The Importance of IRS Enforcement to Stock Price Crash Risk: The Role of CEO Power and Incentives
Bibliographic record
Abstract
ABSTRACT We analyze whether tough IRS monitoring generates a positive externality by constraining managers' bad news hoarding activities. Supporting this prediction, we find a negative relation between the threat of an IRS audit and stock price crash risk. Our evidence is consistent with recent theory that outside investors learn more about firms when tax enforcement is stricter. Additionally, path analysis suggests that the monitoring channel (direct path) plays a critical role in shaping crash risk relative to information asymmetry channels of tax planning and accruals manipulation (indirect paths). Consistent with other predictions, we find that the monitoring role of IRS audits intensifies when firms experience worse agency conflicts stemming from CEO power and incentives. Collectively, our research implies that external monitoring by tax authorities protects shareholders against managers suppressing negative firm-specific information that engenders stock price crash risk, particularly when CEOs have wider scope and stronger incentives to hoard bad news.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".