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 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 | 0.005 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".