Characteristics of banks as determinants of profit management for Islamic and conventional banks in ASEAN
Bibliographic record
Abstract
This study aims to analyze company characteristics as a determinant of conventional and Islamic bank earnings management in several ASEAN countries (Association of South East Asian Nations). The Multiple Discriminant Analysis was applied to determine the differences between Islamic and Conventional Banks. This test was conducted based on Capital Adequacy Ratio, Income Before Tax and Interest, Non-Performing and Changing Loans, and Company's Size in the banks of Indonesia, Malaysia, and Brunei Darussalam from 2014 to 2018. The data obtained from 200 banking entities were analyzed discriminatively. The results showed that there were simultaneous differences between Capital Adequacy Ratio, Earnings Before Tax, Loan Loss Provision, Non-Performing and Changing Loans, and Company's Size as determinants of earnings management between Islamic and conventional banks. Also, it was found that Company's Size was the dominant variable determining the management differences. Based on Discriminant Analysis, there were significant differences in the determinants of conventional and Islamic earnings management. The Changing Loan variable showed the highest contribution in determining earnings management in Islamic banks. Overall, this study found that conventional banks dominated Islamic system in practicing 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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".