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Record W2108994216 · doi:10.5539/ijef.v6n6p103

Foreign Institutional Investments and Liquidity of Stock Markets: Evidence from India

2014· article· en· W2108994216 on OpenAlexvenueno aff
Krishna Prasanna, Bharat Bansal

Bibliographic record

VenueInternational Journal of Economics and Finance · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityStock marketMonetary economicsInstitutional investorLiquidity crisisThird marketMarket impactEconomicsVolatility (finance)Market capitalizationBusinessFinancial systemMarket microstructureFinancial economicsOrder (exchange)FinanceCorporate governance

Abstract

fetched live from OpenAlex

Indian economy has experienced rapid economic growth rate and higher foreign institutional investment (FII) inflows over the decade 2001–2010.This paper examines the impact of Foreign Institutional Investments upon Indian stock market liquidity. Foreign institutional investments contributed for the growth of stock market activity in India. FII flows had significant positive impact on Market capitalisation, volume and value traded in the Indian market. However the empirical results indicate that Foreign Institutional Trading significantly influences market liquidity in a negative direction. Foreign Investments were found to granger cause market liquidity. A 1% change in the gross purchases in the current week will result in 0.10% decrease in the liquidity of the following week, whereas a 1% change in sales would result in 0.12% similar change. Results support the argument that across the emerging markets FIIs result in excess market volatility and lower liquidity.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.021
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.228
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), 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

Citations10
Published2014
Admission routes1
Has abstractyes

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