How Country Reputation Differentials Influence Market Reaction to International Acquisitions
Bibliographic record
Abstract
Abstract This study investigates the influence of country reputation differentials on market reaction to international acquisitions. Building on social identity theory, we argue that market reactions are more positive when the reputation of the acquirer’s country is better than that of the target’s, since a country’s reputation imprints on its firms due to social categorization. Thus, firms from countries with better reputations are perceived as having superior skills/capabilities and the reputation difference suggests that the acquirer is able to identify/exploit undervalued targets and leverage synergies. The influence of country reputation should be weaker when additional information on the merging firms is present, through news media reporting and analyst coverage of the merging firms as well as the acquirer’s prior acquisition track record, since market investors will rely less on country reputation. We find support for our hypotheses for a sample of 4,792 international acquisitions across 48 countries from 2009 to 2017.
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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 | 0.000 | 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 | 0.002 |
| 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".