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Record W2904091937 · doi:10.1111/jbfa.12363

Material weakness disclosures and restatement announcements: The joint and order effects

2018· article· en· W2904091937 on OpenAlexaff
He Li, Bharat Sarath, Nader Wans

Bibliographic record

VenueJournal of Business Finance &amp Accounting · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsWrongdoingClass actionLawsuitBusinessPlaintiffBankruptcyAccountingLitigation risk analysisArgument (complex analysis)Securities fraudStock priceActuarial scienceMonetary economicsEconomicsFinanceAuditLawPolitical scienceSupreme court

Abstract

fetched live from OpenAlex

Abstract We examine differences in stock price, option volatility, and litigation reactions to restatement announcements that are associated with a material weakness (MW) disclosure. Contrasted with restatements that are not associated with any MW disclosure, our analyses reveal that firms that announce both a restatement and an associated MW experience significantly more negative market returns, greater implied volatility, and higher likelihood of class action lawsuits. Separating the restatements into timely reporters , where the MW precedes the restatement, and non‐timely reporters , where the MW is concurrent with or follows the restatement, we find that timely reporters experience more negative returns at the time of the restatement, relative to non‐timely reporters, suggesting that investors perceive the early MW disclosure to signal more pervasive control‐related problems. Interestingly, we find that timely and non‐timely reporters are equally likely to be sued, consistent with the argument that wrongdoing (through either a timely or non‐timely MW disclosure) provides stronger grounds for establishing scienter. However, timely reporters appear to secure more favorable litigation outcomes: they face higher likelihood of lawsuit dismissals and pay much lower settlements, compared to non‐timely reporters. Overall, our evidence provides new insights into how market participants incorporate information about internal control weaknesses into their perceptions regarding the economic implications of financial restatements, and financial reporting quality.

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)0.002
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.399
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.003
Open science0.0000.001
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 head0.010
GPT teacher head0.216
Teacher spread0.206 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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