The Effect of Financial Leverage on The Islamic Banks’s Performance in The Gulf Cooperation Council (GCC) Countries
Bibliographic record
Abstract
This study examines the impact of the financial leverage on the Islamic banks’ performance in the GCC countries during the period from 2005-2017. The population of this study included the Islamic banks in the GCC countries. Thirteen years data of 25 listed Islamic banks in the GCC countries were used, wereby these data were retrieved from the Thomson Reuters DataStream. This study utilized the fixed effect regression model. The findings show that the financial leverage a has significant impact on the performance of the Islamic banks’ performance in the GCC region. More specifically, the financial leverage has a positive and significant impact on ROA, ROE, and Tobin’s Q of the Islamic banks in the GCC countries, thus indicating that the higher is the financial leverage the higher is the performance of the Islamic banks in the GCC region. However, the results of this study do not provide evidence to support the Agency Cost Theory that implies a decrease in the performance when equity ratio is increased. On the other hand, the findings provide evidence to support the Signaling Theory that argues that banks are expected to have a better performance credibly in transmitting this information through the higher capital. The findings imply that the level of financial leverage committed by the Islamic banks depends on their flexibility in adjusting their debt value and earning power.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".