The Impact of Stock Dividends and Stock Splits on Shares’ Prices: Evidence from Egypt
Bibliographic record
Abstract
This research aims to examine the effect of two types of corporate actions,“Stock Split” and “Stock Dividends”, on the shares’ prices, liquidity changes, and price volatility; and to investigate the efficiency of the Egyptian stock market in response to the announcement of the corporate actions. The research provides the investors with a scientific tool to predict and explain changes in stock prices in response to announced corporate actions and to improve their investment decision-making process.The objective is to investigate whether the two actions collectively or independently have a positive impact on the prices of the related stocks listed on the Egyptian Stock Exchange (EGX), and assess the similarities and dissimilarities between their individual impacts.We applied the “Event Study” approach to measure the impact of the stock splits and stock dividends announcement on the stock prices through measuring the cumulated average abnormal return (CAAR) resulted from events to assess their impact on the stock performance around the announcement day (for a period of 30 prior and 30 days post announcement) as applied before by Terhi (2011). The analysis concluded that the announcement of both of stock split and stock dividend has a positive impact on stock prices. This positive impact drove the authors to test the efficiency of EGX in respect of the impact of the announcement the corporate actions to the public investors. A correlation analysis is performed to reflect this impact.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".