Bogging down investors: An unintended consequence of litigation risk
Bibliographic record
Abstract
Abstract Securities litigation risk is a well‐recognized yet underexplored source of financial reporting complexity or unreadability. This study examines the effect of litigation risk on the readability of corporate financial reports. The 1999 Silicon Graphics Inc. (SGI) court ruling unexpectedly reduced litigation risk for firms within the Ninth Circuit Court's jurisdiction. Using a difference‐in‐differences design centered on the SGI court ruling, we find that, while the readability of financial reports generally declines over the sample period, treated firms in the Ninth Circuit experience a comparatively smaller decline in readability than control firms in other states after the ruling. Put differently, treated firms experience a relative improvement in reporting readability following the ruling. This effect is concentrated among firms prone to securities litigation and those with greater external financing needs, but it is muted for firms engaging in earnings management. Furthermore, improved reporting readability among treated firms can be partially attributed to alleviated concerns about the adequacy of cautionary language, as evidenced by a significant decrease in negative forward‐looking statements, particularly risk‐related ones. Collectively, our findings suggest that securities litigation risk contributes to reduced readability in financial reporting.
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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 | 0.086 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".