Has Adoption of IFRS Increased Non–North American Institutional Investment in the Canadian Stock Markets?
Bibliographic record
Abstract
Abstract We investigate whether non–North American (non‐NA) institutional investment in firms listed on the Canadian stock markets increased between the pre‐ and post‐IFRS adoption periods relative to such investment in firms listed on the U.S. stock markets. Prior to IFRS adoption, Canada had high‐quality financial reporting standards that were similar to the U.S. standards. As consequences of IFRS adoption, Canadian financial statements became more comparable with European and other IFRS country financial statements and less comparable with neighboring U.S. financial statements. Thus, a question of interest is whether the enhanced comparability with non‐NA companies was beneficial in terms of attracting non‐NA investment to Canadian companies versus U.S. companies. We find that there was no significant change in non‐NA institutional investment in Canadian firms relative to U.S. firms for the very largest (fifth quintile) and for smaller (first, second, and third quintiles) Canadian companies. However, intermediate‐sized Canadian companies in the fourth size quintile lost non‐NA institutional investment relative to their U.S. peer companies, suggesting that non‐NA investors cared more about comparability with U.S. peer companies than non‐NA peer companies for companies in this size quintile.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".