Financial Reporting Opacity and Expected Crash Risk: Evidence from Implied Volatility Smirks
Bibliographic record
Abstract
The recent financial crisis has stimulated a renewed interest in understanding the determinants of stock price crash risk (i.e., left tail risk). Recent research shows that opaque financial reports enable managers to hide and accumulate bad news for extended periods. When the accumulated bad news reaches a certain tipping point, it will be suddenly released to the market at once, resulting in an abrupt decline in stock price (i.e., a crash). This study extends this line of research by examining the impact of financial reporting opacity on perceived or expected crash risk. Prominent economists, such as Olivier Blanchard, argue that removing the perception of tail risks (in addition to realized tail risks) is crucial in restoring investor confidence and stabilizing the stock market. Using the steepness of option implied volatility skew as a proxy for perceived crash risk, we find that accrual management, the presence of financial statement restatements, and auditor‐attested internal control weakness are all positively and significantly associated with the level of perceived crash risk. Our results suggest that improving financial reporting transparency is an important mechanism for firms and policymakers to reduce the perception of tail risks and stabilize the stock market.
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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 | 0.005 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".