Uninvited U.S. Investors? Economic Consequences of Involuntary Cross‐Listings
Bibliographic record
Abstract
ABSTRACT We study the economic consequences of a recent Securities and Exchange Commission securities regulation change that grants foreign firms trading on the U.S. over‐the‐counter (OTC) market an automatic exemption from the reporting requirements of the 1934 Securities Act. We document that the number of voluntary (sponsored) OTC cross‐listings did not increase following the regulation change, suggesting that it did not achieve its intended purpose of increasing voluntary OTC cross‐listings through a reduction in compliance costs. We do find that the design of the regulation allowed financial intermediaries to create an unprecedented number of involuntary (unsponsored) OTC ADRs: 1,700 unsponsored ADR programs for 920 firms were created for companies that had previously chosen not to cross‐list in the United States. Our difference‐in‐differences analysis based on a matched sample approach documents that foreign firms forced into the U.S. capital markets experience a significant decrease in firm value, and we further show that the decrease in firm value is related to an increase in U.S. litigation risk. We also find that depositary banks’ propensity to involuntarily cross‐list firms is positively related to banks’ expected fee revenue, and that banks chose firms that incur high costs when involuntarily cross‐listed. Our results provide evidence that securities regulation can be exploited for private gain and result in costly unintended consequences.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".