Back to the Origins of the Initial Public Offerings Price Range: Underwriter-Funds Network and Information Production Timeline
Bibliographic record
Abstract
Although the initial price range in U.S. Initial Public Offerings (IPOs) is constrained by SEC regulations, a non-negligible percentage of IPO price ranges falls outside the ‘safe harbour’. We investigate how the price range - which sends the very first signals on the IPO quality to the market - is set in the due diligence phase, with special attention to unexplored networking patterns between underwriters and institutional investors. By making use of a Mixture Model applied to 1,246 US firms listed between 2004 and 2016, we show that underwriters that are centrally positioned in their network of regular investors are more likely to set a price range that is compliant with SEC guidelines. We argue that the flexibility resulting from being safe harbour-compliant allows underwriters to preserve their reputation for fair dealing with issuers by exploiting a dumping ground proviso or quid pro quo agreements with their network funds. Despite information produced by network funds in the due diligence step having no significant effect on the width of the price range, in our study, we provide evidence that the range does serve as a proxy of the uncertainty of the listing firms.
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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 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".