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Record W2802588362 · doi:10.5539/ijef.v10n5p221

Impact of Systemic Risks on Islamic Banks Performance

2018· article· en· W2802588362 on OpenAlexvenueno aff
Bader Mustafa Al-Sharif

Bibliographic record

VenueInternational Journal of Economics and Finance · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsIslamSystemic riskOrder (exchange)BusinessMarket liquidityInvestment (military)Credit riskLiquidity riskCapital adequacy ratioFinanceFinancial systemActuarial scienceAccountingEconomicsFinancial crisis

Abstract

fetched live from OpenAlex

The current study aims at identifying impact of systemic risks on Islamic banks performance: evidence from Jordan as measured by return on assets. The financial reports issued by Islamic Bank of Jordan and Islamic International Arab Bank for the periods (2007-2016) were referred to. The analytical and descriptive approach were applied in order to achieve the objectives and the results of the current study. (E-views) software was applied in order to examine the hypotheses of the study and to answer its questions through Simple &Multiple Linear Regression analysis.The most significant findings of the study was the presence of statistically significant effect of systemic banking risks (capital risks, liquidity risks, credit risks, operational risks) on the performance of Jordanian Islamic banks.The current study recommended that Islamic banks should diversify in financing and investment methods to reduce systemic banking risks and to establish specialized risk departments and activate their role to reduce the negative effects of systemic risks on the performance of Islamic banks in order to continue in the market.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.002
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
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 head0.019
GPT teacher head0.252
Teacher spread0.233 · 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
Published2018
Admission routes1
Has abstractyes

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