The Value Relevance of Fair Value Levels: Time Trends under IFRS and U.S. GAAP
Bibliographic record
Abstract
The IASB's post-implementation review of IFRS 13 Fair Value Measurement motivates our analysis of the evolution of the value relevance of fair value (FV) levels over time on banks that report under IFRS and U.S. GAAP. For both sets of standards, results provide evidence that is consistent with (1) an increase in value relevance across all three FV levels over time, and (2) a convergence of the value relevance of the three FV levels over time. However, FV levels exhibit systematically higher value relevance under U.S. GAAP compared to IFRS. Such gap has closed to some extent since the enactment of IFRS 13. This evolution is likely due to learning about FV accounting and changes in financial reporting regulations that increased disclosure requirements. These findings confirm the IASB's conclusions that FV levels’ disclosure is useful to users of financial statements, but also emphasizes preparers and investors’ learning over time.
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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 | 0.017 | 0.082 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".