Spatio-Temporal Patterns of the International Merger and Acquisition Network.
Spatio-Temporal Patterns of the International Merger and Acquisition Network.
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DOI：
10.1038/s41598-017-10779-z
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发表时间：
2017-09-07
影响因子：
4.6
通讯作者：
Fagiolo G
中科院分区：
文献类型：
--
作者：
Dueñas M;Mastrandrea R;Barigozzi M;Fagiolo G
本文采用复杂网络方法对世界并购网络进行了分析。我们使用并购数据构建了1995-2010年期间二元和加权定向网络的时间序列，其中224个国家（节点）根据其并购流量（链接）连接起来。我们研究了国际并购网络（IMAN）的不同地理和时间方面，构建了链路属于特定距离或时间间隔的过滤子网络序列。鉴于并购和贸易是进入国外市场的互补方式，我们使用用于研究国际贸易网络（ITN）的统计数据进行分析，突出国际贸易网络与国际贸易网络之间的异同。与ITN相比，IMAN是一个低密度网络，其特征是具有许多外部节点和低互易性的持久巨型组件。集群模式是非常异构和动态的。高收入经济体是主要的收购方，其特点是高连通性，这意味着大多数国家都是少数收购方的目标。与ITN相似，地理距离对IMAN的结构有很大影响：链路权重和节点度与距离呈非线性关系，在短距离处呈现分类模式。
This paper analyses 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 (nodes) connected according to their M&As flows (links). We study different geographical and temporal aspects of the international M&A 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 have a non-linear relation with distance, and an assortative pattern is present at short distances.
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DOI：
10.1016/s0378-4371(03)00567-3
发表时间：
2003-10-01
影响因子：
3.3
作者：
Li, X;Jin, YY;Chen, GR
通讯作者：
Chen, GR
影响因子：
1
作者：
Qiu, Larry D.;Wang, Shengzu
通讯作者：
Wang, Shengzu
影响因子：
3.3
作者：
di Giovanni, J
通讯作者：
di Giovanni, J
影响因子：
8.6
作者：
Garlaschelli, D;Loffredo, MI
通讯作者：
Loffredo, MI
影响因子：
8.6
作者：
Garlaschelli, D;Loffredo, MI
通讯作者：
Loffredo, MI