Abstract
Cross‐shareholding is a new type of strategic means for capital operation and is an important component of corporate governance. With the increasing complexity of business motivation, the structure of a cross‐shareholding network (CSN) is becoming more intricate, and it exposes various important local patterns with different economic functions. The goal of this paper is to uncover investment mechanisms and economic functions implied in cross‐shareholding networks (CSNs) by analyzing the local characteristic patterns of company interactions. In this paper, we construct the CSNs of listed companies and extract the directed triadic motifs to reveal the evolutionary characteristics of local investment patterns at the company and industry levels. On the company level, we find that companies tend to form V‐shaped structures with other companies, but bidirectional shareholding patterns and circular relationships in the triads are scarce. On the industry level, we identify the characteristic linking patterns of some industries with a role analysis of the industries. Furthermore, we detect the evolutionary characteristics of industry interrelationships in three implied patterns. Such a motif evolution analysis may provide valuable information for investors and supervisory departments that make decisions about investment portfolios and policy. Meanwhile, this study is also helpful for exploring the implied information in other empirical networks.
Highlights
To explore the interactive behaviors among economic agents, many economic systems have been modeled and better understand as complex networks [1,2,3,4,5], crossshareholding networks (CSNs), which are specific architectures that can reflect the risk-resistant capability of corporate and capital markets [6, 7]
We explore the local implied information in CSNs, which is revealed through a network motif analysis
Thereby, we demonstrate that the anatomy of the local interaction patterns of companies and industries is a promising method to uncover the function and structure of CSNs and other complex networks
Summary
To explore the interactive behaviors among economic agents, many economic systems have been modeled and better understand as complex networks [1,2,3,4,5], crossshareholding networks (CSNs), which are specific architectures that can reflect the risk-resistant capability of corporate and capital markets [6, 7]. With the increasing complexity of business motivations (such as capital financing, spread risk, and industrial alliances), the structure of CSNs is becoming much more intricate and exposes various important local patterns with different economic functions. Detecting the local interactive patterns among companies (or industries) and unveiling the implied information contained in these patterns are of crucial importance in understanding the complex crossshareholding behaviors more precisely and in providing investment advice and risk warnings for enterprises and market regulators. We explore the local implied information in CSNs, which is revealed through a network motif analysis
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