Does Mandatory Adoption of IFRS Enhance Earnings Quality? Evidence From Closer to Home
Bibliographic record
Abstract
The global accounting convergence and the often discussed probable adoption of International Financial Reporting Standards (IFRS) by U.S. regulators is a timely topic. We contribute to the literature by examining a more recent mandatory IFRS adoption by Canada. Canadian GAAP (CGAAP) is often considered a close substitute for U.S. GAAP. One key feature of this setting is that two earnings numbers are available for fiscal year 2010 since Canadian firms were required to reconcile earnings under CGAAP with earnings under IFRS. We run a “horse race” of earnings quality between earnings under CGAAP and IFRS. We find that on average, relative to IFRS-earnings, earnings under CGAAP have greater association with next period cash flows and higher degree of persistence. Further, when the difference between earnings under CGAAP and IFRS is large, IFRS-earnings are less value-relevant and less persistent. These results strongly support the notion that higher earnings quality is associated with CGAAP. Finally, the results also indicate that differences between CGAAP and IFRS with regard to accounting for financial instruments and investments significantly impair the quality of IFRS-earnings. Our findings are potentially informative to any revival of policy debates on the possible adoption of IFRS by U.S. firms.
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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 | 0.006 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".