Adverse Selection in Acquisitions of Small Manufacturing Firms: A Comparison of Private and Public Targets
Bibliographic record
Abstract
Acquisitions of small (less than 500 employees)manufacturing firms are analyzed in this study, with an emphasis onunderstanding the differences between acquisitions of public firms as comparedto acquisitions of private firms. To begin, a literature review is provided topresent theoretical arguments and research hypotheses. Data used in the study were obtained from 923 acquisitions that occurredbetween 1996 and 1999 in the manufacturing industry as captured by theSecurities Data Corporate (SDC) database.Of the 923 acquisitionsconsidered, 457 involved public targets. The statistical results indicate thatwhen private firms are acquired, they tend to be more mature firms in terms ofage. This results from the acquirer's desire to lessen his risk of adverseselection. Acquirers are also more willing to acquire private firms withintheir own industry or core business. Small firms that have gone public experience less difficulty in valuationthan their private counterparts, as they have revealed more information in acreditable environment. Private firms that wish to lessen the uncertainty ofthe value of their intangible assets should consider using collaborationagreements that reduce information asymmetry. (SRD)
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".