Does Liquidity Influence Profitability in Islamic Banks of Bahrain: An Empirical Study?
Bibliographic record
Abstract
This study examines the impact of liquidity on Islamic banks’ profitability during the years from 2010 to 2015. The study extracted its data from the annual reports of six Islamic banks in Bahrain that have been in operations on or before 2010 to 2015. The liquidity model is built from four liquidity variables namely cash & due from banks to total assets (CDTA), cash & due from banks to total deposits (CDTD), investment to total assets (INVSTA) and investment to total deposits (INVSTD). According to adjusted R squares profitability variables return on assets (ROA), return on equity (ROE) and return on deposits (ROD) are respectively 16.2%, 3.1% and 21.3% dependent on liquidity variables.The results of the study show that CDTD and INVESTD are correlated positively with ROE. In addition, CDTD, INVSTA indicate a negative correlation with ROE. Thus, only INVSTA and INVSTD found to be significant with ROE at 0.05 significant level. Durbin-Watson test shows that the residuals are uncorrelated since its value is approximately very close to 2. However, according to the P-value, the overall liquidity model (Model 2) is not significantly related with ROE. Thus, the null hypothesis (H0) is accepted and the alternative hypothesis is rejected for the ROE. Furthermore, the results in the table show that CDTA and INVESTD are positively correlated with ROD, and negatively with CDTA and INVSTA, and CDTA is the only insignificant variable. CDTD is significantly related with ROD at 10%. Durbin-Watson test shows that the residuals are positive auto - correlated since its value is approximately very close to 1. However, according to the P-value, the overall liquidity model (Model 3), is significantly related with ROD at 1% level.The researcher recommended for further studies to add more liquidity variables to the model so as to enhance and enrich Islamic banks outlook.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".