Arbeitspapier

Spatio-temporal patterns of the international merger and acquisition network

This paper analyzes the world web of mergers and acquisitions (M&As) using a complex network approach. We use data of M&As to build a temporal sequence of binary and weighted-directed networks, for the period 1995-2010 and 224 countries. We study different geographical and temporal aspects of the international M&As network (IMAN), building sequences of filtered sub-networks whose links belong to specific intervals of distance or time. Given that M&As and trade are complementary ways of reaching foreign markets, we perform our analysis using statistics employed for the study of the international trade network (ITN), highlighting the similarities and differences between the ITN and the IMAN. In contrast to the ITN, the IMAN is a low density network characterized by a persistent giant component with many external nodes and low reciprocity. Clustering patterns are very heterogeneous and dynamic. High-income economies are the main acquirers and are characterized by high connectivity, implying that most countries are targets of a few acquirers. Like in the ITN, geographical distance strongly impacts the structure of the IMAN: link-weights and node degrees are strongly non-linear, and an assortative pattern is present at short distances.

Sprache
Englisch

Erschienen in
Series: LEM Working Paper Series ; No. 2017/13

Klassifikation
Wirtschaft
Econometric and Statistical Methods: Special Topics: General
International Investment; Long-term Capital Movements
Macroeconomic Aspects of International Trade and Finance: General
Thema
International Economics
Mergers and Acquisitions
Network Analysis
Geographical Distance

Ereignis
Geistige Schöpfung
(wer)
Duenas, Marco
Mastrandrea, Rossana
Barigozzi, Matteo
Fagiolo, Giorgio
Ereignis
Veröffentlichung
(wer)
Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
(wo)
Pisa
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:43 MEZ

Datenpartner

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Objekttyp

  • Arbeitspapier

Beteiligte

  • Duenas, Marco
  • Mastrandrea, Rossana
  • Barigozzi, Matteo
  • Fagiolo, Giorgio
  • Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)

Entstanden

  • 2017

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