Bibliographic record
Abstract
This paper examines the effects of the IFRS adoption on earnings quality of 1245 Canadian firms. I analyze the effects IFRS adoption on earnings persistence, earnings predictability, persistence of earnings components, cash flow predictability, accruals quality, value relevance, earnings smoothness, conservatism, and timeliness. I find that earnings quality of Canadian firms, on average, improves following the adoption and the improvements are mostly driven not by U.S. adopters but by IFRS adopters, suggesting that IFRS has a positive impact on earnings quality. Partitioning the sample, I find that firms with incentives for transparent reporting have stable earnings quality throughout the sample period whereas firms without such incentives show an improvement in earnings quality following the adoption. I also find that earnings quality declines to a greater degree for firms in extractive/high-litigation-risk industries relative to firms in non-extractive/low-litigation-risk industries. Further analyses reveal that (1) earnings quality seems to deteriorate for firms with intense reliance on fair value accounting after the adoption but not for firms with minimal reliance on fair value accounting, that (2) R&D intensive firms see some weak improvements in earnings quality following the adoption in comparison to non-R&D intensive firms, and that (3) IFRS adoption is associated with a greater improvement in earnings quality for loss firms than for profitable firms. Finally, the effects of IFRS seem unlikely to be uniform across different measures of earnings quality. Taken all together, the findings suggest that standard setters and researchers should probably not consider the effects of IFRS in isolation of firms' reporting incentives and that the SEC, that the Financial Accounting Standards Board's (FASB) concerns about the lack of implementation guidance in extractive and high-litigation-risk industries are warranted, and that fair value accounting is likely to be harmful to earnings quality.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".