Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering
Nowadays, financial markets in Iran have attracted the attention of many managers, investors and financial policymakers. Therefore, in order to make the optimal decision and reduce the risks in such a market, it is important to identify and analyze the network behavior of the financial markets at di...
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| Format: | Article |
| Language: | English |
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Iran Finance Association
2020-10-01
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| Series: | Iranian Journal of Finance |
| Subjects: | |
| Online Access: | https://www.ijfifsa.ir/article_117893_835b511fb0a5cd6c91c598102d0ada65.pdf |
| _version_ | 1798013873758928896 |
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| author | Salman Abbasian-Naghneh Reza Tehrani Mohammad Tamimi |
| author_facet | Salman Abbasian-Naghneh Reza Tehrani Mohammad Tamimi |
| author_sort | Salman Abbasian-Naghneh |
| collection | DOAJ |
| description | Nowadays, financial markets in Iran have attracted the attention of many managers, investors and financial policymakers. Therefore, in order to make the optimal decision and reduce the risks in such a market, it is important to identify and analyze the network behavior of the financial markets at different times to obtain the optimal decision. The current study aims to answer the following research question; how is it possible to use the minimum spanning tree and hierarchical clustering in the network analysis of the Tehran Stock Exchange? The period examined was 2013 to 2018. The population consisted of all the companies accepted in Tehran Stock Exchange. The sampling was selected purposefully and contained the companies which had at least one trading day in the time span from the beginning of 2013 to the end of 2018. The stock of the investigated companies was considered as the vertexes of one graph and the coherent information criterion was considered as the weight of the edge. First, the minimum spanning tree of the graph was calculated. The results revealed that the stocks of DarooAbuReihan, DarooPakhsh and Alborzdaroo had a high influence on directing the prices of the other stocks. Furthermore, the results of hierarchical clustering classified the stocks of the companies into 8 clusters. This study presents a viewpoint about the modern method designed for the analysis of complex financial networks. Moreover, the study offers an analysis of Iran's stock market structure which can be the center of finance researchers and analysts' attention. |
| first_indexed | 2024-04-11T15:08:37Z |
| format | Article |
| id | doaj.art-7e27050af9734690823feb1cb5b21a7d |
| institution | Directory Open Access Journal |
| issn | 2676-6337 2676-6345 |
| language | English |
| last_indexed | 2024-04-11T15:08:37Z |
| publishDate | 2020-10-01 |
| publisher | Iran Finance Association |
| record_format | Article |
| series | Iranian Journal of Finance |
| spelling | doaj.art-7e27050af9734690823feb1cb5b21a7d2022-12-22T04:16:43ZengIran Finance AssociationIranian Journal of Finance2676-63372676-63452020-10-014211810.22034/ijf.2020.193709.1042117893Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical ClusteringSalman Abbasian-Naghneh0Reza Tehrani1Mohammad Tamimi2Associate Prof., Department of Financial Management, Masjed-Soleiman Branch, Islamic Azad University, Masjed-Soleiman, Iran.Prof., Faculty of Management, University of Tehran, Tehran, Iran.Associate Prof.,Department ofAccounting, Dezful Branch, Islamic Azad University, Dezful, Iran.Nowadays, financial markets in Iran have attracted the attention of many managers, investors and financial policymakers. Therefore, in order to make the optimal decision and reduce the risks in such a market, it is important to identify and analyze the network behavior of the financial markets at different times to obtain the optimal decision. The current study aims to answer the following research question; how is it possible to use the minimum spanning tree and hierarchical clustering in the network analysis of the Tehran Stock Exchange? The period examined was 2013 to 2018. The population consisted of all the companies accepted in Tehran Stock Exchange. The sampling was selected purposefully and contained the companies which had at least one trading day in the time span from the beginning of 2013 to the end of 2018. The stock of the investigated companies was considered as the vertexes of one graph and the coherent information criterion was considered as the weight of the edge. First, the minimum spanning tree of the graph was calculated. The results revealed that the stocks of DarooAbuReihan, DarooPakhsh and Alborzdaroo had a high influence on directing the prices of the other stocks. Furthermore, the results of hierarchical clustering classified the stocks of the companies into 8 clusters. This study presents a viewpoint about the modern method designed for the analysis of complex financial networks. Moreover, the study offers an analysis of Iran's stock market structure which can be the center of finance researchers and analysts' attention.https://www.ijfifsa.ir/article_117893_835b511fb0a5cd6c91c598102d0ada65.pdfnetwork behavior of stockminimum spanning treehierarchical clusteringbehavioral similarity |
| spellingShingle | Salman Abbasian-Naghneh Reza Tehrani Mohammad Tamimi Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering Iranian Journal of Finance network behavior of stock minimum spanning tree hierarchical clustering behavioral similarity |
| title | Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering |
| title_full | Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering |
| title_fullStr | Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering |
| title_full_unstemmed | Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering |
| title_short | Network Analysis of Tehran Stock Exchange using Minimum Spanning Tree and Hierarchical Clustering |
| title_sort | network analysis of tehran stock exchange using minimum spanning tree and hierarchical clustering |
| topic | network behavior of stock minimum spanning tree hierarchical clustering behavioral similarity |
| url | https://www.ijfifsa.ir/article_117893_835b511fb0a5cd6c91c598102d0ada65.pdf |
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