are not associated either with loan maturity or the number of lenders in the loan syndicate. Additional tests show that the benefits, in terms of a reduced spread, of having a lower number of “unique RMMs” accrue mostly to adopters with a poor information environment. Taken together, our results provide preliminary evidence that the expanded auditor's report disclosures contain relevant information for loan contracting in the United Kingdom. This study highlights the unique role of the expanded auditor's report in providing information relevant to lenders and supports standard setters' efforts to enrich its informational content.
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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 | 0.011 | 0.121 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".