Determinants of capital structure decisions among publicly listed Islamic banks
Bibliographic record
Abstract
This research aims to examine bank specific, market and regulatory determinants of leverage and capital structure based on a panel data of publicly listed Islamic banks in 12 countries over the period 2008-2017. Apart from testing standard corporate finance parameters using both OLS and M-Estimators, this study adds several idiosyncratic and regulatory environment related determinants of leverage unique to Islamic banks. The significance of potential determinants is tested for market and book leverage as well as newly introduced 'Islamic banking leverage'. Overall, the results show that Islamic banks with higher growth opportunities, tangibility, low profitability and low risk are likely to have a high leverage. Similarly, the findings suggest important role played by debt market conditions, share of investment accounts and regulatory environment in such decisions, providing an evidence of the significance of trade-off and pecking order theory in capital structure in Islamic banks. The results are more robust for market and Islamic banking leverage, rather than book leverage. The findings offer insights to regulators, standard setters and especially Islamic banks regarding parameters to strengthen their capital, enhance resilience and thus contribute to the stability of relevant financial. This paper is among the few extant studies that focus on listed Islamic banks and tests determinants based on stock market data.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".