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Record W2745632600 · doi:10.5539/ijef.v9n9p175

The Threats of Unsystematic Risks in Jordanian Commercial Banking Sector

2017· article· en· W2745632600 on OpenAlexvenueno aff
Ghaith N. Al-Εitan, Ismail Y. Yamin

Bibliographic record

VenueInternational Journal of Economics and Finance · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHausman testDividendLoanCapital adequacy ratioPanel dataBusinessMarket liquidityCredit riskRetained earningsSystematic riskActuarial scienceEarningsCapital structureFixed effects modelEconomicsFinanceEconometrics

Abstract

fetched live from OpenAlex

The objective of this study is to empirically examine the effect of unsystematic risks on the performance of commercial banks in Jordan, using panel data for the period of 10 years (2005-2015). The study uses earning per share and dividends as dependent variables to represent Banks’ performance. The empirical analysis based on the fixed effect model selected on the basis of Hausman test. The results indicate that the impact of Non-performing loans on commercial banks’ dividends is positive and significant while the impact of capital adequacy is negative and statistically significant on dividends. The results indicate that the credit risk, liquidity risk, non-performing loan and capital adequacy have significant effect on earnings per share and the effects are negative as expected. Based on the study it is recommended that the Jordanian commercial banks needs enhance the process of credit risk management to determine loan defaulter and impose the appropriate legal action against them.

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 score0.006
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 studies0.0010.001
Scholarly communication0.0030.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.040
GPT teacher head0.269
Teacher spread0.229 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

Explore more

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