Time-varying Stock Market Integration and Diversification Opportunities within Developed Markets Using Aggregated Data Approach
Bibliographic record
Abstract
This study examines the time-varying feature of Developed stock markets to identify diversification opportunities. For this purpose, we sample 21 developed countries ranging from 2000-2018 from the Pacific Region, Northern Europe, Western Europe, Southern Europe, and G7, with each region consisting of a panel with one home country and other as remaining countries portfolio. We applied Panel co-integration and VECM to test the stock market integration and diversification opportunities in short and long run. Our results indicate few short and long-run diversification opportunities for international investors in the post-crisis period that are more relevant. Canada, Japan, and Italy have long-run opportunities for diversification in the G7, and only Japan has short-run opportunities for diversification. Hong Kong and Japan have short-and long-run opportunities for diversification in the Pacific region. In the Northern Europe region, we have only the short-run diversification option of the UK and Norway. In the Western European Region, Australia and Switzerland have long-term diversification. There are no long and short-run diversification opportunities in the Southern European Region in the post-crisis period.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".