The Loss of Information Associated with Binary Audit Reports: Evidence from Auditors' Internal Control and Going Concern Opinions
Bibliographic record
Abstract
ABSTRACT This study provides evidence that binary signals in audit reports are unable to fully communicate underlying risks that are inherently continuous in nature. Specifically, we find that companies whose audit reports signal an improvement in internal control effectiveness relative to the prior year are still more likely to subsequently restate the current year's financial statements than companies with no material weaknesses in either year. Similarly, companies deemed to no longer have substantial doubt of continuing as a going concern are still more likely to declare bankruptcy than companies with no going concern opinion in either year. Results in both settings suggest the presence of residual risk that cannot be communicated through a binary audit report, despite the fact that auditors recognize the risk, as evidenced by higher audit fees and longer audit report lags. Our findings are strongest when the reported improvement is more pronounced, and our results hold in matched samples. Our study provides empirical evidence that supports recent regulatory efforts to improve the content of the audit report and offers suggestions for future research.
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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 | 0.028 | 0.371 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".