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dc.contributor.author
Vaya, Apostolou
en
dc.date.accessioned
2020-06-05T14:14:42Z
dc.date.available
2020-06-06T00:00:31Z
dc.date.issued
2020-06-05
dc.identifier.uri
https://repository.ihu.edu.gr//xmlui/handle/11544/29517
dc.rights
Default License
dc.subject
Banking networks
en
dc.subject
Evolution
en
dc.subject
Minimum spanning tree
en
dc.subject
Systemic risk
en
dc.title
Evolution of Banking Networks: A study of profitability correlations from 2005 to 2018
en
heal.type
masterThesis
en_US
heal.creatorID.email
vagia.apostolou@gmail.com
heal.classification
Banking Networks
en
heal.language
en
en_US
heal.access
free
en_US
heal.license
http://creativecommons.org/licenses/by-nc/4.0
en_US
heal.recordProvider
School of Economics, Business Administration and Legal Studies, MSc in Banking and Finance
en_US
heal.publicationDate
2020-02-19
heal.abstract
The goal of this dissertation is to obtain insight about the behavior of Ecosystem in Banking sector during economic incidents or political shocks that provoke changes in Bank Holding Companies characterized as “Top” (N =100) based on their Market Capitalization during period 2018-2019. Regarding the last results and the availability of data at FDCI Database, we find the profitability ratios of top banks (46 out of 100): (a) Return on Assets (ROA), (b) Return on Equity (ROE) and (c) Net Interest Margin (NIMY) during 2005-2018 (quarterly data). Using the network analysis, we take metrics: (a) Weighted Degree for each node (bank), (b) Density for the whole network and (c) the Minimum Spanning Tree (MST) method. The inputs for the above metrics are the ROA, ROE and NIMY and the outputs inform us which of the top banks are Core, Influential with Positive Weights (positive correlation) and Influential with Negative weights (negative correlation). It is important to refer the role of Systemic Risk that emerges from the type of interconnection between banks and its two related concepts the “Too Connected to Fail” (TCTF) and “Two Big to (let) Fail” (TBTF). Taking into account the results we refer how the top 10 (out of 46) banks are characterized during 2005-2018 (core or influential or both) and the amount of core and influential banks each time period. One important remark based on our analysis is that the Banks in US tend to be robust to financial shocks and struggle to revive from the crises. Regarding to density, our network analysis metrics have the same process as it is depicted in GDP graph. Finally, we assume that network analysis is an innovative field that provides us hidden and influential role of the nodes (banks).
en
heal.advisorName
Gogas, Periklis
en
heal.advisorID
perrygogas@gmail.com
en_US
heal.committeeMemberName
Gogas, Periklis
en
heal.committeeMemberName
Archontakis, Fragkiskos
en
heal.academicPublisher
IHU
en
heal.academicPublisherID
ihu
en_US
heal.numberOfPages
35
en_US


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