Do IFRS‐Based Earnings Announcements Have More Information Content than Canadian GAAP‐Based Earnings Announcements?
Bibliographic record
Abstract
Abstract Using an event‐study methodology, this study investigates whether the information content of earnings announcements changed for firms traded on the Toronto Stock Exchange (TSX) and the Canadian Venture Exchange (TSXV) following mandatory adoption of International Financial Reporting Standards (IFRS) in Canada. A priori, it may be argued that the information content of earnings would increase for both TSX and TSXV firms if IFRS earnings provided more value‐relevant information than Canadian GAAP earnings. Increased value relevance of information provided by IFRS earnings would likely reflect increased measurement of changes in net asset values based on expectations as opposed to realizations. Because values based on expectations are subject to greater divergence of opinion than values based on realizations, greater value relevance is likely to be accompanied by higher abnormal return volatility and abnormal trading volume during announcement periods. Consistent with this argument, we find that abnormal volatility and abnormal volume during earnings announcement periods were higher in post‐IFRS announcement periods than in pre‐IFRS announcement periods for firms traded on the TSX. We discriminate across the two exchanges in terms of information quality based on the mix of institutional and retail investors, analyst following, concentration in the oil, gas and mining sectors, and size of firm. We test for a residual difference in information content based on the more speculative nature of the TSXV exchange and find some evidence that divergence of opinion was higher for TSXV firms than TSX firms in the pre‐IFRS period but this residual difference does not carry through to the post‐IFRS period.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".