The effect of capital and liquidity risks on financial performance: An empirical examination on banking industry
Bibliographic record
Abstract
The present study's primary goal is to examine selected financial risks and financial performance of commercial banks listed on the Bahrain Bourse from 2014 to 2021. However, as independent factors, chosen financial hazards include capital risk, liquidity, and bank size as a control variable, while financial performance as a dependent variable is assessed by return on equity. The panel regression analysis of data technique was used to attain the study goal. Whereas the statistics for the banks were gathered from their annual financial reports. A fascinating conclusion was the discovery of strong correlations between capital risks, bank size, and financial performance. The findings also revealed a negligible link between liquidity concerns and financial success. Due to the limitations of the present study, several ideas for future research may be suggested, such as performing research on other financial hazards, other financial institutions, and other financial performance metrics that are not included in the current research.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".