Impact of initial public offering coalition on deal completion
Bibliographic record
Abstract
Measures of underwriter and top management team prestige have been shown to signal the underlying quality of a company in an initial public offering (IPO). We extend these measures to include the entire coalition (i.e., managers, board, venture capitalists (VCs), underwriters, auditors, and both sets of lawyers) and surprisingly find VCs to have the highest explanatory power in predicting IPO outcomes (completion or withdrawal). Companies with deep management and a separation of the CEO/chair role are more likely to hire prestigious underwriters and successfully complete IPOs. Although companies with prestigious VCs are more likely to have prestigious underwriters, companies with VC-backing are more likely to withdraw the offering, likely to take advantage of better market opportunities. Companies with prestigious underwriters are more likely to have successful IPOs, although we show that the capabilities of underwriters and other intermediaries are more likely driven by activity level (i.e., market share), rather than prestige in affecting IPO outcome. Using an agency framework, we test how signals of monitoring, information asymmetry, bonding, and incentive alignment affect IPO outcomes and show that signals of lower agency costs are associated with a greater likelihood of IPO completion. Finally, because many of these measures are shown to endogenously affect IPO completion, a selection bias may exist in previous IPO studies as up to 70% of IPOs filed annually are not completed.
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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 | 0.004 | 0.029 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".