Ranking methodology for Islamic banking sectors – modification of the conventional CAMELS method
Bibliographic record
Abstract
The state of banking systems is an important issue. The purpose of this paper was to test whether the well-known CAMELS microeconomic methodology, generally used for ranking banks, is applicable to evaluating Islamic banking systems. The hypothesis was tested by implementing a method for a particular case, public, free data – from 2013 till the first quarter of 2018 – on Islamic banking systems from the “Islamic Financial Services Board” (IFBS) database. As expected, modifications were necessary. First, because of the lack of data (in Islamic databases, no data refer to the management (“M”)), and second, to avoid the subjectivity of the five-degree method and to reach more sensibility. Thus, a hundred-level (standardized) rating system was introduced – “CAELS 100”, where “100” refers to the levels. The other part of the methodology – creating a simple average of the (now level 100) rating of raw indicators to get the letters of CA(M)ELS in the relevant period – remained unchanged. After the data cleaning, only six countries (Bahrain, Egypt, Kuwait, Oman, Turkey, and the United Arab Emirates) were able to participate in the analysis.The result showed that Egypt, Turkey and Kuwait were the best ones respectively. Thus, it was concluded that this “CAELS 100” methodology is suitable for evaluating Islamic banking systems. AcknowledgmentThe research was supported by the project “Intelligent specialization program at Kaposvár University”, No. EFOP-3.6.1-16-2016-00007.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".