Management Quality and Equity Issue Characteristics: A Comparison of SEOs and IPOs
Bibliographic record
Abstract
We use hand‐collected data on the management quality of firms making seasoned equity offerings (SEOs) or initial public offerings (IPOs) to analyze the relationship between management quality and equity issue characteristics, and to compare the effect of management quality on SEOs versus IPOs. We hypothesize that higher quality managers are more credible to equity market investors, thereby reducing the information asymmetry they face in the market and outsiders’ information production costs. Therefore, the equity issues of higher management quality firms will have more reputable underwriters, smaller underwriting spreads, and other expenses, and smaller SEO discounts. Further, since better managers will be able to select better projects, higher management quality firms will have larger offer sizes. Finally, since SEO firms are likely to suffer from less information asymmetry compared to IPO firms, these effects will be smaller for SEOs than for IPOs. Our findings support the above hypotheses. Our direct tests of the relationship between management quality and information asymmetry, and our comparison of information asymmetry in SEOs versus IPOs provide further support for these hypotheses.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".