Seeing or Believing? Exploring the Impact of Cross-listing on the Information Environment
Bibliographic record
Abstract
This paper identifies and examines two contrasting mechanisms of information asymmetry for cross-listed firms with respect to the information environment and its impact on corporate governance standards of international firms. We empirically test if the bonding hypothesis is effective in improving corporate governance for cross-listed firms and also assess which mechanism of information asymmetry (‘seeing’ and/or ‘believing’) is more significant by looking at abnormal returns and volume reactions to international firms’ earnings announcements pre and post listing in the U.S. from 1950s to 2012. Our findings indicate that investors ‘seeing’ more (media and analyst coverage) significantly benefits the information environment for cross-listed international firms; however, ‘believing’ more or gaining more credibility--with respect to listed firms adopting more stringent legal and accounting standards-- is not sufficient. Based on our results, we also identify three channels through which the information environment improves for international cross-listed firms--i) through stringent accounting standards (e.g. IFRS), ii) having a common law system, iii) through increased media and analyst coverage--and its implications for international firms pursuing cross-listing.
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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 | 0.017 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".