The Effect of Applying Hedge Accounting in Reducing Future Financial Risks in Jordanian Commercial Banks
Bibliographic record
Abstract
The Effect of Applying Hedge Accounting in Reducing Future Financial Risks in Jordanian Commercial Banks The study aimed to identify the effect of applying hedge accounting on reducing the future financial risks of the Jordanian commercial banks by using financial ratios to find a practical method of calculating the hedge with its three categories and to address the future financial risks of commercial banks in Jordan. The researchers used both the descriptive and analytical methods based on the financial statements and reports of the Jordanian commercial banks for the period (2012-2017), in addition of using financial indicators. The study community included the published financial statements of the Jordanian commercial banks before applying hedge accounting and after in accordance with the amendments to IFRS Standard No. 9, as well as the banks listed in the Amman Exchange Market for the period of study. The sample of the study included all Jordanian commercial banks that disclosed the application of hedge accounting in their annual financial statements. The study concluded that there is a strong correlation between cash flow hedges and reducing the financial risks of Jordanian commercial banks after the application of hedge accounting for the period (2012-2017), and that there is a strong correlation between fair value hedges and reducing the financial risks. The fair value hedges have an explanatory capacity to reduce the financial risk by 27.4%. This has been derived from the R2 value. There is a weak correlation between the net investments in foreign currencies and the financial risks. The study recommended the importance of maintaining the use of hedge accounting to achieve fairness and honest representation in the final financial statements to the benefit of internal and external users.
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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 | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".