Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks
This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projec...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-03-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/6/1545 |
| _version_ | 1827748856846090240 |
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| author | Ruijie Liu Yajing Huang |
| author_facet | Ruijie Liu Yajing Huang |
| author_sort | Ruijie Liu |
| collection | DOAJ |
| description | This paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network and a stock projected network, whose structural characteristics can intuitively reveal the overlapping portfolios among different shareholders, as well as shareholder allocation structures among different stocks. The degree of nodes in the shareholder projected network obeys the power law distribution, the network aggregation coefficient is large, while the degree of most nodes in the stock projected network is small and the network aggregation coefficient is low. Furthermore, the two projected networks’ community structures are analyzed, respectively. Most of the communities in the shareholder projected network and stock projected network are small-scaled, indicating that the majority of large shareholders hold different shares from each other, and the investment portfolios of large shareholders in different stocks are also significantly different. Finally, by comparing the stock projected sub-network obtained from the shareholder-stock bipartite sub-network in which the degree of shareholder nodes is 2 and the original stock projected network, the effectiveness of the statistically validated network model, and the community division method on the research of the shareholder-stock bipartite network are further verified. These results have important implications for understanding the investment behavior of large shareholders in the stock market and contribute to developing investment strategies and risk management practices. |
| first_indexed | 2024-03-11T06:13:47Z |
| format | Article |
| id | doaj.art-dc1269ccacc44b69bd41454cf3b91bf7 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T06:13:47Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-dc1269ccacc44b69bd41454cf3b91bf72023-11-17T12:30:08ZengMDPI AGMathematics2227-73902023-03-01116154510.3390/math11061545Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s StocksRuijie Liu0Yajing Huang1College of Science, Beijing Forestry University, Beijing 100083, ChinaCollege of Science, Beijing Forestry University, Beijing 100083, ChinaThis paper establishes a shareholder-stock bipartite network based on the data of large shareholders’ shareholding in the Shanghai A-share market of China in 2021. Based on the shareholder-stock bipartite network, the statistically validated network model is applied to establish a shareholder projected network and a stock projected network, whose structural characteristics can intuitively reveal the overlapping portfolios among different shareholders, as well as shareholder allocation structures among different stocks. The degree of nodes in the shareholder projected network obeys the power law distribution, the network aggregation coefficient is large, while the degree of most nodes in the stock projected network is small and the network aggregation coefficient is low. Furthermore, the two projected networks’ community structures are analyzed, respectively. Most of the communities in the shareholder projected network and stock projected network are small-scaled, indicating that the majority of large shareholders hold different shares from each other, and the investment portfolios of large shareholders in different stocks are also significantly different. Finally, by comparing the stock projected sub-network obtained from the shareholder-stock bipartite sub-network in which the degree of shareholder nodes is 2 and the original stock projected network, the effectiveness of the statistically validated network model, and the community division method on the research of the shareholder-stock bipartite network are further verified. These results have important implications for understanding the investment behavior of large shareholders in the stock market and contribute to developing investment strategies and risk management practices.https://www.mdpi.com/2227-7390/11/6/1545shareholder-stock bipartite networkstatistically validated network modelprojected networkcommunity division |
| spellingShingle | Ruijie Liu Yajing Huang Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks Mathematics shareholder-stock bipartite network statistically validated network model projected network community division |
| title | Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks |
| title_full | Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks |
| title_fullStr | Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks |
| title_full_unstemmed | Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks |
| title_short | Structural Analysis of Projected Networks of Shareholders and Stocks Based on the Data of Large Shareholders’ Shareholding in China’s Stocks |
| title_sort | structural analysis of projected networks of shareholders and stocks based on the data of large shareholders shareholding in china s stocks |
| topic | shareholder-stock bipartite network statistically validated network model projected network community division |
| url | https://www.mdpi.com/2227-7390/11/6/1545 |
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