To wait or not to wait: When do announced Initial Public Offerings are completed?
Bibliographic record
Abstract
This paper proposes a model that formalizes the optimal external timing for an initial public offering using real options concept and also presents empirical analysis. It is the first study to investigate the factors influencing the IPO waiting period. We find strong evidence of information production by waiting period. In line with the predictions of our model, the waiting period is more likely to be longer the larger syndicate size. We argue that the high competition risk among syndicate members (Corwin and Schultz, 2005) for larger syndicate size sets back the completion of the IPO. We provide evidence that the waiting period is also strongly related to leverage, investment and managerial incentives. Controlling for other potential determinants, we show that the probability of switching syndicate size in subsequent SEOs is strongly related to waiting periods and underwriter switches. We finally show that the longer the SEO waiting period the better the first-day market reaction on subsequent SEO date given that longer waiting periods are associated with less adverse selection risk.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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; both teacher heads agree on what is shown here.
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".