Does Mandatory IFRS Adoption Improve the Information Environment?*
Bibliographic record
Abstract
More than 120 countries require or permit the use of International Financial Reporting Standards (IFRS) by publicly listed companies on the basis of higher information quality and accounting comparability from IFRS application. However, the empirical evidence about these presumed benefits is often conflicting and fails to distinguish between information quality and comparability. In this paper we examine the effect of mandatory IFRS adoption on firms’ information environment. We find that after mandatory IFRS adoption consensus forecast errors decrease for firms that mandatorily adopt IFRS relative to forecast errors of other firms. We also find decreasing forecast errors for voluntary adopters, but this effect is smaller and not robust. Moreover, we show that the magnitude of the forecast error decrease is associated with the firm‐specific differences between local GAAP and IFRS. This finding suggests that it is IFRS adoption rather than a correlated unobservable factor that is causing forecast errors to decrease. Exploiting individual analyst level data and isolating settings where analysts would benefit more from either increased comparability or higher quality information, we document that the improvement in the information environment is driven both by information and comparability effects. These results suggest that mandatory IFRS adoption has improved the quality of information intermediation in capital markets, and as a result firms’ information environment, by increasing both information quality and accounting comparability.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".