How Much Globalization Is There in the World Stock Markets and Where Is It?
Bibliographic record
Abstract
Globalization, as the process of integration of national economies into the international economy through trade, foreign direct investment, capital flows, migration and the spread of technology, has been analyzed by academic literature in different manners.Anyway a comprehensive analysis in a worldwide perspective that compares all the main stock markets' performances in a long term period misses.In this paper, the authors try to fill this gap by a correlation analysis applied to stock exchange market indexes.This methodology is implemented in order to highlight the dynamic trend of financial market globalization.The paper investigates the degree of association of weekly returns for 53 international stock exchanges from 1995 to 2010 in a year-by-year approach, trying to evaluate how the average correlation through national stock indexes changed by the time.Moreover, an analysis of single geographical areas (North America and Canada, Latin America, Asia and Oceania, Northern Europe, Eastern Europe and Western Europe) has been done in order to test the hypothesis that globalization follows a homogenous (or heterogeneous) path.Results suggest an upward globalization trend that is developing at an increasing growth rate.Furthermore, an analysis of single geographical areas supports the hypothesis that globalization is a heterogeneous phenomena where different cluster of countries are engaged in different manners.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".