EXPLORING THE STOCK MARKET CORRELATION AMONG INDIA AND FIVE AMERICAN ECONOMIES DURING THE GLOBAL FINANCIAL CRISIS PERIOD AND POST-FINANCIAL CRISIS PERIOD
Bibliographic record
Abstract
In an increasingly interconnected financial landscape, understanding the co-movements between emerging and developed stock indices is crucial for managing portfolio risks and secure investment. This paper provides a thorough correlation analysis among the calculated returns from indices of India (S&P BSE SENSEX) and five major American countries—US (S&P 500), Canada (S&P/TSX Composite), Brazil (IBOVESPA), Mexico (IPC MEXICO), and Argentina (MERVAL) during three key intervals: the pre-crisis phase (June 3, 2003 – August 2, 2007), the global financial crisis phase (August 7, 2007 – April 16, 2009), and the post-crisis phase (April 20, 2009 – December 30, 2019). Employing calculated returns from daily adjusted stock index closing values and analyzing descriptive statistics alongside correlation metrics, this paper assesses stock-market integration levels. Normality diagnostic test was performed to determine the most suitable correlation approach. Normality test results inferred the application of the non-parametric Spearman Rank Correlation method. The correlation matrix designates persistently weak limited correlation among S&P BSE SENSEX (India) and the five chosen indices from the American region, over the three intervals, suggesting low financial integration and highlighting opportunities for investment diversification in these markets. These findings serve as a basis for crafting resilient investment strategies amidst global financial fluctuations
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| 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".