Auditing, integral approach to quarterly reporting, and cosmetic earnings management
Bibliographic record
Abstract
Purpose This study examines the effect of auditing and the integral approach to interim reporting on cosmetic earnings management, referred by Kinnunen and Koskela as earnings manipulative behavior to report earnings numbers to achieve key cognitive reference points represented by N×10k. Design/methodology/approach Using Benford's Law, the analysis employs 182,278 positive quarterly earnings observations and 103,470 negative quarterly observations for all publicly listed US companies from 1993 to 2003. Findings The empirical results show that firms tended to engage in cosmetic earnings management in each of the four fiscal quarters. More importantly, it was found that the degree of cosmetic earnings management is significantly less severe in the fourth fiscal quarter, which is the only quarter audited, than any of the previous quarters. This result suggests that the auditor plays an important role in reducing the cosmetic earnings manipulative behavior. Originality/value The findings of the study add more evidence to the ongoing debate about the effectiveness of auditing in preventing earnings management.
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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 | 0.004 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".