Evasive shareholder meetings, meeting announcement lag, and stock price crash risk
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We investigate the relationship between evasive shareholder meetings and stock price crash risk. Using hand-collected data on annual shareholder meeting scheduling characteristics for 9,086 meetings held by 1,486 public U.S. firms between 2012 and 2020, we fail to find evidence consistent with managers deterring shareholder and stakeholder attendance at meetings to hoard bad news (i.e., our deterrence hypothesis ). Nonetheless, we initially find a puzzling negative relationship between evasive timing strategies and stock price crash risk. However, in robustness checks, this effect virtually disappears. We also find no evidence that firms are strategically announce meetings closer to the annual meeting dates to withhold bad news from investors. To alleviate potential self-selection bias, we employ an entropy balancing approach. Collectively, we find no evidence that evasive shareholder meetings (distance-based or timing-based) affect future stock price crash risk.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it