The Determinants of Banks’ Profitability under Basel Regulations: Evidence from Lebanon
Bibliographic record
Abstract
The purpose of this study is to shed some lights on the determinants of banks’ profitability operating in Lebanon. Through applying Panel “EGLS period SUR” technique, for the period spans from 2000 to 2015. We have used a set of micro factors that might affect the banks’ profitability such as; asset quality, liquidity, and capital adequacy, on a sample of twenty four banks operating in Lebanon. Net Interest Margin (NIM) has been used to measure the profitability. The results indicate that most positive powerful effects on NIM are Equity to Liability, and Interest rate on Deposits (on Average), and to a lower extent Loan Loss Reserve to Impairment Loans, the Impaired Loans to Equity, Liquid Assets to Total Deposits and Borrowings, whereas, Capital Funds to Liability, loan loss provision to net interest revenue, are the most significant but with a negative effect; and to lower extent Net charge Off to Average gross loans, Net loans to deposits and short term borrowing affect the NIM negatively. Our findings revealed that banks perform better when they maintain higher level of equity relative to their Liabilities, and then can achieve a higher level of profitability.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".