The Effect of the Arab Spring on the Performance of Islamic and Conventional Banks in Egypt: Which Model Performs Better Amidst Crisis?
Bibliographic record
Abstract
This empirical study analyzes financial institutions and performance in times of external crisis and whether a difference in performance between Islamic (IBs) and conventional (CBs) bank models exists. Egypt surrounding the Arab Spring (2009-2013) is taken as a case study, comparing 6 CBs and 3 IBs. Financial ratio analysis is the main method employed, allowing performance to be measured by efficiency, capital adequacy, profitability, solvency, liquidity, and credit risk performance. Due to small sample size, the nonparametric Mann-Whitney U test and effect size analysis assess the significance of the ratio analysis results. Results show CBs have superior performance in all indicators other than Cost-Income and NIM. Efficiency performance for both models were equally volatile or alternately stable with progression through the crisis, while IBs increased capital adequacy and solvency during the crisis. IBs profitability was significantly negatively impacted by the crisis, other than related to NIM, while CBs increased profitability rates. IBs liquidity worsened, then improved midway through the crisis while CBs stabilized liquidity rates throughout. IBs improved credit risk midway through the crisis while CBs declined. Nonparametric results hold observed differences are insignificant and have weak effect size for all but the TENL ratio.
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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 | 0.002 | 0.005 |
| 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 | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".