Towards Early Critical Warnings of Lebanese Banks: An Analytical CAMELS Study
Bibliographic record
Abstract
The stability of the Lebanese banking sector has been jeopardized following the financial crisis that unfolded in 2019. This instability manifested through a loss of customer confidence, widespread doubts about the banks’ ability to repay deposits, indirect control over capital, and restrictions on withdrawals, particularly in foreign currencies. As a result, the Lebanese banking sector, once regarded as the backbone of the economy, now faces an existential challenge. The financial health of the country has deteriorated significantly, especially after the Lebanese government declared bankruptcy on euro bonds held by local commercial banks, coupled with the depreciation of the Lebanese pound against foreign currencies. To evaluate the potential for recovery and the challenges ahead, a study of ten Lebanese commercial banks was conducted using the CAMELS model, which examines capital adequacy, asset quality, management quality, profitability, liquidity, and sensitivity to market risk. The findings reveal that by 2022, all assessed banks received a CAMELS score of 4, reflecting the profound financial and economic crisis in Lebanon.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".