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Identifying Equity Market Integration : A Study of NIFTY-50 and the World’s Top Six Indices

2025· article· W7154059589 on OpenAlexaboutno aff
Sandeep Vodwal, Priya Sawaliya

Bibliographic record

VenueIndian Journal of Research in Capital Markets · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsStock marketStock market indexEquity (law)Stock (firearms)Financial marketIndex (typography)Market riskEmerging marketsInvestment strategy

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.038
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0380.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0050.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.079
GPT teacher head0.379
Teacher spread0.301 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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