IFRS Adoption and Liquidity: A Comparative Analysis of Canada with Australia and the United Kingdom
Bibliographic record
Abstract
In this study, we investigate the impact of countrywide adoption of IFRS on the liquidity of domestic versus international firms listed in a stock market. We consider two competing forces affecting liquidity from IFRS adoption: enhanced comparability of firms within industries that span international boundaries and less tailoring of financial reporting to meet local investor needs. We compare liquidity - bid-ask, zero returns and trading volume - before and after IFRS adoption for domestic and international firms listed on the Canadian exchanges with liquidity before and after IFRS adoption for firms listed on the Australian and U.K. exchanges. These three countries share the British influence on financial reporting historically and have similar legal and institutional settings. We expect that the benefits of global comparability would be relatively greater for firms listed on the U.K. exchanges relative to firms listed on the Canadian and Australian exchanges given the U.K.’s proximity and commerce with other European countries. For Canadian listings, we find that liquidity (bid-ask spreads and zero returns) increased for non-US international firms but decreased (all three variables) for Canadian domestic firms, consistent with trade-offs between international comparability and localization. We find that liquidity increased for both U.K. domestic and international firms listed on the U.K. exchanges but decreased for both types of firms listed on the Australian exchange. For Canadian firms, our exchange level tests indicate that TSX Venture exchange firms lost more liquidity than TSX exchange 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.012 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".