Firm Factors and Share Returns of Secondary Equity Offers at Nairobi
Bibliographic record
Abstract
This paper provides an empirical analysis of the effect of firm factors namely size, profitability, leverage and shareholding structure on share returns of secondary equity offers at Nairobi Securities Exchange in Kenya. An event study employing the market model determined share returns of 52 bonus issues and 28 rights issues announced between January 2006 and December 2015. Multivariate linear regression analysis established the effect of size, profitability, leverage and shareholding structure on share returns of secondary equity offers obtained from the event study. The results of the event study indicate that secondary equity offer announcements had a significant positive effect on share returns and thus investors increased their wealth during the event period. The results of multivariate linear regression analysis revealed that profitability and shareholding structure had a significant positive effect on share returns, size had a significant negative effect on share returns while leverage did not affect share returns. The study recommends investors to participate in secondary equity offers of small sized profitable companies with a high proportion of institutional investors because they will realize positive share returns and increase their wealth. The study further recommends management of small sized and profitable companies with a high proportion of institutional investors to raise capital through secondary equity offers as this will increase their market capitalization. The Capital Markets Authority and Nairobi Securities Exchange should consider size, profitability and shareholding structure when screening companies seeking approval to raise capital through secondary equity offers.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".