Examining the Effect of Stock liquidity on the Relationship between Stock Split and Stock Market Performance
Bibliographic record
Abstract
The effect of stock liquidity on the relation between stock split and stock market performance is puzzling. This paper examines the factors that affect the relationship between stock split and stock market performance. The data are gathered from the Egyptian Stock Exchange listed companies, based on their market capitalization from all sectors in Egypt during 2010 to 2020. This event study employs multiple regression analysis. Liquidity is measured by volume and number of transactions. Announcement date is considered for stock split as independent variable. Also, firm size, split factor, Industry type as control variables have been tested in the model. The research is event study; The time window is based on twenty days and five days. Results indicate that liquidity as moderator is positively affect the relationship between stock split and stock market performance for five- and twenty-days’ time windows. A robustness check has been performed for every regression model. Showing significant effect of liquidity as moderator, measured by volume of transactions, on the relationship for the sample period between 2010 and 2019 for twenty days’ time window. Results support the easiness and enhancing the process of stock split. For example, Financial Regulator Authority could waive its approval for stock split to companies’ general assembly as the market would correct itself for disturbance in liquidity after stock split announcement. Also, Minimizing the number of companies that don't execute stock split affect the investors’ behavior which affects the relationship of stock split announcement and stock market performance.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".