International correlations across stock markets and industries: trends and patterns 1988–2002
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data from eight major stock markets world-wide and five industries in each market are analysed. The correlations of return indices between countries and industries are studied with the hope of finding answers or confirming previous empirical answers to the following questions and the implications of these findings for investment strategy determined. (1) Do both the country-specific correlations and industry-specific correlations fluctuate significantly over time between 1988 and 2002? (2) Are the country-specific and industry-specific correlations positively related to stock market volatilities? It is concluded that: First, the correlations among national stock markets have been increasing between 1988 and 2002 and the correlations are not constant over the time period of this research. This indicates that the effect of globalization outweighs country-specific factors in determining the co-movements of the markets. Second, the correlations are positively related to volatility in the stock markets in this sample. Correlations rise in periods when conditional volatility of markets is large. Finally, in most cases, correlations between national stock markets are greater than those between the five industries chosen in these markets, indicating that investment diversification across industries provides greater risk reduction benefits than diversification across countries.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it