Bibliographic record
Abstract
Abstract This research explores evidence of corporate capabilities for conducting acquisition and alliance deals in young firms. We hypothesize that investors conjecture about the future based on information about a firm's capabilities. Each successive deal carries intrinsic value, creates experience, generates feedback, and yields information about the firm's underlying capabilities. We evaluate whether stock prices impute expectations that firms will capably pursue particular programs of acquisitions and alliances. The analysis covers how investor responses change across successive deals on the theory that firms with a concentrated program of deals may develop capabilities more intensively than those with programs that involve both acquisitions and alliances. The dataset covers the population of firms that went through an initial public offering (IPO) in the United States between 1988 and 1999. It contains information on all of their post‐IPO acquisitions and alliances, and on how their stock prices changed in response to the announcement of each deal. The results suggest that within the first year after IPO, investors expect firms to execute particular streams of alliances and acquisitions that reflect their unique histories of demonstrated capabilities. We also find evidence that investors cannot fully anticipate deal programs. The findings support a capabilities‐based view of the firm and also show that accurate inference using event‐study methods may require digging deep into the early histories of firms. Copyright © 2009 John Wiley & Sons, Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".