Are foreign exchange rates only affected by U.S. and domestic news?
Bibliographic record
Abstract
The purpose of this paper is to determine whether foreign exchange rates are affected by macroeconomic announcements from economies other than Unites States and homelands. The motivation is the economical interaction increase in current globalization trend, while most previous literatures focus on the influence of domestic economic information and the international influence of Unites States on the foreign exchange market.\n\nThis paper investigates respectively the responses of seven major currencies (EUR/USD, GBP/USD, USD/JPY, USD/CHF, USD/CAD, AUD/USD and NZD/USD) to macroeconomic announcements (Gross Domestic Product, Consumer Price Index and Unemployment Rate) from eight corresponding economies United States, Euro Zone, United Kingdom, Japan, Switzerland, Canada, Australia and New Zealand. The period covers from 1st January 2011 to 31st December 2015.\n\nEvidences of the responses of exchange rates and trading volumes are provided in this paper. Firstly, most but not all foreign exchange rates are affected by U.S. and domestic macroeconomic announcements. Secondly, the foreign exchange rates are affected also by macroeconomic announcements from economies other than United States and homeland. Thirdly, the trading volumes emerge the peak effects around the release time of certain announcements, but the quantitative analysis cannot provide meaningful evidences.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".