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Record W2944258469 · doi:10.1111/1911-3838.12200

Has Adoption of IFRS Increased Non–North American Institutional Investment in the Canadian Stock Markets?

2019· article· en· W2944258469 on OpenAlexaffvenueabout
Shahid Khan, Mark C. Anderson, Hussein A. Warsame, Michael Wright

Bibliographic record

VenueAccounting Perspectives · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComparabilityBusinessAccountingInternational Financial Reporting StandardsStock (firearms)Investment (military)Institutional investorFinanceFinancial systemCorporate governance

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.854

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.009
GPT teacher head0.204
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2019
Admission routes3
Has abstractyes

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