Corporate Tax Aggressiveness and Insider Trading
Bibliographic record
Abstract
ABSTRACT We examine the association between corporate tax aggressiveness and the profitability of insider trading under the assumption that insider trading profits reflect managerial opportunism. We document that insider purchase profitability, but not sales profitability, is significantly higher on average in more tax aggressive firms. We also find that the positive association between tax aggressiveness and insider purchase profitability is attenuated for firms with more effective monitoring and is accentuated for firms with a more opaque information environment. In addition, we provide empirical evidence that tax aggressiveness is significantly associated with greater insider sales volume in the fiscal year prior to a stock price crash. Finally, we find that the association between tax aggressiveness and insider purchase profitability weakens after the introduction of FIN 48, consistent with the increased transparency of tax positions under the new disclosure requirement reducing insiders' information advantage and hence their ability to profit from insider trading. To the extent that insider trading profits reflect managerial opportunism, our results are consistent with managers exploiting the opacity arising from tax aggressive activities to extract rent from shareholders, particularly those shareholders who sold their shares to the managers. Our findings are particularly important in light of the number of studies relying on the agency view of tax avoidance to develop arguments or to draw inferences.
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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 | 0.001 | 0.008 |
| 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) | 0.002 | 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".