Risk Management in Lebanese Banks – Relationship with Financial Performance
Bibliographic record
Abstract
The banking sector has always had a spontaneous comprehensiveness of risk, mainly risks that arise from the increased diversity and complications of banking business and from the countless new drivers of development that encouraged the outlines of risk management in banks beyond what probably have been available in the more traditional forms of banking activity of taking deposits and loaning in comparatively steady environments. There exists a lot of pressure for procedures towards risk management at Lebanese banks, which had paced forward in this field between 2013 and 2017. This research paper studies the effect of new variations in risk management Basle III Accords on banks’ performance. Since this change came in an attempt to strengthen global capital, they have introduced the safety credit margins and financial leverage rates and introduced indicators for liquidity risk management in order to provide banks with better performance during pressure and crises periods. Two hypotheses were tested on a sample of banks taking variables into account, such as profitability and growth ratios with credit quality and liquidity ratios. The researchers used two regression models to test the hypotheses, and the study results had supported the hypotheses. Findings of the first empirical study exhibited a negative relationship between performance as relative to profitability (ROA, ROE) and the management of credit risk as relative to non-performing loans and loss of reserves. The results show a positive relationship between capital adequacy and credit risk, since better managing of defaulted loans increased capital adequacy. However, the results of the second empirical study presented a positive association between performance as relative to Equity growth & Assets growth with the liquidity risk management as relative to Liquidity ratios, loans to deposits, yet not as strong as the first test since the variables used explain a minor portion of the disparity of the performance measures.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".