The Price of Being Foreign: Stock Market Penalties Associated with Accounting Irregularities for U.S.‐Listed Foreign Firms
Bibliographic record
Abstract
ABSTRACT We examine the stock market consequences of disclosing accounting irregularities for U.S.‐listed foreign firms. After controlling for the severity of the irregularity and other firm characteristics, we find that foreign firms experience significantly more negative short‐window stock market reactions following irregularity announcements than do U.S. firms. Moreover, for a subsample of 64 irregularities of foreign firms that are listed on both a U.S. and home country stock exchange, we find evidence that restating firms' U.S. investors react more negatively to the same irregularity than their home country investors. This differential market reaction appears related to firm‐specific information risks that are greater for foreign firms than U.S. firms. Collectively, consistent with the reputational bonding hypothesis in prior literature, our results suggest that accounting irregularities cause U.S. investors to reassess the information risk associated with foreign firms.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".