What Determines Banks’ Profitability? Evidence from Emerging Markets—the Case of the UAE Banking Sector
Bibliographic record
Abstract
The primary objective of this study is to examine the variables that impact the profitability of UAE banks. The current study provides evidence of important bank-specific, macroeconomic, and industry-specific variables that have affected UAE banks’ profitability by analyzing balanced panel data for 2006 to 2013. Both Islamic and non-Islamic, domestic commercial banks are considered for the purposes of this study. This paper puts into relief the determinants of the profitability of the domestic commercial banking sector of the UAE. The sample comprises 19 UAE domestic banks. The paper examines internal variables (company-level indicators), which include size, liquidity, and capital adequacy, as well as external variables, which include macroeconomic and industry-specific variables. Panel data regression analysis is used for the analysis. Based on the empirical analysis, the cost efficiency, nontraditional revenue sources, and high asset quality are the most significant bank-specific variables, and bank managers can use them to make future policy decisions. The GDP, a macroeconomic variable, is found to be relevant to the return on assets and return on equity. The model generated in the study can explain a greater than 75% change in the total variance of various measures of profitability. This paper adds to the body of knowledge by empirically highlighting the most recent and extensive panel data for the entire domestic banking sector of the UAE, undoubtedly one of the most important banking sectors in the Middle East. The paper uses a range of independent variables for the internal, macroeconomic, and industry-specific variables.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".