Bibliographic record
Abstract
Purpose Prior research has examined the impact of corporate governance mechanisms, including external auditing, on accounting restatements likelihood. However, little is known about auditor’s monitoring role in restatement disclosure practices. The purpose of this study is to address this gap by investigating the impact of auditor’s oversight on the timeliness of accounting restatement disclosures as measured by the length of the restatement dark period. Design/methodology/approach The study examines panel data from a sample of restating publicly traded US firms. Negative binomial regression is used to analyze the data because the dependent variable is a count variable and is over-dispersed. Findings The main study’s results indicate that longer auditor tenure and non-audit services provision improve restatement disclosure timeliness. Conversely, companies whose auditors exerted abnormally high levels of audit effort have longer restatement dark periods. Originality/value This study is the first archival research that focuses on auditor’s monitoring role and its impact on the timeliness of restatement disclosures. By doing so, this study contributes to the auditing academic research, professional practice and regulation by providing empirical evidence on an exasperating issue for all participants in the financial markets. In addition, it provides a better understanding of auditor’s monitoring role in the accounting restatement process and offers insights to policymakers, practitioners and investors interested in corporate financial transparency and corporate governance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.091 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".