Mandatory Fair Value Accounting and Information Asymmetry: Evidence from the European Real Estate Industry
Bibliographic record
Abstract
We examine the effects of mandating the provision of fair value information for long-lived tangible assets on firms' information asymmetry. Specifically, we investigate whether European real estate firms' compulsory adoption of International Accounting Standard 40 (IAS 40; Investment Property), which mandated the provision of investment property fair values in 2005, resulted in reduced information asymmetry across market participants. Using as a control group firms that voluntarily provided these fair values prior to the mandatory adoption of IAS 40, we find that mandatory adoption firms exhibit a larger decline in information asymmetry, as reflected in lower bid–ask spreads. However, we also find that mandatory adoption firms continue to have higher information asymmetry than voluntary adoption firms, which appears partially attributable to the lower reliability of fair values reported by the mandatory adoption firms. Together, this evidence adds to the debate on fair value accounting by demonstrating that common adoption of fair value, even for long-lived tangible assets, under a mandatory reporting regime can reduce, but not necessarily eliminate, information asymmetry differences across firms. This paper was accepted by Stefan Reichelstein, accounting.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".