Insider Trading Restrictions and Insiders’ Supply of Information: Evidence from Earnings Smoothing
Bibliographic record
Abstract
ABSTRACT We exploit the setting of first‐time enforcement of insider trading laws to investigate the relationship between insider trading opportunities and insiders’ supply of information. Insider trading opportunities motivate insiders to reduce their supply of information by concealing firm performance, thereby increasing their information advantage over outsiders, resulting in higher insider trading profits. Using data from 40 countries over the 1988–2004 period, we find that reporting opacity, as captured by earnings smoothness, decreases significantly after the initial enforcement of insider trading laws in countries with strong legal institutions. The decrease in earnings smoothness is positively related to the strictness of insider trading laws. The decrease in earnings smoothness is also more pronounced for countries that have more persistent insider trading law enforcement and for countries that impose more severe penalties on insider trading cases. Further analyses show that the decrease in earnings smoothness following insider trading enforcement is concentrated among firms that are not closely held and among high‐growth firms. In addition to uncovering a channel through which insider trading restrictions affect the information environment, our evidence highlights the importance of country‐ and firm‐level governance structures in determining the consequences of insider trading restrictions.
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How this classification was reachedexpand
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 | 0.002 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.004 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".