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Record W2160990933 · doi:10.1287/mnsc.1110.1339

Mandatory Fair Value Accounting and Information Asymmetry: Evidence from the European Real Estate Industry

2011· article· en· W2160990933 on OpenAlexaff
Karl A. Muller, Edward J. Riedl, Thorsten Sellhorn

Bibliographic record

VenueManagement Science · 2011
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsInformation asymmetryBusinessFair valueReal estateAccountingValue (mathematics)Accounting information systemInvestment (military)Real estate investment trustMonetary economicsFinanceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.699
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.013
Open science0.0020.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.018
GPT teacher head0.212
Teacher spread0.194 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations49
Published2011
Admission routes1
Has abstractyes

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