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Record W2040235110 · doi:10.5539/ijms.v6n5p161

Fair-Value Accounting’s Role in the Global Financial Crisis?: Lessons for the Future

2014· article· en· W2040235110 on OpenAlexvenueno aff
Najeb Masoud, Abdullah Daas

Bibliographic record

VenueInternational Journal of Marketing Studies · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsFinancial crisisEconomicsFair valueReflexivityValue (mathematics)Order (exchange)Relevance (law)Capital marketEmerging marketsAccountingFinanceMacroeconomicsPolitical scienceSociology

Abstract

fetched live from OpenAlex

This paper debates how fair-value accounting (FVA) that were deeply affected by the global financial crisis. Theglobal financial crisis started in advanced economies spreading to emerging markets and low-income countries.Thus, it has been affected in the middle of 2007 and into 2009, which have examined the role of FVA in thefinancial crisis. This paper is used the value-relevance of fair-value reported under FAS 157 that estimates assetsand liabilities in terms of a simple theoretical and empirical analysis literature framework. This empirical studyproposed is a global crisis that not a normal cyclical crisis of capitalism. Also, it requires a change in themanagement policy to be tackled with new regulatory frameworks for financial institutions in order to stimulateeconomic activities. In other words, FVA may have amplified the crisis. Future research is needed to meetup-to-date information regarding the nature of capital markets and financial institutions. This requires a newtheory of economics; for instance, a change from equilibrium theory to reflexivity theory which requires achange in the underlying model of the economic activity framework. Therefore, this study has concluded a newtheory of the change of equilibrium to reflexivity that led to develop the model in the framework of the economicactivity.

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)0.010
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.518
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.290
Teacher spread0.271 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2014
Admission routes1
Has abstractyes

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