Cross-Country Stock Market Integration and Portfolio Diversification Opportunities Evidence from Developed, Emerging and Frontier Countries
Bibliographic record
Abstract
This study examines the stock market integration in cross-regional countries of developed, emerging, and frontier markets based on low correlation. The objective of the study is to identify the diversification opportunities and link between correlation and integration among country-level stocks. For this purpose, we select 62 countries from all three classifications of developed, emerging, and Frontier Markets. We constructed portfolios by selecting least 5 correlated countries denoted with Pjt in which each country has a correlation of less than .10 with base country Pit. Thirty-two countries fulfill the criteria of low correlation; 7, 13 and 12 from developed, emerging and frontier markets, respectively. Panel co-integration and VECM are applied to test the stock market integration and long & short-run linkages between country-level portfolios designed based on low correlation criteria. After conditioning for oil price movements, S&P 500 and exchange rate, we found Canada, France and Germany from developed category; Chile, Colombia, Greece, South Korea, Malaysia, Pakistan and Philippine from emerging category; and Bahrain, Jordan, Kuwait, Morocco, and Sri Lanka from frontier category have long-run diversification opportunities. Countries including; Canada and Italy from developed category; Argentina, Chile, China, Colombia, India, Indonesia, South Korea, Mexico and the Philippine from emerging category; and Bahrain, Kuwait, Morocco, Nigeria, and Tunisia from emerging category have short-run diversification opportunities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".