Debt Covenant Violations, Firm Financial Distress, and Auditor Actions
Bibliographic record
Abstract
Abstract We conduct a comprehensive study on the associations between debt covenant violations (“violations”) and auditor actions for financially distressed and nondistressed firms. Our study is motivated by a lack of research on the consequences of violations resulting from auditors' actions. We find that firms with violations have significantly higher audit fees, a greater likelihood of receiving a going‐concern opinion, and a greater likelihood of experiencing an auditor resignation. Importantly, the positive associations hold for all types of firms, including financially nondistressed firms. In fact, we find that, after controlling for other financial information, the relation between violations and an increased likelihood of a going‐concern opinion is stronger for nondistressed versus distressed firms. Our evidence is consistent with belief‐revision research in auditing that finds auditors react more strongly to information that is inconsistent with their prior beliefs. This study provides further evidence on the indirect yet significant consequences of covenant violations on firms resulting from auditor actions.
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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 | 0.003 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".