Managing Earnings Management: A Framework of Standards, Governance and Ethics
Bibliographic record
Abstract
Manipulating earnings to superficially represent financial reports is a flagrant violation of management's assertions about the fairness of financial statements. Therefore, auditors have developed objectives to counter management assertions to ensure that financial reporting is credible and free from earnings management practices (EMP). This study examines the role of an integrated system of auditing standards and corporate governance surrounded by a framework of ethical values and principles in reducing EMP through the use of a sequential explanatory mixed method. Through this approach, the results obtained from 131 surveys were used as input for the interviews to add a more in-depth understanding of this phenomenon and find fruitful ways to reduce EMP. The results revealed that management's assertion on “accuracy, valuation and allocation” was most involved in EMP, thus, requiring a high degree of exercising professional skepticism. While “objectivity” was the ethical principle most respected by auditors, the results showed that the most serious threat to auditor independence that poses a challenge to the audit profession is the threat of intimidation. Moreover, the study showed that exercising the supervisory role in holding management accountable and following up on the work of auditors to uncover EMP requires real independence of the audit committee from the executive members. The findings have implications for economic policymakers in emerging countries where the sustainability of the audit profession and public companies is critical to economic development and stability. The study provides insights and guidance in mapping corporate governance for the future and benefiting from the experiences of developed countries as outlined in the study’s conclusion.
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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 | 0.040 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.045 |
| Scholarly communication | 0.018 | 0.010 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".