Disclosure under heightened legal accountability: evidence from the Sarbanes–Oxley act
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".