Enforcement Waves and Spillovers
Bibliographic record
Abstract
We document that regulatory enforcement actions for financial misrepresentation cluster in industry-specific waves and that wave-related enforcement has information spillovers on industry peer firms. Waves and spillovers have significant effects on share prices. Early-wave target firms have the largest short-run losses in share values and the largest information spillovers on industry peer firms. Late-wave targets’ short-run losses are smaller, but not because they involve less costly instances of misconduct. Rather, late-wave targets are subject to more information spillovers from earlier in the wave. These results indicate that prices incorporate changes in the likelihood that a firm will face wave-related enforcement action for financial misconduct. Short-window share-price losses understate the total share-price impact, particularly for firms whose financial misrepresentation is revealed late in an enforcement wave. This paper was accepted by David Simchi-Levi, finance. Supplemental Material: The internet appendix and data are available at https://doi.org/10.1287/mnsc.2023.4711 .
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.029 | 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".