The Impact of mandatory conversion to IFRS on the net income of FTSEurofirst 80 firms
Bibliographic record
Abstract
Since the start of 2005 all European Union (EU) firms trading in a regulated market are required to adopt International Financial Reporting Standards (IFRS) for their consolidated financial accounts. Many more countries will - soon or later - follow suit and adopt IFRS for all listed firms. Nonetheless, the process of conversion from domestic standards to IFRS can often cause confusion for both preparers and user groups. This study investigates the impact of the mandatory conversion to IFRS on the Net Income of some of the largest firms in the EU - specifically, the constituents of the FTSEurofirst 80 index. The sample for the present work comprises those 37 constituents of the FTSEurofirst 80 index which: (i) are first time adopters of IFRS and (ii) had, at the sample selection date, voluntarily revealed their 2004 annual Net Income figures under both national standards and IFRS. Our results show that the conversion to IFRS leads to a statistically significant increase in 2004 Net Income and for nearly 75% of sample firms we find that the increase is material at the 5% level. Additional analysis reveals that IFRS 3 (Business Combinations) dwarfs all other international standards in terms of driving the observed differences between reported Net Income under domestic GAAP and IFRS for our sample 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".