Executives' Legal Records and the Deterrent Effect of Corporate Governance
Bibliographic record
Abstract
ABSTRACT We study whether the effectiveness of corporate governance mechanisms varies depending on the characteristics of the executives subject to these mechanisms, namely their “psychological type,” as proxied by their history of legal infractions. In particular, we examine insider trading, where we can compare the trading behavior of different types of executives in the same firm. We find that “recordholder” executives, that is, those with prior legal infractions, earn significantly higher profits from purchases and sales than nonrecordholder executives. Furthermore, the profitability of both purchases and sales is significantly increasing in the severity of the infraction. Governance mechanisms, such as blackout policies, lower profits of executives with only traffic infractions; however, profits for executives with serious infractions appear insensitive to blackout policies. Insiders with serious infractions are also more likely to trade during blackout periods and before large information events and are more likely to report their trades to the SEC after the filing deadline. Collectively, our evidence suggests that while governance mechanisms can discipline executives with minor offenses, they appear largely ineffective for those with more serious infractions.
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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 | 0.003 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".