Bibliographic record
Abstract
Recently, the number of financial derivatives have been created to attract the business and public to invest. Among them, the Exchanged-Traded Fund (ETF) is one of the most stable financial derivatives and profitable. In 2005, the first currency ETF (euro IMF) has been established, and it’s performance was affirmed by the international financial markets. The major currencies were used to issue a series of currency ETFs. This study takes the major currencies –such as Australia, United Kingdom, Canada, Switzerland, Japan, USA, New Zealand and other countries as study subjects. The sample data of daily return is from the beginning of January 2007 to the end of December 2015 as the objects. This paper uses the ARIMAX-GARCH model to analze, test and forecast the dynamic relationship of currency ETFs returns and macroeconomic factors. The results are as follows: 1.When volatility index (VIX) rises, the current returns have significantly mixed effect. This shows that the investors may change investment strategies which affected by the previous returns, CRB, stock index and short term interest rate. Therefore, the current returns interacted with investors’s investment strategies may ultimately affect the next-term or few-terms of return. 2.When the current retrrns have upward trend and the previous returns have a negative impact , while CRB, and short-term interest rate showed positice correlation. 3.The results of forecast for the rerurns, reveal that Canada currency ETF's MAE and RMSE have the minimum value, which means the best predicting performance for the returns and most suitable for investment. The above results indicate the economics factors such as short-term interest rate, stock index, CRB, and VIX having dynamic effects with the currency ETFs. Institution and the investors can take indicators as references, to choose the favor currency ETF based on their investing preference in order to effectively control the returns and avoid the investment risk.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".