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Record W1974951060 · doi:10.5430/ijfr.v4n2p49

Sources of Synergy Realization in Mergers and Acquisitions: Empirical Evidence from Non-Serial Acquirers in Europe

2013· article· en· W1974951060 on OpenAlexvenueno aff
Nancy Huyghebaert, Mathieu Luypaert

Bibliographic record

VenueInternational Journal of Financial Research · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
FundersUniversiteit Gent
KeywordsLeverage (statistics)Mergers and acquisitionsBusinessMonetary economicsRevenueShareholderSample (material)Debt ratioOperating leverageRealization (probability)Operating marginEarnings before interest and taxesDebtEconometricsEconomicsReturn on assetsAccountingFinanceProfitability indexStatisticsCorporate governance

Abstract

fetched live from OpenAlex

We empirically investigate the sources, magnitude, and timing of synergy realization for 293 M&As by non-serial listed acquirers in Europe during 1997–2005. In contrast to much of the existing literature, we find that the shareholders of non-serial acquirers gain significantly upon deal announcement. Next, we unravel the various sources of M&A value creation, in particular operating synergies resulting either from revenue enhancement or from savings on operating costs and investments, and financial synergies. Compared to its non-combining industry peers, the median combined sample firm reports a 4.92% larger sales growth rate by the third post-deal year. Operating costs relative to sales are reduced by an extra 1.53% over this same window. In leverage-increasing acquisitions, the median combined firm realizes a persistent 6.09% rise in its long-term debt ratio. Finally, our multivariate regression results point out that non-serial acquirers with a larger market-to-book ratio achieve more extensive operating synergies.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation 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 score0.044
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.080
GPT teacher head0.346
Teacher spread0.266 · 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 teacher head, 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

Citations10
Published2013
Admission routes1
Has abstractyes

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