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Record W3122034921

Why Do Foreign Firms Leave U.S. Equity Markets? An Analysis of Deregistrations under SEC Exchange Act Rule 12h-6

2008· preprint· en· W3122034921 on OpenAlexfundno aff
Craig Doidge, George Andrew Karolyi, René M. Stulz

Bibliographic record

VenueTSpace · 2008
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersDaiichi Sankyo CompanySocial Sciences and Humanities Research Council of CanadaDaiichi Sankyo EuropeMerck KGaA
KeywordsCommissionBusinessStock exchangeCross listingMonetary economicsEquity (law)Listing (finance)IssuerCapital marketStock (firearms)AccountingFinanceEconomicsCorporate governance
DOInot available

Abstract

fetched live from OpenAlex

On March 21, 2007, the Securities and Exchange Commission (SEC) adopted Exchange Act Rule 12h-6 which makes it easier for foreign private issuers to deregister and terminate the reporting obligations associated with a listing on a major U.S. exchange. We examine the characteristics of 59 firms that immediately announced they would deregister under the new rules, their potential motivations for doing so, as well as the economic consequences of their decisions. We find that these firms experienced significantly slower growth and lower stock returns than other U.S. exchange-listed foreign firms in the years preceding the decision. There is weak evidence that firms experience negative stock returns when they announce deregistration and stronger evidence that the stock-price reaction is worse for firms with higher growth. When we examine stock-price reactions around events associated with the passage of the Sarbanes-Oxley Act (SOX), we find negative average stock-price reactions with some specifications but not others. Further, there is no evidence that deregistering firms were affected more negatively by SOX than foreign-listed firms that did not deregister. Our evidence supports the hypothesis that foreign firms list shares in the U.S. in order to raise capital at the lowest possible cost to finance growth opportunities and that, when those opportunities disappear, a listing becomes less valuable to corporate insiders so that firms are more likely to deregister and go home.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.064
GPT teacher head0.310
Teacher spread0.247 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations8
Published2008
Admission routes1
Has abstractyes

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