What Companies Need to Know About International Cross‐Listing
Bibliographic record
Abstract
This article addresses four questions about cross‐listing by non‐U.S. companies on a U.S. stock exchange: Why do companies cross‐list? Does a U.S. listing increase firm value? If so, what are the sources of the increased valuation? And finally, how has the Sarbanes‐Oxley Act (SOX) affected the value of a U.S. listing? Both managerial surveys and academic research show that companies list in the U.S. to increase visibility and share liquidity, to broaden their shareholder base, to gain access to cheaper financing and reduce the cost of capital, and, in some cases, to implement a global business strategy. Foreign companies also typically cross‐list after periods of strong market performance and experience a positive valuation effect around the time of listing, but then underperform the market in the period after the cross‐listing. On average, cross‐listed companies exhibit higher valuations than their home‐market peers, but with significant variation based on firm characteristics: The valuation premiums are larger for smaller companies with higher past sales growth, higher ROAs, and lower financial leverage. In the long run, the companies that show a permanent increase in valuation are those that succeed in expanding their U.S. shareholder base and improving their levels of shareholder protection. Finally, the evidence suggests that SOX, while perhaps deterring some would‐be overseas listings, has not seriously eroded the net benefits of a U.S. listing.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".