Are The Sudanese Banks Financially Sound?
Bibliographic record
Abstract
This study attempts to investigate whether the banks operating in Sudan are solvent and financially sound as well as to examine whether there are roots for a banking panic. The research is also intended to determine whether there are candidate bankrupt banks. The study employs quantitative and qualitative research methods and utilizes both secondary and primary data and covers the eight-year period 2013-2020. The annual audited financial reports of banks published for the period under study represent the source of the secondary data and the primary data is collected through questionnaires distributed to depositors. The sample comprises 30 banks out of a total population of 37 banks. Also, responses from 416 participants in the questionnaire are considered for constructing the depositors’ confidence index. To test the hypotheses a number of quantitative models, namely, univariate financial ratios models, Ahmed (2003) Z-score model, Altman (2002) emergent markets Z-score model, and depositor’s confidence index (DCI) model are utilized. The statistical results of three out of the four models, namely, the univariate financial ratios model, Ahmed (2003) Z-score model, and depositor’s confidence index, document that banks operating in Sudan are financially unsound and financially distressed and none of those banks is thoroughly healthy. However, the results of EM Z-score model show that banks operating in Sudan can be categorized as healthy and financially sound and that there are no roots for a banking panic in the country.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".