Financial statement effects of adopting IFRS: the Canadian experience
Bibliographic record
Abstract
Purpose The purpose of this study is to examine the effects of adopting International Financial Reporting Standards (IFRS) on financial statements of the largest Canadian firms (S&P/TSX 60) listed on the Toronto Stock Exchange (TSX). Design/methodology/approach This study investigates the financial statement effects of 46 companies from the S&P/TSX 60 index which report under IFRS in 2011 and switched to IFRS from CGAAP. This study used panel data analysis, which can be considered as more powerful when conducting cross-sectional and in time analysis among companies. Because of weakness of Cramer statistic on R-square, the authors used interaction terms as suggested by Hope (2007). Findings Consistent with the authors’ perceptions, this study finds that significant effects of adopting IFRS are associated with industry practices. The empirical results show that the adoption of IFRS in Canada created more relevant financial reporting for book value of equity and net income in the post-adoption periods. Originality/value This study should be of interest to the US regulators considering IFRS adoption by US publicly traded companies as well as to regulators, standard setters and listed companies in all countries worldwide that are in transition to IFRS.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".