Does Disclosure Regulation Work? Evidence from International IPO Markets*
Bibliographic record
Abstract
This study examines whether IPO disclosure requirements mandated by countries’ securities laws are associated with variation in IPO underpricing in international IPO markets. Our empirical analysis uses a unique sample of 6,025 IPOs from 34 countries over the period from 1995 to 2002. We show for the first time that the stringency of disclosure requirements for IPO prospectuses is negatively associated with the extent of IPO underpricing, after controlling for various country‐ and firm‐level determinants of underpricing. Moreover, we find that the disclosure effect on IPO underpricing is moderated by the extent of a country’s capital market integration. Taken together, our findings are consistent with the view that increased disclosure regulation appears to reduce IPO underpricing and hence the cost of equity, and that institutional factors such as capital market integration play an important role in understanding the economic consequences of disclosure regulation in international IPO markets.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".