The merits of securities litigation and corporate reputation
Bibliographic record
Abstract
Abstract We explore how securities litigation affects corporate reputation. Experts remain concerned that nonmeritorious securities class actions—those that will be dismissed or settled for nuisance amounts—cause reputational damage. Although several prior studies show reputational costs for nonmeritorious cases, they generally use indirect measures based on returns or total market losses, which are mechanically associated with securities litigation elements. In contrast, we use a relatively direct reputation measure from Fortune 's “Most Admired Companies” list. We find significant reputational damage after meritorious litigation, with the strongest cases having the largest effects. However, we find no evidence of reputational damage after nonmeritorious litigation. We also find that Fortune 's reputational damage measure is associated with more negative returns around the litigation filing date. We show possible mechanisms for our results, as initial legal filings contain information allowing market participants to assess case merits. Our results imply that reputational damage is primarily due to fraud, which securities litigation helps reveal to the market, rather than litigation itself. Thus, reputational damage is not an issue in over 70% of securities class actions due to the high frequency of nonmeritorious cases.
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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 | 0.004 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".