Underwriter Quality and Long‐Run IPO Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We analyze the relationship between the quality of underwriters and the long‐run performance of initial public offerings (IPOs) in light of underwriter marketing, certification and screening, and information production. We find that higher underwriter quality (measured by the number of managing underwriters, underwriter reputation, and absolute price adjustment) predicts better long‐run performance, even when returns are value weighted. We compare underwriter quality measures and find that the effects of the number of managing underwriters and underwriter reputation are mutually complementary and are especially strong among IPOs with high uncertainty, while absolute price adjustment, which is more likely to be associated with information production than marketing or certification/screening, loses significance. Our findings are consistent with the marketing and certification and screening roles of investment banks but lend little support for the information production role of underwriters.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it