Are the Islamic Banks Really more Profitable than the Conventional Banks in a Financial Stable Period?
Bibliographic record
Abstract
The international banking sector is dominated by the conventional banks but the global market share of the Islamic banks is increasing gradually. This study determines which model maximizes a bank’s profitability in several heterogeneous contexts and over a financial stable period (2010-2018). Most previous studies examined banking profitability in commercial conventional and Islamic banks providing a limited literature. We retained three categories for each type of banks (commercial, investment and universal) because of the predominance of these categories in the money market. Within the framework of this study, two samples were taken from two reference populations. Basic populations were composed of all active conventional and Islamic banks existing in the selected countries. The choice of the banks is limited to the countries whose banking structures incorporate simultaneously the two types of banks independently of the proportion of each system in each country banking market. We then reduced the size of each population focusing on qualitative and quantitative filtering criteria, so that each conventional bank had its Islamic equivalence in terms of capital and size in the same country. This restriction reduced the sample size to 63 large banks for each type. The two banks’ samples were selected from sixteen countries and were listed in different stock exchanges around the world. Consequently, the empirical results showed that the conventional banks were more profitable than the Islamic banks during the period of financial stability.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".