1 Long-Run Performance of European Listings in the U.S.
Bibliographic record
Abstract
The dramatic surge in the European listings on the U.S. exchanges in the 1990s raises questions about the underlying motivation and the value of U.S. listings. We explore this issue by examining long-run stock price performance of European listings in the U.S. relative to their European and U.S. industry peers. We document that European firms list in the U.S. after a strong market performance relative to their peers. This trend continues in the post-listing period as European listings outperform their industry peers by about 30 percent to 40 percent during three years subsequent to listing. This evidence is in contrast to the Canadian listings in the U.S. which underperform their domestic market indexes subsequent to listings. Univariate analysis, however, shows substantial variation across firms by country, industry, exchange of U.S. listing and whether a listing is an initial public offering or not. Multivariate analysis is proposed to be completed by July 2003. 3
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".