Auditors' Affirmations of Compliance with IFRS around the World: An Exploratory Study*
Bibliographic record
Abstract
ABSTRACT It is widely believed that international financial reporting standards (IFRS) have been adopted in many countries, at least for the consolidated reporting of listed companies. However, in nearly all cases, what the rules require is some national or supranational version of IFRS. This might create problems for investor confidence and comparability. We examine what companies and auditors report concerning compliance with IFRS, focusing on the first full year of IFRS reporting by companies in the stock market indices of four major European countries and Australia. We find that, even when companies were complying with IFRS, they were generally not saying so, which seems to miss part of the point of the 35‐year project on international harmonization. In a small number of cases, auditors provided dual reports: on full IFRS in addition to the mandated reference to national GAAP where the latter corresponds with full IFRS. These cases were found only in Germany and the United Kingdom, and mainly related to companies that filed with the Securities and Exchange Commission as foreign private issuers. We propose explanations for the general lack of dual reports and for the exceptions. We call for widespread adoption of dual reporting where a plain report on IFRS is not yet possible.
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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 | 0.040 | 0.132 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".