Bibliographic record
Abstract
Research summary: I examine how acquisition motives relate to the distribution of post‐acquisition performance. I argue that acquisitions motivated by operating synergies have the potential to experience greater gains than acquisitions driven by financial synergies but are harder to value and implement, making them more uncertain. Using SEC filings, conference calls and press releases to capture acquisition motives, I find that acquirers pursuing operating synergies are more likely to experience highly positive and highly negative long‐term returns than acquirers pursuing financial synergies. I also find that acquisition experience and geographic proximity to targets soften acquirers' extreme downside outcomes in operating synergy acquisitions. My theory and results suggest that approaches that emphasize average outcomes for acquirers and use industry classifications to capture acquisition motives may be incomplete . Managerial summary: Managers engage in acquisitions for various reasons. In this study, I find that reasons related to operating synergies (e.g., revenue growth through new product offerings or cost savings through economies of scale) are more likely to result in extreme high and low performance outcomes for the acquiring firm compared to reasons related to financial synergies (e.g., diversification of cash flow streams). In addition, I find that the acquirer's prior acquisition experience and the geographic proximity between the target and acquirer help soften the extreme low performance outcomes related to operating synergies . Copyright © 2017 John Wiley & Sons, Ltd.
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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 | 0.015 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".