The acquisition puzzle and mispricing: evidence of over-optimism
Bibliographic record
Abstract
Purpose – Researchers suspect that the overvaluation of equity issuing acquirers is a major cause of their subsequent post-event underperformance. Definitive conclusions regarding this overpricing hypothesis have not been possible since indicators of overpricing such as the book-to-market ratio and subsequent underperformance are open to alternative interpretations. The purpose of this paper is to corroborate or refute overvaluation as a driver of equity issuing acquirers’ subsequent underperformance. Design/methodology/approach – The literature has linked overvaluation of acquirers to over-optimistic expectations. The authors use analysts’ earnings forecasts to reflect the market's expectations. Over-optimism is indicated by subsequent earnings disappointments. The authors examine the relation between acquirers’ choice of payment method and their tendency to report disappointing earnings. The authors also examine the effect of including a more direct measure of over-optimism in a model to explain the long-run post-event buy-and-hold-abnormal returns of acquirers. Findings – The post-acquisition earnings of equity issuing acquirers disappoint more often than those of acquirers employing alternative financing methods. This relationship is confined to glamour acquirers. The ability of financing method to predict long-run post-acquisition performance is subsumed when direct measures of optimism are included in a model explaining long-run post-acquisition performance. This result is robust to controls for overpayment and other potential explanations of post-acquisition underperformance. Research limitations/implications – Acquirers’ management exploit their information advantage to exchange overvalued equity for the assets of the target company in accordance with Loughran and Ritter's (2000) behavioural timing hypothesis. Originality/value – The study provides new and unambiguous evidence that equity-issuing acquirers are optimistically priced at the time of acquisition.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".