Trade and Investment Linkages and Stock Market Long-Run Relationship
Bibliographic record
Abstract
This paper aims to examine whether the intensity of trade and investment linkages among the countries matter for their stock market long-run relationship. To achieve this, we classify Australia's bilateral trade and investment partners into major, medium and minor. Empirical findings of an asymmetric generalised dynamic conditional correlation generalised autoregressive conditional heteroskedasticity model show that correlations are time varying and increased significantly during the global financial crisis (GFC). Results of multivariate cointegration test confirm the long-run equilibrium relationship between the stock markets of Australia and its major partners in the pre-GFC and during GFC. Based on the full-sample results, it indicates that the GFC has segmented the stock markets from the long-run equilibrium relationship. Granger non-causality test results on full sample show that Australian stock market causes only the New Zealand market while the USA, the UK, Germany, Canada, Switzerland and Italy drives the Australian market. Our results therefore suggest that the intensity of bilateral trade and investment linkages among the countries matter for their stock markets' long-term relationship.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".