Save money to lose money? Implications of opting out of a voluntary audit review for a firm’s cost of debt
Bibliographic record
Abstract
An audit review (AR) is a mechanism used by boards to assess the quality of interim financial reports on a timely basis. In Canada, the AR is voluntary, with listed firms mandated to disclose when they choose not to purchase additional audit verification. Given the relatively low cost of an AR, opting out of it can be regarded as a negative signal, especially in the context of lenders’ sensitivity to downside risk. Using a sample of 7,585 firm-year observations from 1,616 public firms in Canada over the period 2004-2015, we document that firms without a voluntary AR have a higher cost of debt than firms with an AR. Furthermore, after firms opt out of the AR, the increase in the cost of debt is accompanied by a rise in discretionary abnormal accruals and managers’ stock-based compensation. Moreover, no-AR firms are more likely to reduce post-switch private borrowing and have lower equity analyst following. Our study is the first to document that although listed borrowers that opt out of an AR have a higher cost of debt financing, they are concurrently able to engage in more earnings management and grant their managers higher stock-based compensation because of lower external monitoring.
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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 | 0.007 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".