Blockholder Exit Threats and Financial Reporting Quality
Bibliographic record
Abstract
Abstract Recent theoretical and empirical studies suggest that blockholders (shareholders with ownership ≥ 5 percent) exert governance through the threat of exit. Blockholders have strong incentives to gather private information and sell their shares when managers are perceived to underperform. To prevent blockholders from selling their shares and the firm from suffering a stock price decline, managers align their actions with the interests of shareholders. As a result of the greater manager‐shareholder alignment, managers' actions are more likely to be in shareholders' best interest, and consequently there is less need for managers to manipulate earnings. Consistent with these predictions from economic theory, we find evidence that as exit threat increases, firms have higher financial reporting quality. Theory also predicts that the impact of blockholders' exit threat on financial reporting quality (FRQ) should increase as the manager's wealth is tied more closely to the stock price, and this is what we find. Our study contributes to the research on the impact of shareholders on FRQ and to an emerging literature on the impact of blockholders in financial markets. Blockholders play an important role in managers' reporting outcomes through their actions as informed investors.
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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 | 0.009 | 0.076 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".