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Record W2347636307

Empirical Analysis of Fraudulent Financial Affairs of Listed Companies

2010· article· en· W2347636307 on OpenAlexaboutno aff
Furong Zhang

Bibliographic record

VenueJournal of Tianjin University of Commerce · 2010
Typearticle
Languageen
FieldMathematics
TopicBenford’s Law and Fraud Detection
Canadian institutionsnot available
Fundersnot available
KeywordsCommissionQuarter (Canadian coin)AccountingSample (material)BusinessAuditChinaFinanceListed companyEmpirical researchBenford's lawFinancial ratioProfit (economics)EconomicsLawPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Based on Benford's Law,the paper takes listed companies punished by China Securities Regulatory Commission because of fraudulent financial affairs as sample,pairs each sample company,has an empirical test of financial data of sample company and paired company,and draws a conclusion that listed companies with fraudulent financial affairs are likely to make up profit sheets and the data of the fourth quarter is lowest in authenticity.Therefore,it is necessary to strengthen supervision and auditing of profit sheets and the data of the fourth quarter to reduce the possibility of fraudulent financial affairs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.040
GPT teacher head0.289
Teacher spread0.250 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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