An Analysis of the Relationship between Nominal Exchange Rates and Stock Prices
Bibliographic record
Abstract
In this dissertation, the relation between nominal exchange rates and stock prices is examined in the nine markets of Australia, Canada, Hong Kong (HK), Japan, United Kingdom (UK), Sweden, India, the Philippines and Thailand. Monthly closing observations from July 1997 to July 2015 for Thailand and from June 1995 to June 2015 for the remaining markets are used to study the interaction between the two variables. First, the results of unit root tests indicate that two variables are not stationary and integrated of order one, that is I(1). Next, no evidence of a long-term cointegration relation between the two series is discovered when Johansen’s cointegration test is employed. Then, Granger causality test shows a unidirectional Granger causality running from stock prices to exchange rates for Canada, causality in the opposite direction for Japan, UK and the Philippines, bi-direction causality between the two variables for Thailand and no any causal relationship for the remaining markets. Finally, analysis of impulse response functions reveals that data from are in agreement with the traditional approach. Increasing differenced exchange rate has a negative effect on stock return for most of markets over the sample period, and vice versa. The results of variance decompositions indicate that stock price is driven to a lesser extent by change in exchange rate for all markets while exchange rate is driven to some extent by a shock to stock return for all markets except for Sweden, HK and UK. Moreover, these findings have implications for policy makers, market researchers and global investors.
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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 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".