The fluctuation of exchange rate and its effect on commercial bank performance
Bibliographic record
Abstract
The present study examines the effect of exchange rate fluctuation and bank size on the financial performance of top commercial banks of Afghanistan (Azizi Bank, Afghan United Bank, Afghanistan International Bank, Islamic Bank of Afghanistan) for the period from the first quarter of 2016 to the fourth quarter of 2020, where Return on Equity (ROE) is the most important indicator of measuring performance. Using panel data regression estimates spanning across four banking institutions, the evidence reveals that bank size, in the form of the natural logarithm of total assets, exerts a significant and statistically positive impact on ROE, whereas exchange rate fluctuation exhibits a statistically insignificant but positive relationship with profitability. Sufficient diagnostic checks validate the integrity and consistency of the model. These results indicate that bank size is a more fundamental determinant than exchange rate fluctuation with respect to profitability in the banking sector of Afghanistan. The study emphasizes the importance of diversification and expansion strategies in financial institutions in the process of building them into robust and resilient institutions against the influence of a poor economic environment. The conclusions here carry high policy relevance for policymakers and bank chief executives who seek to promote financial stability and sustainable development within Afghanistan's nascent financial sector.
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".