The Convergence of <scp>IFRS</scp> and U.S. <scp>GAAP</scp>: Evidence from the <scp>SEC</scp>'s Removal of Form 20‐F Reconciliations
Bibliographic record
Abstract
Abstract We use the SEC's 2007 decision that eliminates the reconciliation requirement for foreign listed private issuers (FPIs) reporting under IFRS as a natural experiment to examine whether IFRS and U.S. GAAP produce accounting information of comparable quality. We conduct statistical analyses using a sample of 563 firm‐year observations of FPIs during the period 2002 through 2008 for a panel of 70 FPIs that report under IFRS as our treatment group and 46 FPIs that report under U.S. GAAP as our control group. Using a stock‐valuation model and a difference‐in‐differences (DD) analysis of scaled residuals we examine whether the reconciliation values reported by IFRS FPIs prior to 2007 were value relevant and if the elimination of the reconciliation information in the post‐intervention period (2007 and 2008) has adversely affected the information available to investors to make stock‐valuation decisions. The results for the 2002 through 2006 period suggest that the reconciliation values for book value were value relevant. The DD analysis shows that while the residual‐value model incorporating reconciliation information produced comparable scaled stock price residuals for IFRS and U.S. GAAP FPIs prior to 2007, the removal of the reconciliation information did not lead to statistically significant increases in scaled stock price residuals for IFRS and U.S. GAAP FPIs.
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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 | 0.012 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".