An Empirical Analysis of Underpricing and Oversubscription between Venture-Backed IPO and Non-Venture-Backed IPO in Italy
Bibliographic record
Abstract
Over the years, in order to meet the financial needs of companies, new forms of financing alternative to the traditional banking channel have been developed. These include the institutional investment in risk capital, which is defined by the terms Anglo-Saxon venture capital and private equity. In this empirical analysis, the divestment of the venture capitalist's participation will be emphasized by listing the invested companies in the stock market, a channel not widely used in Italy, but highly desired because of the various benefits it can bring. Analyzing the IPOs that were carried out in Italy on the main list from 2007 to 2017, we will verify what is described in the economic literature, which is that a venture capitalist, by performing a certification role, is able to reduce the information asymmetries presented in the listing process and, as a result, contain underpricing and improve oversubscription. By using the presence of a venture capitalist within the venture capital as the only variable, it has been observed that on average the underpricing and oversubscription of the venture-backed IPOs slightly differentiate from the non-venture-backed IPOs. However, the study carried out shows that this difference, although not significant, turns out to be very interesting.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".