Asymmetric Effects of Exchange Rates on Stock Prices in G7 Countries
Bibliographic record
Abstract
This paper examines whether there are asymmetric effects of exchange rates on stock prices in G7 countries. According to Fratzscher (2008), the G7 has been overall effective in moving the US dollar, yen and euro in the intended direction at horizons of up to three months after G7 meetings. Therefore, it is important to find out the impacts of G7 exchange rate adjustments on the stock markets. These effects can become worldwide as G7 stock markets have been the trading platform for international market capitalization for about last thirty years. A vast body of research documented that a country’s currency can have a large effect on stock market movement, however the empirical findings are mixed. This study contended that exchange rates can affect stock prices asymmetrically. This study systematically discussed four views on how exchange rates can affect stock prices asymmetrically. A dataset consists of 227 monthly data from 31-12-1997 to 31-10-2016 for G7 countries, namely Canada, France, Germany, Italy, Japan, the United Kingdom and the United States are collected from Thomson Reuters DataStream and Bank for International Settlements (BIS). A nonlinear ARDL model is employed to analyse the asymmetric effects of exchange rates on stock prices. The findings showed that the exchange rate changes in all G7 countries have short-run asymmetric effects on stock prices. However, the results do not hold into the long-run, except for Germany. This paper suggests that policymakers should have a different reaction in policy decision between the depreciation and appreciation of exchange rates. Investors can make profit from stock market by buying or selling stocks according to the predicted response of stock market to exchange rate changes. Take into consideration of the importance of G7 currencies and stock markets, this paper examined and compared the asymmetric effects of exchange rates on stock prices in G7 countries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".