A Descriptive Analysis of IPOs Post Listing Liquidity in Malaysia
Bibliographic record
Abstract
This study provides an overview of the issue of stock market liquidity among initial public offering (IPOs) by investigating the trend liquidity of IPOs for the period beginning 2002 through to 2017. Based on the Thompson Reuters DataStream database which provides data of Amihud's illiquidity measure (ILLIQ) for 304 IPOs Malaysian companies, detailed analysis shows that the corporate governance mechanisms plays an important role in increasing the transparency and disclosure of financial information, then reduces information asymmetries among market participants such as managers, large shareholders and investors, this leads increase stock market liquidity. The results of the study will be useful for firms through enables firms to know what factors are influence their stock market liquidity. Further, this study could have implications on regulating bodies such as Bursa Malaysia to help design regulations that could enhance stock market liquidity. Moreover, this study would provide implications for the investors and traders in terms of helping them on how to formulate their trading strategy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".