The impact of <scp>SEC</scp> reporting changes on information acquisition and market dynamics: Evidence from foreign cross‐listed firms
Bibliographic record
Abstract
Abstract This paper examines how a change in disclosure regulation influences investors' information acquisition and trading across multiple markets. We leverage the 2007 elimination of the Form 20‐F reconciliation requirement for cross‐listed firms that prepare financial statements under IFRS. Using a difference‐in‐differences research design, we show that investors acquire fewer Form 20‐Fs of IFRS‐reporting cross‐listed firms when these forms are not filed in a timely manner relative to the home‐country earnings announcement. We also find an increased acquisition of earnings‐specific 6‐Ks, indicating a shift in investor attention from delayed and unreconciled 20‐Fs to more timely earnings releases in the home country. Furthermore, we find that American Depositary Receipt (ADR) market reactions to local earnings announcements increase after the deregulation, especially for firms with strong home‐country institutions. In addition, we find that the deregulation increases return co‐movement between the US ADR market and the home‐country stock market for IFRS filers' shares. Our results bring novel insights regarding the cross‐market impact of the disclosure regulation change.
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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 | 0.008 |
| 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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".