Comparative and Demonstrative Study Between the Liquidity of Islamic and Conventional Banks in a Financial Stability Period: Which Type of Banks Is the Most Liquid?
Bibliographic record
Abstract
Due to the failure of several conventional banks and the closure of some Islamic banks around the world, both types are exposed to the risk of liquidation and bankruptcy. Theoretically, knowledge production has until recently been the monopoly of academic research (Vinck, 2000). The choice of the most liquid type of bank and which maximizes the liquidity of its customers is a problem to be solved. Since most of the previous studies that have dealt with relative or comparative banking liquidity are unconfirming, our research interest is to overcome these constraints in order to provide a more optimistic answer. Two samples were removed from two reference populations over the period (2010-2018). Samples were selected from 16 countries. Basic populations consist of all existing conventional and Islamic banks in the selected countries. The choice of banks is limited to countries whose banking systems incorporate both types. Subsequently, the list for each type of banks was reduced on the basis of qualitative and quantitative filtering criteria. Therefore, each conventional bank has its closest Islamic equivalence in terms of capital and size taken from the same country. This restriction reduced the sample size to 63 large banks each. All selected banks were listed in different stock exchanges around the world. Empirical results showed that Islamic banks are more liquid than conventional banks during a financial stable period.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".