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An analysis of the impact of economic context of selected determinants of cross-border mergers and acquisitions in the EU

Author

Listed:
  • Robert Stefko
  • Jaroslava Heckova
  • Beata Gavurova
  • Tomas Valentiny
  • Alexandra Chapcakova
  • Dagmara Ratnayake Kascakova

Abstract

Cross-border mergers and acquisitions represent a significant global phenomenon that allows businesses to generate business synergies, procure assets, generate tax optimisations, gain access to new technologies and markets, increase competitiveness and market value and differentiate and diversify business activities. In this study, we analyse the impact of economic determinants which influence the year to year increase in the average volume of cross-border mergers and acquisitions (M&A) directed from the source country to the target country. We run binary logistic regression on the data which contains observation of selected indicators in EU countries (including the UK) within 1998–2015. Our data have nature of panel data. We study impact of selected indicators on the year to year increase of merges and acquisitions in the European Union. We document that market capitalisation growth has a positive influence on the year-on-year increase in the volume of M&A. We also provide proof that changes in relative distance between source and target country affects the volume of M&A and that existence of a common border between the source and target country induce an increase in M&A. Our study contributes to better understanding of the cross-border mergers and acquisitions phenomena and is complementary to already conducted research.

Suggested Citation

  • Robert Stefko & Jaroslava Heckova & Beata Gavurova & Tomas Valentiny & Alexandra Chapcakova & Dagmara Ratnayake Kascakova, 2022. "An analysis of the impact of economic context of selected determinants of cross-border mergers and acquisitions in the EU," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 6385-6402, December.
    • Handle: RePEc:taf:reroxx:v:35:y:2022:i:1:p:6385-6402
    DOI: 10.1080/1331677X.2022.2048200
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    Cited by:

    1. Simona Hašková & Petr Šuleř & Róbert Kuchár, 2023. "A Fuzzy Multi-Criteria Evaluation System for Share Price Prediction: A Tesla Case Study," Mathematics, MDPI, vol. 11(13), pages 1-17, July.

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