Accounting Conservatism, the Sarbanes‐Oxley Act, and Crash Risk
Bibliographic record
Abstract
We examine how accounting conservatism at the firm level and the Sarbanes-Oxley Act (SOX) influence idiosyncratic stock crash risk. We document that firms that are more conservative in reporting their earnings are less prone to stock price crash, consistent with the finding of Jin and Myers (2006) that firms disclosing bad news in a timelier manner are less likely to deliver large negative stock returns. We also find strong evidence that idiosyncratic crash risk has decreased significantly in the post-SOX period, supporting the argument that SOX has led to less withholding of bad news and has improved disclosure and transparency. Moreover, the impact of SOX on crash risk varies systematically with the extent that firms withheld bad news in the pre-SOX period, showing an inverted U-shaped pattern. However, the impact of accounting conservatism on stock crash propensity has been mitigated since SOX was enacted, suggesting that firms are pressured to reveal bad news more through a variety of channels (e.g., management earnings forecasts and press conferences). This is probably due to the high litigation risk and severe penalties imposed by SOX.
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 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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".