The Impact of Eliminating the 20-F Reconciliation Requirement for IFRS Filers on Earnings Persistence and Information Uncertainty
Bibliographic record
Abstract
SYNOPSIS: On November 15, 2007, the U.S. Securities and Exchange Commission (SEC) eliminated the requirement that foreign private issuers reporting under International Financial Reporting Standards (IFRS) include a reconciliation to U.S. GAAP in their 20-F filing. To the extent that the reconciliations had information content, it is possible that the information environment of IFRS filers deteriorated in the post-reconciliation period, unless they voluntarily improved disclosure quality. Using difference-in-differences tests, we examine whether there was any change in the persistence of earnings and analyst forecast dispersion after the new regulation. We find that earnings persistence increased (did not increase) and analyst uncertainty measured by the forecast dispersion did not increase (increased) for firms domiciled in weaker (stronger) investor protection countries. These results suggest that firms from a weaker investor protection environment had a greater incentive to “signal” the quality by voluntarily improving the disclosure quality in the post-reconciliation period to compensate for any possible information loss from no longer providing the reconciliation. Our findings also suggest that the elimination of the reconciliation requirement did not have a uniform effect on IFRS filers and that the effect varies with the firm's home country reporting environment. JEL Classifications: M41
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".