Trading Volume Reaction to the Earnings Reconciliation from IAS to U. S. GAAP*
Bibliographic record
Abstract
This paper examines the trading volume reaction to the reconciliation of IAS-based earnings to U.S. GAAP on Form 20-F filed during the period of 1995-2001. The sample includes all non-U.S. firms that use IAS as their primary reporting standards and reconcile their earnings to U.S. GAAP in their Form 20-F with available filing dates. The research question is of particular interest to policy makers when evaluating the necessity of reconciling the earnings. We find that the absolute magnitude of the earnings reconciliation is significantly and positively associated with trading volume around the Form 20-F filing dates, which supports the assertion that earnings reconciliation on Form 20-F provides additional information content to U.S. market investors beyond that provided by the IAS earnings. These findings also hold after the Core Standards Project and for various event windows. Moreover, the results indicate a stronger reaction for positive earnings reconciliation than for negative reconciliation.
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".