Impact of globalization on stock market synchronization: some empirical evidence
Bibliographic record
Abstract
Purpose The main purpose of this paper is to examine the impact of globalization on the synchronization of international financial markets. Monthly data from January 1990 to July 2005 for ten major stock markets, namely, Australia, Canada, France, Germany, Hong Kong, Japan, Singapore, the UK, and the USA are used. Design/methodology/approach A battery of test procedures to determine the stationarity conditions of the data set is employed. The Johansen and Jsuelius method is used to test for the existence of long run equilibrium relationship among various markets. If a given market is found to be integrated of order one, it would imply that the market is weak-form efficient. Similarly, if markets are collectively found to be cointegrated, this would suggest that globalization had a significant impact on international financial integration. Findings Results indicate that each market is weak-form efficient. As such, price movement in every market is random and cannot be predicted. The tests produced a large number of cointegrating vectors which implied a strong long run relationship between all markets. Thus, globalization seems to have greatly impacted international financial integration. Originality/value The findings of this paper are expected to provide valuable insights into international portfolio diversification as an investment strategy.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".