The Effect of Section 404 of the Sarbanes-Oxley Act on Earnings Quality
Bibliographic record
Abstract
In this article, the authors study the effect of Section 404 of the Sarbanes-Oxley Act on two primary characteristics of earnings quality, reliability and relevance in combination. Using a difference-in-differences method, they find that firms that were required to comply with Section 404 during the first 2 years of its implementation improved the reliability of their reported earnings more than control firms that were not required to comply. The results also suggest that the regulation helped to reduce intentional misstatement, which may contribute to the improvement in earnings reliability. Furthermore, the improvement in reliability did not come at the expense of the relevance of earnings. On the contrary, the authors find a significantly larger improvement in the predictive power of current earnings over future earnings and future cash flows associated with complying firms. As earnings quality improved, investor confidence appears to be restored, in that they reacted more strongly to earnings surprises of complying firms than to those of the control firms in the post-404 period. The results of this study suggest that Section 404 helped to achieve the main goal of the Act: protecting investors and restoring their confidence in the stock market by improving the accuracy and reliability of corporate disclosure.
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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 | 0.013 | 0.081 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".