应用最小生成树确定市场关联结构

B. T. Khoa, T. Huynh, Vo Dinh Nhat Truong, Le Vu Truong, Do Bui Xuan Cuong, Tran Khanh
{"title":"应用最小生成树确定市场关联结构","authors":"B. T. Khoa, T. Huynh, Vo Dinh Nhat Truong, Le Vu Truong, Do Bui Xuan Cuong, Tran Khanh","doi":"10.46223/hcmcoujs.tech.en.13.1.2669.2023","DOIUrl":null,"url":null,"abstract":"Determining the structure of market correlation is an important topic in theory and experiments. Under the impact of the Covid-19 pandemic, the market structure may be deformed. Therefore, this study examines the pandemic’s impact on the market structure. This study considered the correlation structure of the VN30 portfolio (including 30 stocks with the largest market capitalization); the collecting period is from July 28, 2000, to July 30, 2021. The data was divided into 02 phases before and after the pandemic. The Kruskal algorithm is implemented to determine the Minimal Spanning Tree (MST) structure to define the structure of market correlation. This study compared the change in the structure before and after the Covid-19 pandemic by structures’ mean of distances comparison. T-test results show that there are structural differences before and after the pandemic. Based on the research result, investors should change their risk management strategy to suit the market context because the previous structure has been changed.","PeriodicalId":34742,"journal":{"name":"Ho Chi Minh City Open University Journal of Science Engineering and Technology","volume":"52 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Minimal Spanning Tree application to determine market correlation structure\",\"authors\":\"B. T. Khoa, T. Huynh, Vo Dinh Nhat Truong, Le Vu Truong, Do Bui Xuan Cuong, Tran Khanh\",\"doi\":\"10.46223/hcmcoujs.tech.en.13.1.2669.2023\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Determining the structure of market correlation is an important topic in theory and experiments. Under the impact of the Covid-19 pandemic, the market structure may be deformed. Therefore, this study examines the pandemic’s impact on the market structure. This study considered the correlation structure of the VN30 portfolio (including 30 stocks with the largest market capitalization); the collecting period is from July 28, 2000, to July 30, 2021. The data was divided into 02 phases before and after the pandemic. The Kruskal algorithm is implemented to determine the Minimal Spanning Tree (MST) structure to define the structure of market correlation. This study compared the change in the structure before and after the Covid-19 pandemic by structures’ mean of distances comparison. T-test results show that there are structural differences before and after the pandemic. Based on the research result, investors should change their risk management strategy to suit the market context because the previous structure has been changed.\",\"PeriodicalId\":34742,\"journal\":{\"name\":\"Ho Chi Minh City Open University Journal of Science Engineering and Technology\",\"volume\":\"52 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-04-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ho Chi Minh City Open University Journal of Science Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.46223/hcmcoujs.tech.en.13.1.2669.2023\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ho Chi Minh City Open University Journal of Science Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.46223/hcmcoujs.tech.en.13.1.2669.2023","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

确定市场关联结构是理论和实验中的一个重要课题。在新冠肺炎疫情的影响下,市场结构可能会发生变形。因此,本研究考察了疫情对市场结构的影响。本研究考虑了VN30投资组合(包括市值最大的30只股票)的相关结构;收集期为2000年7月28日至2021年7月30日。数据分为大流行前后的02个阶段。采用Kruskal算法确定最小生成树(minimum Spanning Tree, MST)结构,定义市场关联结构。本研究通过结构均值距离比较,比较了新冠肺炎大流行前后结构的变化。t检验结果显示,疫情前后存在结构性差异。根据研究结果,投资者应该改变他们的风险管理策略,以适应市场环境,因为之前的结构已经改变。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Minimal Spanning Tree application to determine market correlation structure
Determining the structure of market correlation is an important topic in theory and experiments. Under the impact of the Covid-19 pandemic, the market structure may be deformed. Therefore, this study examines the pandemic’s impact on the market structure. This study considered the correlation structure of the VN30 portfolio (including 30 stocks with the largest market capitalization); the collecting period is from July 28, 2000, to July 30, 2021. The data was divided into 02 phases before and after the pandemic. The Kruskal algorithm is implemented to determine the Minimal Spanning Tree (MST) structure to define the structure of market correlation. This study compared the change in the structure before and after the Covid-19 pandemic by structures’ mean of distances comparison. T-test results show that there are structural differences before and after the pandemic. Based on the research result, investors should change their risk management strategy to suit the market context because the previous structure has been changed.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
6
审稿时长
8 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信