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Record W4381950409 · doi:10.35137/kijms.v3i1.966

The Influence of ROA and CAR on Stovk Price with Interest Rates As Intervening Variables (Case Study of Bank Rakyat Indonesia Tbk Company) (PERIOD 2010 -2020)

2023· article· en· W4381950409 on OpenAlexaboutno aff
Muhammad Abi Daud, Suharto Suharto, Iwan Kurniawan Subagja

Bibliographic record

VenueKRISNADWIPAYANA INTERNATIONAL JOURNAL OF MANAGEMENT STUDIES · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEconometricsInterest rateStock (firearms)Regression analysisLinear regressionVariablesEconomicsPath analysis (statistics)Quarter (Canadian coin)Return on assetsStock exchangeStatisticsBusinessMathematicsMonetary economicsFinanceEngineeringGeography

Abstract

fetched live from OpenAlex

<p class="Style1">This research was conducted with the aim of empirically examining the effect of Return on Assets (ROA), and Capital Adequacy Ratio (CAR), partially on stock prices with the intervening variable interest rates in the banking company (Bank Rayat Indonesia Tbk). In addition, this study empirically examines the effect of ROA, CAR, and interest rates simultaneously on the stock price of Bank Rakyat Indonesia Tbk. In this study the authors used quantitative research with an associative approach. Types of secondary data from Quarter I 2010- Quarter IV 2020 data taken from Bank BRI's website on the IDX. Data analysis used classical assumption test, simple linear regression analysis, multiple linear regression analysis, hypothesis testing and path analysis. For the coefficient of determination (R2), where it is found that the greatest direct contribution is the interest rate variable to the stock price of 50.1%, while the indirect contribution is ROA and CAR to stock prices through interest rates of 0.513 or 51.3%.</p>

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 score0.136
Threshold uncertainty score0.620

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.028
GPT teacher head0.271
Teacher spread0.244 · 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
Published2023
Admission routes1
Has abstractyes

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Same venueKRISNADWIPAYANA INTERNATIONAL JOURNAL OF MANAGEMENT STUDIESSame topicFinancial Analysis and Corporate GovernanceFrench-language works237,207