Optimal Rationing in IPOs with Risk Averse Institutional Investors
Bibliographic record
Abstract
Using a mechanism design approach, we derive endogenously the optimal IPO mechanism when institutional investors are risk averse. We show that the optimal allocation rule is such that all the institutional investors with sufficiently good information are allocated a positive quantity of shares which is increasing in the quality of the their information. Additionally, we also derive the optimal rationing scheme which is uniform, i.e. all institutional investors are rationed by the same amount of shares [JEL Classification: D8, G2]. In questo articolo, usando un approccio di mechanism de-sign, deriviamo endogenamente il meccanismo di offerta pubblica ottimale quando gli investitori istituzionali sono avversi al rischio. Il meccanismo è tale per cui tutti gli investitori istituzionali con un’informazione sufficientemente buona ricevono una quantità positiva di azioni, che risulta inoltre crescente nella qualità del-l’informazione. Infine, deriviamo anche lo schema di razionamen-to ottimale che è uniforme cioè tutti gli investitori istituzionali sono razionati per un uguale numero di azioni.
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".