A comparison of direct listings and<scp>IPOs</scp>
Bibliographic record
Abstract
Abstract IPOs and direct listings (DLs) offer two different mechanisms for firms to go public. In contrast to IPOs, DLs do not employ an underwriter or raise new capital. Using a sample of IPOs and DLs on major stock markets in the European Union, we document that firms that choose to go public via DLs are larger, more profitable, and less levered, on average, than IPO firms. These pre‐listing differences suggest that DL firms should be less risky than IPO firms; however, controlling for this selection effect, we find that DLs have higher aftermarket price volatility than IPOs. This is consistent with some policy‐makers' concerns that, because they lack an underwriter, DLs expose investors to higher risk than IPOs in the immediate post‐listing period. We show that this heightened price volatility persists, on average, for the first 20 trading days after listings, and is larger in industries where listed peer firms provide relatively low‐quality disclosures. Our results provide new evidence regarding the types of firms that choose to list via DLs versus IPOs and the riskiness of IPOs versus DLs in the immediate post‐listing period; additionally, our results are consistent with underwriters improving the quality of information available to investors for IPO firms in the pre‐listing period.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".