Theoretical Framework of Foreign Exchange Exposure, Competition and the Market Value of Domestic Corporations
Bibliographic record
Abstract
The purpose of this study is to examine the foreign exchange rate exposure of domestic corporations in the United Arab Emirates (UAE) and the implications of that exposure for the market value of those corporations, considering the effect of competition as a determinant of exchange rate exposure. The justification for this study is that the UAE has an open economy with a high per capita income and a sizable annual trade surplus. In addition, the World Economic Forum issued its Global Competitiveness report for the year 2010-2011 in which the UAE was the only Arab country that was included in the elite club of countries that have shown an increment in endorsing new and improved methods for developing their economies. However, because of the indirect nature of foreign exchange rate exposure for local or domestic firms, the managers of these firms are unwilling to engage in hedging activities that may mitigate exchange rate exposure. A change in prices, the cost of final goods, the cost of raw material, labor costs or the costs of input or output and other substitute goods due to fluctuating exchange rates may have an adverse effect on the competitive position of a local or domestic firm with no international and foreign activities. The outcomes of this study will determine whether the domestic firms are exposure to the fluctuation of foreign exchange rates.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".