The effect of macroeconomic variables on Sukuk Issuance in Malaysia
Bibliographic record
Abstract
The Islamic capital market, particularly sukuk has a prime role to the development of Islamic finance into a mainstream force for global financial industry. This study aims to investigate the effect of macroeconomic variables on Sukuk issuance in Malaysia from the period of first quarter of 2005 till the fourth quarter of 2019. The main objective of this study is to analyze the relationship of Sukuk issuance in Malaysia with selected macroeconomic variables; Bursa Malaysia composite index (BMCI), exchange rate (EXR), GDP, Inflation (INF) and interest rate (INT). This study employs time series analysis techniques such as Vector Error Correction Model (VECM), Johansen Co-Integration Analysis and Forecast Error Variance Decomposition (FEVD) Analysis to identify the relationship among variables in the short run and the long run. Based on our analysis, BMCI is the significant determinant factor of sukuk issuance in Malaysia in the long run while for the short run, the BMCI, EXR, and GDP, are significant. EXR is the most important contributor in short-term for sukuk issuance, followed by GDP and Bursa Malaysia (BMCI). Investors, regulators, and market participants may find this finding beneficial to recognise the crucial roles played by macroeconomic variables used in this study in determining the effect of macroeconomic variables on Sukuk issuance in Malaysia.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".