运筹与管理 ›› 2025, Vol. 34 ›› Issue (3): 149-154.DOI: 10.12005/orms.2025.0089

• 应用研究 • 上一篇    下一篇

行情波动下基金共持网络的小世界和无标度性及演化特征分析

郭小平1,2, 王建伟1   

  1. 1.东北大学 工商管理学院,辽宁 沈阳 110169;
    2.吉林外国语大学 国际商学院,吉林 长春 130117
  • 收稿日期:2022-12-07 出版日期:2025-03-25 发布日期:2025-07-04
  • 作者简介:郭小平(1984-),男,甘肃酒泉人,博士研究生,助教,研究方向:金融复杂网络与风险管理。
  • 基金资助:
    国家自然科学基金资助项目(62076057)

Analysis of Small-world, Scale-free and Evolutionary Characteristics of Funds’ Co-holding Network under Market Fluctuation

GUO Xiaoping1,2, WANG Jianwei1   

  1. 1. Shool of Business Administration, Northeastern University, Shenyang 110169, China;
    2. International Business School, Jilin Internation Studies University, Changchun 130117, China
  • Received:2022-12-07 Online:2025-03-25 Published:2025-07-04

摘要: 公募基金等机构投资者行为和投资者网络对资本市场信息传递和风险传染具有重要作用,然而现有极少量投资者网络研究多从社会关系等直接关联网络视角研究微观个体网的简单拓扑结构及影响,较少关注基金间共同持股而间接形成的“基金共持网络”的整体拓扑结构特征及体现的基金群体行为。本文借鉴复杂网络研究方法,运用两种基金共同持股行为界定方法分别构建大-小基金间共持网络Network 1和大基金间共持网络Network 2,并对比研究了其整体拓扑结构特性及演化特征。研究发现:(1)两种网络虽都属大型稀疏性网络,但基金间共持行为仍广泛存在;(2)两种网络都具有小世界和无标度特征,但在具体程度上存在显著差异;(3)两种网络的“小世界和无标度性”演化存在显著差异,Network 2的特性是稳步增长,Network 1的特性则更多地受股市行情影响且其波动率大于Network 2。本研究为深入理解基金间共同持股行为和影响以及监管机构进行股市风险管理和机构投资者治理提供了一定的参考和借鉴。

关键词: 基金共持网络, 小世界性, 无标度性, 演化特征

Abstract: As important institutional investors in the capital market, the behaviour and influence of public funds have always been a hot topic of concern for the industry and academia. In recent years, under the policy of “vigorously developing institutional investors” by the SFC, with the rapid expansion of the number and scale of public funds, in the pursuit of diversified investment, the phenomenon of “inter-fund co-holding”, that is, multiple funds hold one or more stocks together, has become increasingly common and the linkage of holdings among funds has become more networked, with profound implications for investor behaviour, asset pricing and risk management.
However, influenced by the traditional economic thinking, the existing studies on fund co-holding have largely been conducted from the perspective of the impact and economic consequences of institutional shareholding, with most studies treating the shareholding and transactions of different institutions as independent but not considering the interconnectedness of individual institutions. A few studies on investor networks have mostly studied the simple topology (e.g. degree or betweenness centrality, etc.) and characteristics of micro-individual networks from the perspective of directly related networks such as social and business relationships, and their impact on investment decisions and performance, while less attention has been paid to the topological characteristics (especially small-world, scale-free, etc.) and evolutionary patterns of “funds’ co-holding networks” forming indirectly through common holding among funds at the overall network level, as well as what factors influence such overall network characteristics and the underlying fund group behaviour. But these are crucial for understanding information transmission, risk contagion and efficient stock market risk management and investor governance in investor networks. Based on the above,this paper draws on the complex network research methodology to construct the co-shareholding Network between large and small funds(Network 1) and the co-shareholding network between large and small funds(Network 2), and compare their overall topological characteristics and evolutionary features.
The results show that: (1)Although the two networks are large sparse networks, the co-holding behavior among funds still widely exists. (2)Both networks have the characteristics of small-world and scale-free, but there are significant differences in the degree of specificity. (3)There are significant differences in the evolution of “small-world and scale-free of characteristics” between the two networks.
This study provides a reference for understanding the influence of mutual shareholding among funds, and for regulators to manage stock market risk and institutional investor governance.

Key words: funds’ co-holding network, small-world characteristics, scale-free characteristics, evolutionary characteristics

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