Profitability of Banks in Lebanon: Some Theoretical and Empirical Results
Bibliographic record
Abstract
The paper, instead of relying on ad hoc measures, derives a simple theoretical model for the income of a commercial bank. This model identifies eight internal exogenous factors to the profitability of these banks. A total panel of 39 banks over the twelve-year period 2003-2014 is studied. The dependent variable is taken to be the return on average total assets (ROAA). The estimation procedure is through panel least squares. Fixed effects and random effects are considered. The results support the cross section fixed effects model, which brings to light the heterogeneity of banks in Lebanon. Four out of the eight factors are found to be statistically highly significant, explaining about 50% of the variation in ROAA. These are: the interest rate spread, the capital adequacy ratio, the cost to income ratio, and the ratio of non-interest income to total assets. Dynamics are included in the model by adding to the regressors the first lag of the dependent variable. This makes for different short run and long run impacts, with the latter found to be higher than the former as economic theory postulates. Among other recommendations banks are advised to diversify their income towards more wealth management and investment banking, to pay particular attention to their traditional source of income, which is the interest rate spread between loans and deposits, and to manage carefully their cost structure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".