Does the 20-F Reconciliation Affect Investors' Perception of Comparability between Foreign Private Issuers (FPIs) and U.S. Firms?
Bibliographic record
Abstract
SYNOPSIS Until recently, all Foreign Private Issuers (FPIs) listed on U.S. exchanges were required to reconcile their non-U.S. GAAP financial statements with U.S. GAAP in their annual Form 20-F filing. In November 2007, the Securities and Exchange Commission (SEC) eliminated this requirement for FPIs reporting in IFRS. We use this rule change to provide evidence on whether the U.S. GAAP reconciliation affects investors' perception of the degree of comparability between FPIs and domestic U.S. firms reporting in U.S. GAAP. To do so, we test whether the SEC's rule change reduced information transfer from IFRS-reporting FPIs to comparable U.S. firms at the FPIs' earnings announcements. Consistent with the U.S. GAAP reconciliation increasing investors' perception of comparability between FPIs and U.S. firms, we find that information transfer from IFRS-reporting FPIs to comparable U.S. firms decreased significantly after the rule change, on average. We also find evidence consistent with a decrease in comparability for financial analysts forecasting earnings for comparable U.S. firms. In contrast, we find no evidence of a similar decrease in information transfer for FPIs not reporting in IFRS that are unaffected by the rule 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 | 0.003 | 0.026 |
| 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 | 0.000 |
| 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".