How do hedge fund activists use and affect financial reporting of income taxes? Evidence from the valuation allowance for deferred tax assets
Bibliographic record
Abstract
Abstract This study uses valuation allowances (VAs) for deferred tax assets to examine whether hedge fund activists (HFAs) use and affect financial reporting of income taxes. Specifically, we investigate whether HFAs target firms with VAs and whether target firms are more likely to release VAs post‐intervention. We find that the existence, magnitude, and increases in VAs increase the marginal probability that HFAs will target a firm by between 12% and 24%. We also find that target firms are 4.6% more likely to release VAs following the intervention, and this effect persists for up to 2 years. Releases of VAs appear to stem from implemented tax avoidance strategies and changes in financial reporting of income taxes rather than real changes in operating performance or earnings management. Overall, HFAs appear to understand the interplay between tax planning and financial reporting of income taxes and use both to unlock value in target firms.
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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 | 0.016 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".