The Likelihood of Fraudulent Financial Reporting: The New Implementation of Malaysian Code of Corporate Governance (MCCG) 2017
Bibliographic record
Abstract
On 26 April 2017 Securities Commission Malaysia has released new Malaysian Code of Corporate Governance (MCCG 2017) replacing MCCG 2012 with several changes and recommendations to enhance corporate’s accountability, transparency and sustainability. Therefore, the objective of this study is to compare the degree of compliance of this new MCCG 2017 among healthy companies and likelihood of fraudulent financial reporting companies using PN17 companies as a proxy. This study used content analysis of MCCG 2017 and disclosures provided in the annual report of the companies and analyzed it using descriptive statistics. We find that the degree of compliance even among healthy companies in Malaysia in terms of board diversity and board remuneration is still insufficient, and some of the companies are still reluctant to comply. This study provides initial evidence on the effect of new amendment of MCCG 2017 on the likelihood of fraudulent financial reporting in Malaysia.
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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 | 0.011 | 0.059 |
| 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 | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".