MétaCan
Menu
Back to cohort
Record W4245981371 · doi:10.31227/osf.io/253ck

Effect of Composite Stock Price Index, Exchange Rate and Interest Rate Share Price of Mining Companies Listed in Indonesia Stock Exchange

2017· preprint· en· W4245981371 on OpenAlexaff
Imaduddin Murdifin, Suriyanti Mangkona

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsStock exchangeStock (firearms)Nonprobability samplingExchange rateInterest rateComposite indexCost priceBusinessStock market indexEconometricsPanel dataEconomicsStock priceMonetary economicsFinanceStock marketPopulation

Abstract

fetched live from OpenAlex

This study aimed to examine the effect of Composite Stock Price Index (Composite Stock Price Index (CSPI)), the exchange rate, and interest rates on stock prices of mining companies listed in Indonesia stock Exchange. This research is associative with quantitative approach. Data were analyzed using panel data regression. The data used is secondary data such as financial data, and the percentage of monthly interest rates over the last three years. The collection of data taken with documentation techniques derived from published reports of Bank Indonesia and the Indonesia Stock Exchange. Sampling was done by purposive sampling with the number nine companies. The results showed that the CSPI and interest rates but not significant positive effect on stock prices. The rupiah exchange rate and significant negative effect on stock prices. Simultaneously the composite stock price index, the rupiah exchange rate, and interest rates have a significant effect on stock prices of mining companies listed on the Indonesia Stock Exchange

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)0.003
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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.256
Teacher spread0.218 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

Explore more

Same topicFinancial Analysis and Corporate GovernanceFrench-language works237,207