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Record W3186444549 · doi:10.1108/jfc-05-2021-0113

The dark side of blockholder control: evidence from financial statement fraud cases

2021· article· en· W3186444549 on OpenAlexaffabout
Nadia Smaïli, Paulina Arroyo, Faridath Antoinette Issa

Bibliographic record

VenueJournal of Financial Crime · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsUniversité de MontréalUniversité du Québec à Montréal
Fundersnot available
KeywordsShareholderAccountingFinancial statementBusinessCommissionFinancial statement analysisControl (management)FinanceCorporate governanceFinancial ratioEconomicsAudit

Abstract

fetched live from OpenAlex

Purpose The purpose of this study is to investigate whether large blockholders are associated with financial statement fraud at their companies. Although a substantial body of prior studies has focused on chief executive officers’ motivations to manipulate financial statements, the correlation between majority shareholders and financial statement fraud has received little attention. This paper aims to fill this gap by investigating whether the sample firms have controlling shareholders or executives (i.e. blockholders vs management) and whether financial statement fraud schemes, motivations and consequences differ between blockholder- and management-controlled firms. Design/methodology/approach Using a clinical approach, the authors Study 12 Canadian financial statement fraud cases uncovered by the Ontario Securities Commission between 1997 and 2020. Findings First, the authors find blockholder control in six cases. These findings infer that these large shareholders received private benefits at the expense of minority shareholders. The comparative analyzes suggest that fraudulent firms controlled by blockholders go bankrupt more often than those controlled by managers. The authors also find that improper disclosure is the most common fraud scheme in blockholder-controlled firms. Originality/value The authors conduct a deep analysis of financial statement fraud cases to examine the of blockholder control on the likelihood of financial statement fraud. This paper adds new insights to the research on financial crime by investigating whether large shareholders affect the probability of fraud and the extent to which they might do so.

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)0.001
metaresearch head score (Gemma)0.042
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.966

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.042
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.244
Teacher spread0.225 · 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 designNot applicable
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

Citations13
Published2021
Admission routes2
Has abstractyes

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