• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

中国关键行业股票市场复杂网络稳定性研究

Study on the Stability of Complex Networks in the Stock Markets of Key Industries in China.

作者信息

Wang Zinuoqi, Zhang Guofeng, Ma Xiaojing, Wang Ruixian

机构信息

School of Economics, Hebei GEO University, Shijiazhuang 050031, China.

Research Base for Scientific-Technological Innovation and Regional Economic Sustainable Development of Hebei Province, Hebei GEO University, Shijiazhuang 050031, China.

出版信息

Entropy (Basel). 2024 Jun 30;26(7):569. doi: 10.3390/e26070569.

DOI:10.3390/e26070569
PMID:39056931
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11275281/
Abstract

Investigating the significant "roles" within financial complex networks and their stability is of great importance for preventing financial risks. On one hand, this paper initially constructs a complex network model of the stock market based on mutual information theory and threshold methods, combined with the closing price returns of stocks. It then analyzes the basic topological characteristics of this network and examines its stability under random and targeted attacks by varying the threshold values. On the other hand, using systemic risk entropy as a metric to quantify the stability of the stock market, this paper validates the impact of the COVID-19 pandemic as a widespread, unexpected event on network stability. The research results indicate that this complex network exhibits small-world characteristics but cannot be strictly classified as a scale-free network. In this network, key roles are played by the industrial sector, media and information services, pharmaceuticals and healthcare, transportation, and utilities. Upon reducing the threshold, the network's resilience to random attacks is correspondingly strengthened. Dynamically, from 2000 to 2022, systemic risk in significant industrial share markets significantly increased. From a static perspective, the period around 2019, affected by the COVID-19 pandemic, experienced the most drastic fluctuations. Compared to the year 2000, systemic risk entropy in 2022 increased nearly sixtyfold, further indicating an increasing instability within this complex network.

摘要

研究金融复杂网络中的重要“角色”及其稳定性对于防范金融风险至关重要。一方面,本文首先基于互信息理论和阈值方法,结合股票收盘价收益率,构建了股票市场的复杂网络模型。然后分析了该网络的基本拓扑特征,并通过改变阈值来考察其在随机攻击和针对性攻击下的稳定性。另一方面,本文使用系统风险熵作为衡量股票市场稳定性的指标,验证了作为广泛、意外事件的新冠疫情对网络稳定性的影响。研究结果表明,该复杂网络具有小世界特征,但不能严格归类为无标度网络。在这个网络中,关键角色由工业部门、媒体和信息服务、制药和医疗保健、交通运输以及公用事业部门扮演。降低阈值后,网络对随机攻击的恢复力相应增强。动态来看,2000年至2022年期间,重要行业股票市场的系统风险显著增加。从静态角度看,2019年前后受新冠疫情影响,波动最为剧烈。与2000年相比,2022年的系统风险熵增加了近60倍,进一步表明这个复杂网络的不稳定性在加剧。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/1d97e55b248a/entropy-26-00569-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/8322f526d945/entropy-26-00569-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/4611fba67661/entropy-26-00569-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/74cba1f9ab23/entropy-26-00569-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/3836385729ba/entropy-26-00569-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/e404d7a0e888/entropy-26-00569-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/0269b018a476/entropy-26-00569-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/3d28160d0aa9/entropy-26-00569-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/489973b2069a/entropy-26-00569-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/da367afd20e5/entropy-26-00569-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/f3919a9ae51a/entropy-26-00569-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/1d97e55b248a/entropy-26-00569-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/8322f526d945/entropy-26-00569-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/4611fba67661/entropy-26-00569-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/74cba1f9ab23/entropy-26-00569-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/3836385729ba/entropy-26-00569-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/e404d7a0e888/entropy-26-00569-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/0269b018a476/entropy-26-00569-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/3d28160d0aa9/entropy-26-00569-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/489973b2069a/entropy-26-00569-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/da367afd20e5/entropy-26-00569-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/f3919a9ae51a/entropy-26-00569-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/850c/11275281/1d97e55b248a/entropy-26-00569-g011.jpg

