Effects of Internal and External Factors on Profitability of Jordanian Commercial Banks: Panel Data Approach
Bibliographic record
Abstract
This article assesses the effects of internal and external factors on the profitability of Jordanian commercial banks. A panel data set of thirteen commercial banks between 2000 and 2018 was used. Pooled ordinary least squares, random and fixed models were applied. Moreover, a Hausman test was performed to confirm the suitability of models, which was preferred on the random effect model. Also, a Wooldridge test for serial correlation and a modified Wald test for groupwise Heteroskedasticity were used and both of their null hypotheses were rejected. However, to deal with these problems, a robustness analysis was performed using feasible generalized least square. The findings suggested that internal factors and in particular, bank size and diversification, had positive effects on bank profitability, while credit risk, operational risk and leverage risk were negatively related to bank performance. However, capital risk had a positive but insignificant impact on bank profitability. As for the effect of external factors, the results suggested that financial development and inflation had a positive and significant impact on bank profitability, while market concentration and stock market volatility had a significant negative effect on bank profitability. Further, a negative and insignificant impact were found for GDP and refugee crisis on bank profitability in Jordan. The findings would help managers of commercial banks, investors, government, policy makers and shareholders to make better decisions and improve performance by highlighting areas of weaknesses. In general, policy makers should become more aware with these insights on profit determinants in Jordanian commercial banks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".