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Record W2331209103 · doi:10.1111/1475-679x.12035

Uninvited U.S. Investors? Economic Consequences of Involuntary Cross‐Listings

2013· article· en· W2331209103 on OpenAlexaff
Peter Iliev, Darius P. Miller, Lukas Roth

Bibliographic record

VenueJournal of Accounting Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBusinessEconomicsMonetary economicsFinancial systemFinance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.141
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.004
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.038
GPT teacher head0.303
Teacher spread0.264 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations35
Published2013
Admission routes1
Has abstractyes

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