The Survival Profile of U.S. IPO Issuers 1985-2005
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
This article examines the survival profile of U.S. Initial Public Offerings (IPOs) for the 1985-2005 period. More specifically, the authors develop Multinomial Logit models based on the information contained in the prospectus and attempt to determine what factors influence the post-issue transition of the IPO firms into survivors, non-survivors or targets. The main findings of the article are that larger IPOs experience a lower probability of delisting, and higher underpricing increases the probability of failure or becoming a target. Further, the presence of venture capitalists and a prestigious underwriter at the IPO stage seems to influence the post-IPO transition state. They also estimate an accelerated-failure-time model as a robustness test and confirm their previous results. They also find that the survival time is negatively affected if the IPO is in the internet sector.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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