Are Acquirers Efficiently Priced? Evidence from Subsequent Earnings Announcements
Bibliographic record
Abstract
We adopt a model-free measure of long-run abnormal returns, the subsequent earnings announcement-period abnormal returns, to examine the price efficiency of acquirer stocks involved in mergers and acquisitions. We find strong evidence of both overreaction and underreaction. First, the market underreacts to acquirer valuation information. Overvalued acquirers earn lower returns at the announcement period as well as during the long-run period following the announcement. Second, in deals involving public targets, the market underreacts to stock payment information, as both announcement-period abnormal returns and long-run returns are lower when stock is used to pay for acquiring public targets; in private deals, the market overreacts to stock payment information, as announcement-period abnormal returns are higher if stock is paid for private targets but the long-run returns are significantly lower. There is also evidence that the market incorporates information regarding asset relatedness mostly over the longer term. The overall evidence suggests that acquirers are not efficiently priced at the announcement period.
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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 | 0.015 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".