A Comparative Analysis of the S&P Bse Sensex and Global Stock Indices: Implications for Portfolio Diversification
Bibliographic record
Abstract
In an era of increasing financial globalization, the degree of co-movement between international stock markets has become a critical factor for portfolio managers and individual investors. This study investigates the correlation between India’s S&P BSE SENSEX and five prominent global indices: NASDAQ (USA), Karachi Stock Exchange (KSE - Pakistan), Philippine Stock Exchange Index (PSEI), Toronto Stock Exchange (TSX - Canada), and the MSC Thailand Index. Utilizing historical daily closing prices from January to March 2024, the research employs Pearson’s correlation coefficient to determine the strength and direction of these relationships. The findings reveal a moderate positive correlation (0.493) with the NASDAQ, suggesting that while Indian markets are influenced by global tech trends, they still offer significant diversification benefits. Conversely, relationships with other regional indices like MSC Thailand were found to be weaker, highlighting the importance of geographical diversification. The paper concludes with strategic recommendations for risk management in volatile global environments.
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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 | 0.002 | 0.003 |
| 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".