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Record W2122985769 · doi:10.1002/smj.2009

Acquisition announcements and stock market valuations of acquiring firms' rivals: A test of the growth probability hypothesis in China

2012· article· en· W2122985769 on OpenAlexaff
Ajai Gaur, Shavin Malhotra, Peng Zhu

Bibliographic record

VenueStrategic Management Journal · 2012
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsStock marketChinaStock (firearms)BusinessTest (biology)Financial economicsMonetary economicsEconomicsEconometrics

Abstract

fetched live from OpenAlex

Abstract To examine the impact of acquisition announcements on the stock market returns of rivals of the acquiring firms, we propose a growth probability hypothesis: when an acquisition is announced, it signals the potential for future growth in the acquirer's industry to the market, resulting in positive stock market reactions to rivals of the acquiring firms. We test the growth probability hypothesis with a longitudinal sample of Chinese domestic and cross‐border acquisitions during 1993–2008. The results provide robust support for this hypothesis as a means to explain market reactions to rivals of acquiring firms. We also empirically test and negate alternative theoretical explanations advanced in prior literature to explain positive market reactions to rivals of the target firms. Copyright © 2012 John Wiley & Sons, Ltd.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.039
GPT teacher head0.232
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2012
Admission routes1
Has abstractyes

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