The Effect of the Third Party Fund: Its Distribution and Fluctuation on the BOPO Growth at Commercial Foreign Exchange Banks in Indonesia
Bibliographic record
Abstract
For comercial banks, funding activities include savings deposits and time deposits. Lending activities also include commercial papers, credits, inter-bank placement, and exchange rate. The study attempted to reveal the effects of such factors on the BOPO growth in commercial foreign exchange banks. It utilized secondary data from bank’s financial reports and exchange rate, during the first quarter 2006 to third quarter of 2011. The study is descriptive and the sampling technique of census was utilized with criteria for total assets. Four state-owned banks meet these criteria: PT. Bank CIMB Niaga Tbk., PT. Bank Danamon Tbk., PT. Pan Indonesia Tbk., and PT. Bank Permata Tbk. The analysis was done by performing mathematical calculations and statistics from various financial ratios that reflect the growth of savings products and their distribution. It shows that the saving deposits, time deposit, commercial papers, credits, inter-bank placement, exchange rate have no effect on Overhead Cost Operation (BOPO). The significance is at 13.5 percent. Among the independent variables, only commercial papers have significant effect on Overhead Cost Operation (BOPO). Commercial papers become the most dominant variable at 7.78 percent; the growth of securities variable contributes most to the growth of Overhead Cost Operation Ratio, especially for private national commercial banks.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".