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Record W4414787100 · doi:10.1057/s41310-025-00321-3

Disclosure under heightened legal accountability: evidence from the Sarbanes–Oxley act

2025· article· en· W4414787100 on OpenAlexafffund
Chen Zhu, Changjie Hu, Ming Liu

Bibliographic record

VenueInternational Journal of Disclosure and Governance · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAuditing, Earnings Management, Governance
Canadian institutionsSaint Mary's University
FundersMcGill University
KeywordsCorporate governanceAccountabilityLegislatureTone (literature)Capital marketEvent studyCorporate financeInterpretation (philosophy)

Abstract

fetched live from OpenAlex

Abstract This paper examines managerial disclosure behavior in mandatory filings in response to heightened legal accountability. We treat the enactment of the Sarbanes–Oxley Act (SOX) in 2002 as a regulatory event that increases the legal accountability of top executives and analyze the filing tones of a large sample of Forms 10-Q and 10-K from 1994 to 2017 using textual analysis. We find that changes in filing tones carry substantial information that is promptly reflected in the capital market. Furthermore, we identify a structural break in the distribution of filing tones around the passage of SOX. Firms tend to adopt a more negative tone in their quarterly mandatory disclosures following the enactment of SOX. Interestingly, investors exhibit a stronger reaction to changes in filing tone in the post-SOX era, which is not solely driven by the systematic shift in tone distribution. Additionally, we document that while filing tones are generally associated with common performance measures, this relationship weakens after SOX. These findings contribute to the understanding of how legal reforms shape managerial communication strategies and how market participants interpret soft information under regulatory scrutiny. By shedding light on disclosure behavior in response to increased legal accountability, this study offers timely implications for future legislative reforms, investor interpretation of corporate filings, and the broader governance of 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)0.001
metaresearch head score (Gemma)0.004
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 score0.251
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.003
Open science0.0010.001
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 head0.011
GPT teacher head0.250
Teacher spread0.238 · 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
Published2025
Admission routes2
Has abstractyes

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