Does Manager Ability Influence Prospectus Earnings Quality and IPO Underpricing?
Bibliographic record
Abstract
Prior literature suggests that manager ability influences several factors, including financial reporting quality, key to the bargaining power of an issuing firm during their initial public offering (IPO). However, we also know that high ability managers are better able to engage in and conceal opportunistic behavior which may dampen any positive effects their abilities have in the IPO process. Given the conflicting affect that managerial ability may have on financial reporting and firm performance in the IPO setting, we examine the impact of manager ability on prospectus earnings quality and IPO underpricing. We find that IPO firms with high ability managers tend to have better earnings quality and are less underpriced than firms with low ability managers. We also find preliminary evidence that equity ownership strengthens the relationship between manager ability and IPO underpricing. Our findings are consistent with the streams of literature suggesting that better managers produce higher quality earnings and raise more capital during the IPO to invest in future growth opportunities if they are closely monitored. These findings should be useful to issuing firms considering hiring high caliber managers, investors in evaluating IPO firms, and researchers in examining the influence of human capital on IPO underpricing.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".