Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data
Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Informatio...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2020-07-01
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| Series: | Entropy |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1099-4300/22/7/773 |
| _version_ | 1797562402476130304 |
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| author | Yan Yan Boyao Wu Tianhai Tian Hu Zhang |
| author_facet | Yan Yan Boyao Wu Tianhai Tian Hu Zhang |
| author_sort | Yan Yan |
| collection | DOAJ |
| description | Complex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory. |
| first_indexed | 2024-03-10T18:27:41Z |
| format | Article |
| id | doaj.art-7b026b2f807a48a5ad87436a3e46f4e5 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-03-10T18:27:41Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-7b026b2f807a48a5ad87436a3e46f4e52023-11-20T06:52:00ZengMDPI AGEntropy1099-43002020-07-0122777310.3390/e22070773Development of Stock Networks Using Part Mutual Information and Australian Stock Market DataYan Yan0Boyao Wu1Tianhai Tian2Hu Zhang3School of Mathematics and Physics, Wuhan Institute of Technology, Wuhan 430205, ChinaSchool of Mathematics, Monash University, Melbourne, VIC 3800, AustraliaSchool of Mathematics, Monash University, Melbourne, VIC 3800, AustraliaSchool of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan 430073, ChinaComplex network is a powerful tool to discover important information from various types of big data. Although substantial studies have been conducted for the development of stock relation networks, correlation coefficient is dominantly used to measure the relationship between stock pairs. Information theory is much less discussed for this important topic, though mutual information is able to measure nonlinear pairwise relationship. In this work we propose to use part mutual information for developing stock networks. The path-consistency algorithm is used to filter out redundant relationships. Using the Australian stock market data, we develop four stock relation networks using different orders of part mutual information. Compared with the widely used planar maximally filtered graph (PMFG), we can generate networks with cliques of large size. In addition, the large cliques show consistency with the structure of industrial sectors. We also analyze the connectivity and degree distributions of the generated networks. Analysis results suggest that the proposed method is an effective approach to develop stock relation networks using information theory.https://www.mdpi.com/1099-4300/22/7/773part mutual informationstock relation networkpath-consistencycorrelation coefficientcliquedegree |
| spellingShingle | Yan Yan Boyao Wu Tianhai Tian Hu Zhang Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data Entropy part mutual information stock relation network path-consistency correlation coefficient clique degree |
| title | Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data |
| title_full | Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data |
| title_fullStr | Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data |
| title_full_unstemmed | Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data |
| title_short | Development of Stock Networks Using Part Mutual Information and Australian Stock Market Data |
| title_sort | development of stock networks using part mutual information and australian stock market data |
| topic | part mutual information stock relation network path-consistency correlation coefficient clique degree |
| url | https://www.mdpi.com/1099-4300/22/7/773 |
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