The Pricing and Performance of Supercharged IPOs
Bibliographic record
Abstract
ABSTRACT This study examines a new form of initial public offerings, “supercharged” IPOs, where a firm-organized pre-IPO as a pass-through entity undergoes a series of transactions that steps-up the adjusted tax basis of the IPO firm's assets. This step-up imposes tax liabilities on pre-IPO owners, but also creates significant future tax benefits for the firm; the average anticipated deferred tax asset is $486 million ($13 per share) for our sample of supercharged IPO firms. Pursuant to tax receivable agreements, supercharged IPO firms pay a large portion of these tax benefits to pre-IPO owners as they are realized in the future. Future firm performance must be sufficiently strong for the IPO firm and the pre-IPO owners to realize the future tax benefits created by the supercharged transaction structure. We hypothesize and provide evidence of higher IPO offer prices and stronger future performance for supercharged IPO firms relative to traditional IPO firms. JEL Classifications: G14; G32; G34; H25.
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".