Bibliographic record
Abstract
The paper investigates the degree of sensitivity of international equity market returns, using MSCI indices as widely tracked global equity benchmarks of stock exchanges, traded throughout the world. In particular using a time-varying methodology, the research examines whether the returns of developed international stock exchange markets (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, the UK and the USA) are each associated with movements of MSCI World Index in order to see the level of bilateral influence between equity markets, affected by globalization processes. In this paper the author uses, first, the correlation analysis applied to stock exchange markets in order to highlight the dynamic of financial market globalization. Second, the author uses the estimation model of security markets convergence based on the bilateral differences of their returns, suggested and developed by Frazer (1994; 2008). The model contains the Kalman filter and, as a consequence, this model accommodates fundamental shifts in the bilateral relationships. However, there is no study where the Kalman filter methodology is used to examine time-varying convergence of international stock exchange market returns relative to MSCI World Index. The results of research show that all analyzed developed equity markets are moving toward greater integration in terms of increasing correlation. Moreover, all analyzed markets are interdependent and affected by globalization processes, showing strong and lasting relationships between each other. However, bilateral convergence of international equity markets is not an equal process, where different cluster of markets are engaged in different manners. Some of equity markets have less influence on other market’ returns in global context, suggesting that bilateral convergence is not a homogeneous process. Furthermore, the findings suggest including industry portfolio diversification approach instead of geographical diversification in asset-allocation models.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".