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Record W2782453189 · doi:10.5539/ijef.v10n2p28

Impact of Macroeconomic Variables on Karachi Stock Market Returns

2018· article· en· W2782453189 on OpenAlexvenueno aff
Javed Pervaiz, Junaid Masih, Teng Jian-zhou

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMonetary Policy and Economic Impact
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsEconometricsStock exchangeStock marketInterest rateRegression analysisInflation (cosmology)Exchange rateStock (firearms)VariablesStock market indexIndex (typography)Financial economicsMonetary economicsStatisticsMathematicsFinanceGeography

Abstract

fetched live from OpenAlex

The study investigated The study examines the impact of selected macroeconomic variables (inflation, exchange rate, interest rate) on Karachi stock market returns. Mainly secondary data used in the research process. The study consists of data for the period of 10 years and 5 months starting from January 2007 till May 2017. For this purpose, monthly data of KSE-100 index has been observed for the period January 2007 to May 2017. The market returns have been calculated through the opening and closing index value of each month. The inflation, interest rate, and exchange rate has been taken as independent variables. Hypotheses have been tested to find out whether there exists a significant relationship between the Stock market return and macroeconomic variables or not. To test this hypothesis, Regression analysis used and results are calculated through Stata software.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.038
GPT teacher head0.258
Teacher spread0.220 · 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

Citations14
Published2018
Admission routes1
Has abstractyes

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