Credit Risk, Islamic Contracts and Ownership Status: Evidence From Malaysian Islamic Banks
Bibliographic record
Abstract
The paper attempts to model the key drivers of credit risk for Islamic banks in Malaysia. This paper is motivated to introduce Islamic financing types (IFT) and banks ownership status (STATUS) as additional factors in investigating the key drivers. This study also investigates the level of credit risk between the crisis and non-crisis period. This study employs a panel data analysis method using generalized least squares (GLS) regression for random effect model. The dependent variable is credit risk which assumed to be a function of bank-specific variables and other potential variables that are ownership status, Islamic financing types and financial crisis. The sample of this study comprised of 160 observations for 15 full-fledged Islamic banks in Malaysia, covering the period of 2000 to 2016. The finding suggests that financing expansion, financing and capital buffer are amongst important drivers that significantly influence the level of credit risk of Malaysian Islamic banks. The estimation results of this study also suggest that any Islamic bank that offers equity-based financing (EBF) has significantly higher credit risk.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".