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Record W3004069737 · doi:10.1108/jiabr-09-2017-0133

Credit risk in Islamic banking: evidence from the GCC

2020· article· en· W3004069737 on OpenAlexaff
Trevor W. Chamberlain, Sutan Emir Hidayat, Abdul Rahman Khokhar

Bibliographic record

VenueJournal of Islamic accounting and business research · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsSaint Mary's UniversityMcMaster University
Fundersnot available
KeywordsInefficiencyCredit riskOrdinary least squaresLogistic regressionMarket liquidityBusinessEconomicsUnivariateActuarial scienceCapitalizationIslamEconometricsPanel dataFinancial servicesAccountingMultivariate statisticsStatisticsFinanceMathematics

Abstract

fetched live from OpenAlex

Purpose This study aims to investigate the differences in the credit profiles of Islamic and conventional banks in the Gulf Cooperation Council (GCC) region and attempts to identify the factors responsible for those differences. Design/methodology/approach Financial data sourced from the Bankscope database for a sample of 25 Islamic and 56 conventional banks headquartered in the GCC region between 1987 and 2014 are used. The credit risk of Islamic versus conventional banks is compared using a variety of univariate (mean difference test and correlation analysis) and multivariate tests (pooled ordinary least squares (OLS) regressions with robust standard errors and year fixed effects, regressions with interaction variables and logistic regressions). Findings Pooled OLS regressions find that Islamic banks have lower credit risk than conventional banks. Robustness checks using logistic functions and interaction variables confirm this result. Using multiple econometric specifications, we also find that higher capitalization, greater liquidity and cost inefficiency contribute to the lower risk profile of Islamic banks. Research limitations/implications The study is unable to disaggregate data for banks offering both Islamic and conventional banking services and hence does not include conventional banks with Islamic windows. In addition, there are differences across countries even within the GCC region as to what is considered Sharia’h -compliant and what is not. Practical implications The results are of potential interest to not only researchers, but also market participants, regulators and legislators. The methods used in this study could be extended to other two-tiered banking systems and, in the case of Islamic and conventional banking, to other markets. Originality/value The authors use a unique sample of banks headquartered in the GCC countries, whose banking markets are similar, if not homogeneous, thus excluding operations of multinational banks. By focusing on the Gulf region, differences in the credit profiles of Islamic and conventional banks can be examined without the confounding effects of unobserved factors like culture, accounting regime or regulatory environment.

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)0.004
metaresearch head score (Gemma)0.006
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 score0.117
Threshold uncertainty score0.824

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
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 head0.059
GPT teacher head0.302
Teacher spread0.243 · 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

Citations49
Published2020
Admission routes1
Has abstractyes

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