相似文献

1
Study on the Stability of Complex Networks in the Stock Markets of Key Industries in China.中国关键行业股票市场复杂网络稳定性研究
Entropy (Basel). 2024 Jun 30;26(7):569. doi: 10.3390/e26070569.
2
Research on Risk Contagion in ESG Industries: An Information Entropy-Based Network Approach.ESG行业中的风险传染研究:一种基于信息熵的网络方法。
Entropy (Basel). 2024 Feb 27;26(3):206. doi: 10.3390/e26030206.
3
The dynamic network of industries in US stock market: Evidence of GFC, COVID-19 pandemic and Russia-Ukraine war.美国股票市场中行业的动态网络:全球金融危机、新冠疫情和俄乌战争的证据。
Heliyon. 2023 Sep 6;9(9):e19726. doi: 10.1016/j.heliyon.2023.e19726. eCollection 2023 Sep.
4
Predicting stock market movements using network science: an information theoretic approach.运用网络科学预测股票市场走势:一种信息论方法。
Appl Netw Sci. 2017;2(1):35. doi: 10.1007/s41109-017-0055-y. Epub 2017 Oct 10.
5
The structure and resilience of financial market networks.金融市场网络的结构和弹性。
Chaos. 2012 Mar;22(1):013117. doi: 10.1063/1.3683467.
6
Volatility correlation structure, dynamic network and portfolio implications of Chinese stock market.中国股票市场的波动率相关结构、动态网络及投资组合影响
Procedia Comput Sci. 2022;202:122-127. doi: 10.1016/j.procs.2022.04.017. Epub 2022 May 10.
7
Information Transfer between Stock Market Sectors: A Comparison between the USA and China.股票市场板块间的信息传递:美国与中国的比较
Entropy (Basel). 2020 Feb 7;22(2):194. doi: 10.3390/e22020194.
8
Structural Change and Dynamics of Pakistan Stock Market during Crisis: A Complex Network Perspective.危机期间巴基斯坦股票市场的结构变化与动态:复杂网络视角
Entropy (Basel). 2019 Mar 5;21(3):248. doi: 10.3390/e21030248.
9
The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets.新冠疫情对股票和加密货币市场稳定性及序列不规则性的影响。
Chaos Solitons Fractals. 2020 Sep;138:109936. doi: 10.1016/j.chaos.2020.109936. Epub 2020 May 28.
10
The Evolution Characteristics of Systemic Risk in China's Stock Market Based on a Dynamic Complex Network.基于动态复杂网络的中国股票市场系统性风险演化特征
Entropy (Basel). 2020 Jun 2;22(6):614. doi: 10.3390/e22060614.

引用本文的文献

1
Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.开发金融网络预警系统:一种可解释的机器学习方法。
Entropy (Basel). 2024 Sep 17;26(9):796. doi: 10.3390/e26090796.

本文引用的文献

1
Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany.新冠疫情期间的原油市场与股票市场:来自美国、日本和德国的证据
Int Rev Financ Anal. 2021 Mar;74:101702. doi: 10.1016/j.irfa.2021.101702. Epub 2021 Feb 6.
2
Research on Risk Contagion in ESG Industries: An Information Entropy-Based Network Approach.ESG行业中的风险传染研究:一种基于信息熵的网络方法。
Entropy (Basel). 2024 Feb 27;26(3):206. doi: 10.3390/e26030206.
3
Complexity in Economic and Social Systems: Cryptocurrency Market at around COVID-19.
经济与社会系统中的复杂性:新冠疫情期间的加密货币市场
Entropy (Basel). 2020 Sep 18;22(9):1043. doi: 10.3390/e22091043.
4
A behavioral approach to instability pathways in financial markets.一种金融市场不稳定性路径的行为方法。
Nat Commun. 2020 Apr 6;11(1):1707. doi: 10.1038/s41467-020-15356-z.
5
Development of stock correlation networks using mutual information and financial big data.利用互信息和金融大数据开发股票相关网络。
PLoS One. 2018 Apr 18;13(4):e0195941. doi: 10.1371/journal.pone.0195941. eCollection 2018.
6
The structure and resilience of financial market networks.金融市场网络的结构和弹性。
Chaos. 2012 Mar;22(1):013117. doi: 10.1063/1.3683467.
7
Collective dynamics of 'small-world' networks.“小世界”网络的集体动力学
Nature. 1998 Jun 4;393(6684):440-2. doi: 10.1038/30918.