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Record W3092367021 · doi:10.5430/ijfr.v11n5p414

Impact of Institutional Governance Tools on Reducing Agency Costs to Commercial Banks Listed in the Amman Stock Exchange

2020· article· en· W3092367021 on OpenAlexvenueno aff
Omar Fareed Shaqqour

Bibliographic record

VenueInternational Journal of Financial Research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
FundersZarqa University
KeywordsBusinessAudit committeeAccountingStock exchangeAgency costFree cash flowCorporate governanceFinanceDebt ratioAuditOperating expenseAgency (philosophy)DebtShareholder

Abstract

fetched live from OpenAlex

This study aims to identify the Impact of institutional governance tools on reducing agency costs in the banks listed in Amman Stock Exchange (ASE). To this end, the researcher has studied the impact of an institutional governance tools on reducing the agency costs. Which are: board of directors` size, board of directors` independent members ratio, number of audit committee meetings, ratio of debt-financing, market share and bank`s size.The agency costs are measured by three indicators: assets turnover ratio, operating expenses ratio and free cash flow indicator. Study sample comprises all 16 banks listed in the ASE, for which data are available in the ASE during period of the study (2017 – 2019). EXCEL and SPSS are used to identify descriptive characteristics of study and analyze data. Regression analysis method is also used to test the study hypotheses. The study results have concluded that agency costs increase with the increase in the board of directors size, the independent members ratio, number of meetings of audit committees, debt finance ratio and market share ratio. The study has also concluded, as per the operating expenses indicator, that agency costs increase with the increase of debt financing, while they decrease with the increase in the board of directors size. According to the free cash flow indicator, the study results have showed that the agency costs increase with increase in the board of directors size.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmano category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptno category
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designlow
models splitAgreement compares identical category sets and study designs across arms.

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)0.003
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0030.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.127
GPT teacher head0.391
Teacher spread0.264 · 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

Labeled directly by 2 models reading the full record.

The models applied no category: nothing in the taxonomy fit this work.

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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

Citations2
Published2020
Admission routes1
Has abstractyes

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