Can Continuous Disclosure Defaults Be Attributed to Big 4 versus Non-Big 4 Auditor Differences?
Bibliographic record
Abstract
ABSTRACT The numerous scandals attributed to lack of independence on the part of Big 4 auditors have reignited one of the most controversial issues in the accounting profession: Do Big 4 auditors provide higher audit quality? The objective of this paper is to examine whether the auditor's reputation affects non-compliance with disclosure obligations and the type and number of defaults detected by the Ontario Securities Commission. Using an internet-based list published by the Ontario Securities Commission in February 2020, I develop a sample of 286 firms consisting of 143 issuers in default and their 143 matching firms. Results show that the presence of a Big 4 auditor is associated negatively with the various default types and the number of defaults. One implication of these findings is that hiring a reputable auditor may prevent firms and shareholders from being on the “shame list.” JEL Classifications: G38; G34; G32.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".