Hedge Fund Intervention and Accounting Conservatism
Bibliographic record
Abstract
Abstract Hedge fund intervention has been associated with many positive corporate changes and is an important vehicle for informed shareholder monitoring. Effective monitoring has also been positively associated with accounting conservatism. Building upon these prior results, we predict an increase in accounting conservatism after hedge fund intervention. We use a large sample of hedge fund activist events and identify control firms with similar likelihoods of being targeted using the propensity score matching method to apply difference‐in‐difference tests. We find that when hedge fund activists have relatively large ownership and sufficient time to exert their monitoring power, target firms experience significant increases in conditional conservatism. CFO turnovers, upward/lateral auditor switches, and improvements in audit committee independence after intervention are accompanied by greater increases in conditional conservatism. Finally, we find greater increases in conditional conservatism when there is a lack of monitoring by dedicated institutional investors before the intervention. Our study suggests that hedge fund activists improve accounting monitoring tools and thus adds important new evidence on the effectiveness of shareholder monitoring on accounting practices.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".