How Are Foreign Firms Valued in U.S. Markets? Evidence from Firm and Country Characteristics
Bibliographic record
Abstract
This paper investigates the determinants of foreign firms’ value in U.S. markets by examining both firm and country characteristics. Prior studies have agreed on foreign firms’ value premium when they cross-list stocks in U.S. exchanges. However, little research has pursued evidence regarding how these foreign firms are valued after the cross-listing. I attempt to answer this question by comparing the determinants of firm value for both foreign cross-listing firms and U.S. domestic firms. The results from regression models show that, although foreign firms share similar firm-level determinants with U.S. firms (firm size, firm leverage, and firm growth), they are on average undervalued by U.S. investors. Furthermore, the home countries’ characteristics, such as the rule of law, play an important role in foreign firms’ market value. In fact, the undervaluation is only observed in foreign firms from the weak rule of law countries, but not from strong rule of law countries. Overall, foreign firms’ market value is determined by both firm-level and country-level characteristics after they cross-list in the U.S. markets.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".