Acquisitions of private vs. public firms: Private information, target selection, and acquirer returns
Bibliographic record
Abstract
Abstract The acquisition of privately held firms is a prevalent phenomenon that has received little attention in mergers and acquisitions research. In this study, we examine three questions: (1) What drives the acquirer's choice between public and private targets? (2) Do acquisitions of private targets elicit a more positive stock market reaction than acquisitions of public targets, which, on average, destroy value for acquirers' shareholders? (3) Do acquirers gain when their selection of a public or private target fits the theory? In this paper, we argue that the lack of information on private targets limits the breadth of the acquirer's search and increases its risk of not evaluating properly the assets of private targets. At the same time, less information on private targets creates more value‐creating opportunities for exploiting private information, whereas the market of corporate control for public targets already serves as an information‐processing and asset valuation mechanism for all potential bidders. Using an event study and survey data, we find that: (1) acquirers favor private targets in familiar industries and turn to public targets to enter new business domains or industries with a high level of intangible assets; (2) acquirers of private targets perform better than acquirers of public targets on merger announcement, after controlling for endogeneity bias; (3) acquirers of private firms perform better than if they had acquired a public firm, and acquirers of public firms perform better than if they had acquired a private firm. These results support the expectation that acquirer returns from their target choice (private/public) are not universal but depend on the acquirer's type of search and on the merging firms' attributes. Copyright © 2007 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 | 0.020 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".