Corporate Governance Attributes in Fraud Detterence
Bibliographic record
Abstract
The failures of corporations such as Enron, WorldCom and HIH Insurance, to name but a few, have heightened investor awareness of the need to not only evaluate company performance, but also to consider the possibility that financial statements may not be a true reflection of company results, as fraudulent activities may have occurred during the reporting period. Since parties who are outside of the firm do not have access to pertinent information, they have to rely upon published financial and non-financial data to form an opinion regarding performance and/or the risk that fraudulent activities may have occurred. The prior literature shows a relationship between weak corporate governance and fraudulent activities, although most if not all of this research relates to Western economies. The differences in institutional setting e.g. cultural values and legal environment in Malaysia would not give the same findings with the study in western economies. Composing of many ethnicities, Malaysia is a multicultural country. With each ethnic group upholding its own culture, values and belief, businesses are conducted according to each ethnic’s culture. The results of this study could shed some light on the influence of institutional setting regarding corporate governance. Companies that were charged with accounting and auditing offences from year 2003 to 2007 were selected as the fraudulent samples. Data was collected from the years these companies were charged with fraud and the year prior to that. Logistic regression analysis was carried out to determine the significant differences between fraudulent and non-fraudulent companies with respect to corporate governance characteristics. The results indicated that the size of the board and the percentage of institutional shareholdings had significant relationships with the likelihood of corporate fraud occurrences consistently across the two-year period studied. The results of this study will assist public, corporate and accounting policy makers in formulating more effective corporate governance mechanisms.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".