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Record W4205560744 · doi:10.5539/ijef.v14n3p1

Correlates of Stock Market Development and Economic Growth: A Confirmatory Study from Ghana

2022· article· en· W4205560744 on OpenAlexvenueno aff
Edward Alabie Borteye, Williams Kwasi Peprah

Bibliographic record

VenueInternational Journal of Economics and Finance · 2022
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsnot available
Fundersnot available
KeywordsMarket capitalizationMarket liquidityStock marketGross domestic productEconomicsBivariate analysisMarket depthStock exchangeMonetary economicsFinancial economicsBusinessFinanceMacroeconomicsStatistics

Abstract

fetched live from OpenAlex

The study confirms the debate on whether stock market development correlates to economic growth. The dimensions used for the stock market development consisted of market liquidity, size, and capitalization. Economic growth was represented by the real gross domestic product (GDP) growth rate. Based on secondary data obtained from the Ghana Stock Exchange (GSE) and Ghana Statistical Service from 2014 to 2018, a correlational research design was adopted to analyze the data with SPSS 20v by using bivariate and regression. The study found that there is a high positive relationship between market liquidity and economic growth, a moderate negative relationship between market size and economic growth, and a moderate positive relationship between market capitalization and economic growth. Also, the stock market development of market liquidity, size, and capitalization predict 95.7 percent of economic growth. The study summarized that there is a high positive association between stock market development and economic growth as a confirmatory revelation, but all the relationship results were not statistically significant. The result points to the casualty of the relationship between stock market development and economic growth. The study recommends that more firms must be encouraged to be listed on GSE to enhance economic growth in Ghana.

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)0.001
metaresearch head score (Gemma)0.000
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 score0.107
Threshold uncertainty score0.732

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.023
GPT teacher head0.213
Teacher spread0.190 · 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

Citations11
Published2022
Admission routes1
Has abstractyes

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