Bibliographic record
Abstract
The purpose of this study was to determine the effect of variable Liquidity risk, credit risk, market risk, and operational risk toward CAR on Foreign Exchange National Private Commercial Bank simultaneously and partially. The sample used in this research is a Bank Maybank Indonesia, Bank OCBC NISP, Bank Permata and Pan Indonesia Bank. This research period starting from the first quarter of 2010 until the second quarter of 2015. The technique of data analysis in this research is descriptive analysis and multiple linier regression analysis. The results provides evidence that LDR, IPR, NPL, APB, IRR, PDN, BOPO and FBIR have significant influence simultaneously toward CAR on Foreign Exchange National Private Commercial Bank. IPR partially has positive significant influence toward CAR on Foreign Exchange National Private Commercial Bank. LDR, IRR, and FBIR partially have influence positive unsignificant toward CAR on Foreign Exchange National Private Commercial Bank. NPL, APB, PDN, and BOPO partially have influence negative unsignificant toward CAR on Foreign Exchange National Private Commercial Bank. Among the eight independent variables LDR, IPR, NPL, APB, IRR , PDN, BOPO and FBIR the most dominant influence on CAR is IPR. Key word : Liquidity risk, credit risk, market risk, operational risk , and CAR.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".