The effect of liquidity risk on bank performance: A comparative study of Islamic and conventional banks in the middle east and north Africa region
Bibliographic record
Abstract
This study explores the impact of liquidity risk on Bank performance through a comparative study between conventional and Islamic banks in the Middle East and North Africa Region (MENA). Bank Size, Capital adequacy ratio, liquidity Gap and Return on Assets are used as independent variables and the Bank Age, Inflation Rate and Growth Rate of Domestic product are used as macro-economic variables and the dependent variable is liquidity risk. The methodological choice is the generalized method of moments (GMM). We used a sample of 10 Islamic banks and 25 conventional banks in the MENA region during the period of 2006-2018. The results show various impacts of these variables on liquidity risk in both banks. We also find that the rise in CAR in Islamic banks and conventional banks does not influence liquidity risk. The logical explanations are that the bank could allocate funds to improve credit and fixed assets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".