The Robustness of the Determinants of Overall Bank Risks in the MENA Region
Bibliographic record
Abstract
Purpose: The banking sector in the MENA region is exposed to financial risks that originate from both the internal and external environment. Related studies in the literature have reached inconclusive determinants of the overall risks to banks. This paper examines the robustness of the determinants cited in the related literature. Design/methodology/approach: This paper examines the country-specific and bank-specific factors that affect banks’ Z-score (being a proxy for the overall bank risks) in the MENA region. The sample banks consist of 33 listed commercial banks operating in six countries in the MENA region. Balanced panel data over 20 years (2000 to 2020) was examined, having a total of 660 observations. The Pooled Ordinary Least Square estimation (OLS) was used to carry out the empirical analysis. Findings: The findings of this paper showed that the robust determinants of overall bank risks are follows: (a) The unemployment rate had a negative effect on high overall bank risks in the period 2000–2010, (b) The financial crisis had a positive effect on the MENA overall bank risks in the period 2000–2010, but only for the low overall bank risks, (c) A robust and negative effect of cost/income ratio was observed in the period 2010–2020 only for high overall bank risk, (d) Low overall-risk banks were able to manage overall risks in a shorter time than high overall bank risks, and (e) In terms of the country-wide effect, the results for Egypt only showed that the overall bank risk had positive effects in the period 2000–2010, but negative and significant effects in the period 2011–2020 where overall bank risks reduced. Originality: This paper offers robust findings in the controversy around the determinants of overall bank risks in the MENA region, which is beneficial in light of the fact that the literature thus far has not reached a consensus regarding this issue.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".