A Drivers of Islamic Bond Liquidity in Malaysia: Latent Liquidity Approach
Bibliographic record
Abstract
A steady liquidity level is an importance characteristic of a financial market, especially after the 2008 financial crisis. The Islamic financial market was virtually isolated from the crisis. It is interesting to explore the underlying determinants that stabilise a market’s liquidity level. This paper studies the determinants of a Sukuk’s liquidity level in the Malaysian bond market using a new liquidity measure known as latent liquidity. The measure does not require transaction data, which makes it applicable to an illiquid market such as the Malaysian bond market. Utilising data from the Malaysian bond market, the paper involves two steps of data analyses, namely an insight into the trend and the liquidity level of the Sukuk market. It then continues to investigate the driver of Sukuk’s liquidity using the latent liquidity as a proxy against five Sukuk characteristics in a random effect regression model. Four variables issuance amount, maturity, coupon rate, and age are found to be significant drivers of Sukuk’s liquidity level. Conclusions drawn from the regression results indicate Sukuk’s investors’ preference in matching long term Sukuk with their long term liabilities, in addition to their fondness for keeping their Sukuk to amortise the return.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".