The Impact of IFRS Adoption on Earnings Quality: A Study Conducted on Foreign Issuers in the United States
Bibliographic record
Abstract
The worldwide adoption of International Financial Reporting Standards (IFRS) is affecting many countries around the globe as it has become widely spread. Since 2007 the United States (US) allows foreign issuers to voluntarily adopt IFRS. This paper investigates the effect of IFRS adoption on earnings quality after voluntary IFRS adoption was allowed to foreign issuers in the US. More precisely, the discretionary accruals and the small positive earnings are tested for a sample of foreign issuers in the US that are registered and reporting with the SEC, comparing a pre-period from 2002 to 2006 with a post-period from 2008 to 2011. The results from the difference-in-differences regression analysis suggest that in terms of discretionary accruals there is no statistical difference between the pre-IFRS and the post-IFRS period, therefore the earnings quality remains the same. For small positive earnings it is found that, when foreign issuers incorporate IFRS, these are lower, indicating higher earnings quality.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".