Identifying Equity Market Integration : A Study of NIFTY-50 and the World’s Top Six Indices
Bibliographic record
Abstract
Purpose : The decoupling theory proposed the benefits of internationally diversified portfolios to mitigate investment risk for global investors. The global investment risk was observed to be inversely related to the degree of co-movement of the stock indices. Therefore, the current study attempted to seek the answer to whether the stock indices of the world’s top seven countries were independent or exhibited a lead–lag relationship. Methodology : The study employed the Johannesburg co-integration and Granger-causality to investigate the relationship for the monthly returns data starting from 1st January 1995 and ending on 31st March 2025. Findings : The study found that the Indian index was co-integrated in the long run with American, Chinese, British, Japanese, and Canadian stock indices ; whereas, in the short-run, it was significantly influenced by the US markets and followed the returns patterns. Theoretical, Managerial, and Practical Implications : The performance of the US market was taken as a proxy for the Indian market in the short run, and the performance of the Indian market affected the markets in the UK, Canada, and French stock indices. Originality: The current study reported the interlinkages of the Indian stock market with the top six developing and developed economies of the world.
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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 | 0.004 |
| 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 | 0.001 |
| 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".