Convergence in Motion: A Review of Fair Value Levels’ Relevance
Bibliographic record
Abstract
The IFRS 13 post-implementation review by the IASB motivates our investigation on the value relevance of fair value (FV) measurement hierarchy (i.e. level 1, level 2, and level 3). First, using a meta-analysis, which allows us to summarize inconsistent empirical findings, we synthesize studies on the value relevance of the FV hierarchy. Overall, value relevance is lower for level 3 than for levels 1 and 2, but it increases over time. In non-U.S. studies, we note lower value relevance across all levels of FV assets. Underlying asset fundamentals, model risk, and measurement process complexity may contribute to this value relevance gap. Second, from interviews with professionals from financial institutions, we note that, in practice, there has been extensive learning about FV accounting since the 2007–9 Financial Crisis and a formalization of the valuation process that the academic literature has yet to fully recognize. We thus highlight conceptual and methodological issues and areas for research with practical implications.
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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 | 0.073 | 0.236 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.026 | 0.017 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".