• Türkçe
    • English
  • English 
    • Türkçe
    • English
  • Login
View Item 
  •   DSpace@Muğla
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • WoS İndeksli Yayınlar Koleksiyonu
  • View Item
  •   DSpace@Muğla
  • Araştırma Çıktıları | TR-Dizin | WoS | Scopus | PubMed
  • WoS İndeksli Yayınlar Koleksiyonu
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Hierarchies in communities of UK stock market from the perspective of Brexit

Date

2020

Author

Balci, Mehmet Ali
Akguller, Omer
Can Guzel, Serdar
Article has an altmetric score of 3

See more details

Posted by 4 X users
17 readers on Mendeley

Metadata

Show full item record

Abstract

Nowadays, increase of analyzing stock markets as complex systems lead graph theory to play a key role. For instance, detecting graph communities is an important task in the analysis of stocks, and as planar maximally filtered graphs let us to get important information for the topology of the market. In this study, we first obtain correlation network representation of UK's leading stock market network by using a novel threshold method. Then, we determine vertex clusters by using modularity and analyze clusters in planar maximally filtered graph substructures. Our analyze include a new measure called weighted Gini index for measuring the sparsity. The main goal of this paper is to study the hierarchical evolution of the market communities throughout the Brexit referendum, which is known as the stress period for the stock market. Hence, the overall sample is divided into two sub-periods of pre-referendum, and post-referendum to obtain communities and hierarchical structures. Our results indicate that financial companies are leading elements of the clusters. Moreover, the significant changes within the network topologies are observed for insurance, consumer goods, consumer services, mining, and technology sectors whereas oil and gas and health care sectors have not been affected by Brexit stress.

Source

Journal of Applied Statistics

URI

https://doi.org/10.1080/02664763.2020.1796942
https://hdl.handle.net/20.500.12809/398

Collections

  • Scopus İndeksli Yayınlar Koleksiyonu [6219]
  • WoS İndeksli Yayınlar Koleksiyonu [6466]



DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 




| Policy | Guide | Contact |

DSpace@Muğla

by OpenAIRE
Advanced Search

sherpa/romeo

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeInstitution AuthorThis CollectionBy Issue DateAuthorsTitlesSubjectsTypeLanguageDepartmentCategoryPublisherAccess TypeInstitution Author

My Account

LoginRegister

DSpace software copyright © 2002-2015  DuraSpace
Contact Us | Send Feedback
Theme by 
@mire NV
 

 


|| Policy || Guide|| Instruction || Library || Muğla Sıtkı Koçman University || OAI-PMH ||

Muğla Sıtkı Koçman University, Muğla, Turkey
If you find any errors in content, please contact:

Creative Commons License
Muğla Sıtkı Koçman University Institutional Repository is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 Unported License..

DSpace@Muğla:


DSpace 6.2

tarafından İdeal DSpace hizmetleri çerçevesinde özelleştirilerek kurulmuştur.