The impact of some economic variables on exchange rates traded in the forex markets / applied study
Bibliographic record
Abstract
The study sought to study the relationship between some of the economic variables such as: unemployment rate, inflation rate, crude oil inventories, and the exchange rates of some currencies traded in the forex markets i.e. the Canadian dollar, the sterling pound, the euro. To show the effect of these variables on exchange rates in order to provide Forex traders with information that enables them to know the mechanism, in which these markets operate, and to avoid the risks that lie on them. The problem of the research was based on the following question: What are the economic variables affecting the prices of currencies traded in the Forex markets, and how much impact and how can be diagnosed? The empirical testing directed to examine the relationship between the study variables by the correlation coefficient, and then the multiple regression was used to identify the effect of the selected economic variables on the exchange rates of the currencies examined. The research reached to some conclusions, the most important of which is that the economic variable, which has a negative effect on the base currency, will be positive at the same time on another currency in the pair and vice versa as well. The study also recommends the need to identify the economic variables that affect the course of Forex before the start of the trading process.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".