Financial derivatives and the commercial banks performance in UAE
Bibliographic record
Abstract
The introduction of derivatives at the financial market of the United Arab Emirate (UAE) is to enhance liquidity and broadens the range of securities. This is because it brings exciting opportunities for investors to diversify their investment in an efficient and cost-effective way. Evidence from previous studies has shown that financial market derivatives help to reduce risk. Even though trading losses produced by unsuitable derivative activity are frequently big enough to create financial problems and even bankruptcy, there is minimal research on how bank profitability and performance are affected. The study examines the determinants of financial derivatives on the performance of commercial banks in UAE and the financial risk exposure between derivatives financial assets and derivatives financial liabilities. The research employs Pecking order theory, panel ARDL and data from 30 commercial banks’ financial statements in 2020 in UAE. The study found that an increase in the level of return on assets will create an increase in traded financial derivatives that will enhance bank performance by a high level of percentage. Stability of the banking sector in UAE is recommended to enhance better performances of commercial banks on financial derivatives in UAE.
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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 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".