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Record W2959003265 · doi:10.5430/rwe.v10n2p15

Studies on Earnings Management and Financial Statement Fraud in Corporate Firms

2019· article· en· W2959003265 on OpenAlexvenueno aff
Noorul Azwin Md Nasir, Muhammad Jahangir Ali, Noorshella Che Nawi

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsnot available
Fundersnot available
KeywordsEarnings managementFinancial statementAccountingBusinessEarningsHedgeFinancial statement analysisAccounting recordsEarnings response coefficientStatement (logic)Statement of changes in financial positionAccounting managementFinanceActuarial scienceFinancial ratioAccounting information systemPolitical scienceLawAudit

Abstract

fetched live from OpenAlex

The purpose of this paper is to explain the transformation of earnings management into financial statement fraud. Related papers on earnings management and financial statement fraud were intensively reviewed. Although there are some overlaps in both concepts, application of earnings management is not always related to financial statement fraud. Earnings management can occur for a number of reasons, including unintentional errors and legitimate disagreement over GAAP. Results of this study indicate that accounting records are allowed to be managed up to certain hedge. Crossing the hedge would lead the organization to be convicted in illegal financial reporting.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.635
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.060
GPT teacher head0.304
Teacher spread0.244 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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