Bibliographic record
Abstract
Our paper critically reviews the different factors that motivate a corporation to issue a Sukuk versus a Conventional Bond. Currently, Sukuk papers are growing, yet the studies are limited compared to Conventional Bond research. Our paper analyzes firm performance and characteristics to demonstrate how this affects choosing between the two securities. We examine these in a time series with the 2008 financial crisis intervention to see how this may have affected the issuance choice through logistic regression. Our study examines 628 Conventional Bonds and 227 Sukuk issuers globally across 12 countries from 2005 – 2017. We find in our research that the performance of companies issuing Sukuks resembles Conventional Bond issuers in financial performance. We confirm that larger companies will enter the Sukuk market as an alternative to the Conventional Bond market when there is a higher demand for capital. We also find that firms with higher financial performance may enter the Sukuk market as a premium where it may not be accessible in the Conventional Bond Market.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".