Targets' Accounting Conservatism and the Gains from Acquisition*
Bibliographic record
Abstract
ABSTRACT We present evidence on the effects of target firms' accounting conservatism in a merger and acquisition transaction. Conservatism is distinct from other accounting or accrual quality constructs examined in prior work. Its unique features can lead to potential benefits for both the targets and the acquirers. The use of conservatism by targets reduces acquirers' risks of acquiring underperforming assets or overpaying for well‐performing assets. In addition, targets' conservatism results in greater production of verifiable information that can help the acquirers better estimate and realize synergies of the combined firm. Consistent with these arguments, we find that firms with greater accounting conservatism are more likely to receive a bid. We also find that targets' conservatism increases the deal premium and the announcement returns of both the targets and the acquirers, respectively. Overall, these results indicate that conservatism provides benefits to both sellers and buyers of equity in an acquisition transaction.
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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 | 0.003 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".