Systematic and unsystematic determinants of liquidity risk in the Islamic banks in the middle east
Bibliographic record
Abstract
Liquidity risk (LR) is a concern in Islamic banks and may lead to major problems if not managed appropriately and planned, due to the lack of external liquidity sources for Islamic banks. However, the purpose of this article is to look at the factors that affect liquidity risk in Middle Eastern Islamic banks. To arrive at a substantial and compelling conclusion, the cross-sectional data from 30 Islamic banks was gathered between 2011 and 2022. The random effect regression model, GMM, and fixed effect regression model were all utilized. According to the report, Islamic banks in the Middle East have safe levels of liquidity. It also demonstrates how the financing-to-deposit ratio, inflation, economic growth, and return on assets all have a favorable impact on Islamic banks' liquidity risks. Furthermore, the study discovered that non-performing financing, capital sufficiency, operational effectiveness, and scale had no bearing on the liquidity issues associated with Islamic banks. This paper provided guidance regarding liquidity risk management procedures and systems in Islamic banks in order to design banking liquidity risk management policies. To avoid liquidity risks in Islamic banks, the optimal level of financing to deposit ratio must be determined, maintaining the quality of financing, reducing the non-performing loan ratio to the lowest possible level, and enabling Islamic banks to benefit from the central bank as a last resort for liquidity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".