Network structure detection and analysis of Shanghai stock market
<p><strong>Purpose:</strong> In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange) 180-index, a stock correlation network is built to find the intra-community and inter-community relationship.</p> <p><strong>Design/me...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
OmniaScience
2015-04-01
|
Series: | Journal of Industrial Engineering and Management |
Subjects: | |
Online Access: | http://www.jiem.org/index.php/jiem/article/view/1314 |
_version_ | 1828866107464744960 |
---|---|
author | Sen Wu Mengjiao Tuo Deying Xiong |
author_facet | Sen Wu Mengjiao Tuo Deying Xiong |
author_sort | Sen Wu |
collection | DOAJ |
description | <p><strong>Purpose:</strong> In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange) 180-index, a stock correlation network is built to find the intra-community and inter-community relationship.</p> <p><strong>Design/methodology/approach:</strong> The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds.</p> <p><strong>Findings:</strong> The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries.</p> <p><strong>Originality/value:</strong> Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.</p> |
first_indexed | 2024-12-13T04:43:25Z |
format | Article |
id | doaj.art-01801bb06b364623aad422ce75c25fd7 |
institution | Directory Open Access Journal |
issn | 2013-8423 2013-0953 |
language | English |
last_indexed | 2024-12-13T04:43:25Z |
publishDate | 2015-04-01 |
publisher | OmniaScience |
record_format | Article |
series | Journal of Industrial Engineering and Management |
spelling | doaj.art-01801bb06b364623aad422ce75c25fd72022-12-21T23:59:14ZengOmniaScienceJournal of Industrial Engineering and Management2013-84232013-09532015-04-018238339810.3926/jiem.1314342Network structure detection and analysis of Shanghai stock marketSen Wu0Mengjiao Tuo1Deying Xiong2University of Science and Technology BeijingUniversity of Science and Technology BeijingUniversity of Science and Technology Beijing<p><strong>Purpose:</strong> In order to investigate community structure of the component stocks of SSE (Shanghai Stock Exchange) 180-index, a stock correlation network is built to find the intra-community and inter-community relationship.</p> <p><strong>Design/methodology/approach:</strong> The stock correlation network is built taking the vertices as stocks and edges as correlation coefficients of logarithm returns of stock price. It is built as undirected weighted at first. GN algorithm is selected to detect community structure after transferring the network into un-weighted with different thresholds.</p> <p><strong>Findings:</strong> The result of the network community structure analysis shows that the stock market has obvious industrial characteristics. Most of the stocks in the same industry or in the same supply chain are assigned to the same community. The correlation of the internal stock prices’ fluctuation is closer than in different communities. The result of community structure detection also reflects correlations among different industries.</p> <p><strong>Originality/value:</strong> Based on the analysis of the community structure in Shanghai stock market, the result reflects some industrial characteristics, which has reference value to relationship among industries or sub-sectors of listed companies.</p>http://www.jiem.org/index.php/jiem/article/view/1314complex network, stock market, community structure, GN algorithm |
spellingShingle | Sen Wu Mengjiao Tuo Deying Xiong Network structure detection and analysis of Shanghai stock market Journal of Industrial Engineering and Management complex network, stock market, community structure, GN algorithm |
title | Network structure detection and analysis of Shanghai stock market |
title_full | Network structure detection and analysis of Shanghai stock market |
title_fullStr | Network structure detection and analysis of Shanghai stock market |
title_full_unstemmed | Network structure detection and analysis of Shanghai stock market |
title_short | Network structure detection and analysis of Shanghai stock market |
title_sort | network structure detection and analysis of shanghai stock market |
topic | complex network, stock market, community structure, GN algorithm |
url | http://www.jiem.org/index.php/jiem/article/view/1314 |
work_keys_str_mv | AT senwu networkstructuredetectionandanalysisofshanghaistockmarket AT mengjiaotuo networkstructuredetectionandanalysisofshanghaistockmarket AT deyingxiong networkstructuredetectionandanalysisofshanghaistockmarket |