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Record W2129647502 · doi:10.17722/ijrbt.v2i1.10

Stock Market Relationship in South Asia: An Empirical Analysis

2013· article· en· W2129647502 on OpenAlexvenueno aff
Damber Singh Kharka, M. S. Turan, Kapil Kaushik

Bibliographic record

VenueInternational Journal of Research in Business and Technology · 2013
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsBusinessStock marketEmpirical researchFinancial economicsEconometricsFinancial systemEconomicsGeographyStatisticsMathematics

Abstract

fetched live from OpenAlex

Literature indicates that there are several studies that have focused on stock market relationships and integration but very limited studies are found in this area that have focused for South Asian markets. Few that have studied stock market relationships and market integration in this region have never included Bhutan, probably due to unavailability of data on Bhutanese stock market or because it is too insignificant in terms of regional market. Particularly the relationship between Bhutanese stock market and Indian stock market is expected to be positive and significant given the very close economic ties/dependence with India. Correlation and regression analysis (data sample includes January 2006 to December 2011 for weekly returns) do not show relations between Bhutanese, Indian or any other stock returns except between Indian and Pakistani market returns. Few time varying effects were noticed when correlations were calculated for different set of split data. In general South Asian markets seem quite independent of each other. Indian stock market, which is more proficient in the region, was expected to have some influence but the results do no support this. Based on the Granger causality test, Indian market seems to provide unidirectional effect on most of the neighbouring stock markets.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.166
Threshold uncertainty score0.472

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.086
GPT teacher head0.363
Teacher spread0.277 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2013
Admission routes1
Has abstractyes

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