Stock Split Rule Changes and Stock Liquidity: Evidence from Bursa Malaysia
Bibliographic record
Abstract
We test the impact of stock split rule changes on liquidity behavior in Bursa Malaysia during 2004–2020. Using event study methodology, this study examines stock liquidity on and around stock split days through three subperiods of study, including the first (2004–2006), second (2007–2009), and third (2010–2020) period. We find that liquidity improvement is short-lived in the first and second periods, while it is a long-lived phenomenon in the third period. Firms in the first and second period experienced liquidity improvement only on the split announcement day, while it lasts up to a year after the Ex-date for firms in the third period. Our findings also show a liquidity improvement after the Ex-date only in the third period for the groups of firms categorized based on the liquidity, split factor, and other simultaneous announcements. The findings suggest a positive effect of stock split rule changes implemented by the Securities Commission.
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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 | 0.002 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".