Highlighting Differences in Cash Flow from Investing Activities and Capital Adequacy Ratio Relationship between Indonesian and Malaysian Commercial Banks
Bibliographic record
Abstract
This study compares the impact of Cash Flow from Investing Activities (CFI) on the Capital Adequacy Ratio (CAR) between Indonesian and Malaysian commercial banks. The study uses time-series data from the countries' big-five banks from 2009 to 2013. This study engages the regression model to measure the CFI's impact on CAR and then applies a Chow test to compare the discrete regression output between the two countries. The statistical tests reveal that the CFI of Indonesian banks correlated negatively with the CAR, while Malaysian banks showed a positive correlation. Consistent with the correlations, the influence of CFI on CAR in both countries is equally significant. By a Chow test, this study concluded that the CFI's impact on CAR significantly differs between the banks in the countries. From the literature perspective, the CFI and CAR relationship of Malaysian banks is closer to the findings of previous detached studies. The figures indicate that nowadays, Malaysian banking harvests cash inflow from their past investments. Otherwise, the Indonesian banks portray the ongoing spending for long-term investment during the recent five years. This preference results in a consistent decrease in CFI when the CAR moves upward. Departing from the existing differences, to optimize the CFI and CAR relationship, this study suggests the CFI and CAR equilibrium formulation for banks to avoid cash shortages and failure to earn interest due to capital buffer retaining to maintain a high ratio of capital adequacy.
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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 | 0.006 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".