Secondary Equity Offer Announcements and Share Returns at Nairobi Securities Exchange, Kenya
Bibliographic record
Abstract
This paper empirically analyzes the effect of secondary equity offer announcements on share returns at Nairobi Securities Exchange in Kenya by investigating the information content of the announcements and ascertaining whether the release of financial information in the capital market affects share returns. An event study employing the market return model determined share returns of 52 bonus issues and 28 rights issues announced between January 2006 and December 2015. The study established that secondary equity offer announcements had a significant and positive effect on share returns and that rights issues witnessed higher share returns when compared to bonus issues during the twenty-day event period. This study recommends management of Nairobi Securities Exchange listed companies to raise capital through secondary equity offers, as companies will increase their market capitalization. Investors on Nairobi Securities Exchange are encouraged to participate in secondary equity offers because they will earn positive share returns and increase their wealth. Existing shareholders should fully participate in rights issues because they will forgo positive share returns if they renounce their rights. Capital Markets Authority and Nairobi Securities Exchange should encourage more listed companies to raise capital through secondary equity offers, as this is advantageous to companies and investors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".