Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Cluster analysis of European banking data
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematical Statistics.
2017 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Klusteranalys av Europeisk bankdata (Swedish)
Abstract [en]

Credit institutions constitute a central part of life as it is today and has been doing so for a long time. A fault within the banking system can cause a tremendous amount of damage to individuals as well as countries. A recent and memorable fault is the global financial crisis 2007-2009. It has affected millions of people in different ways ever since it struck. What caused it is a complex issue which cannot be answered easily. But what has been done to prevent something similar to occur once again? How has the business models of the credit institutions changed since the crisis? Cluster analysis is used in this thesis to address these questions. Banking-data were processed with Calinski-Harabasz Criterion and Ward's method and this resulted in two clusters being found. A cluster is a collection of observations that have similar characteristics or business model in this case. The business models that the clusters represents are universal banking with a retail focus and universal banking with a wholesale focus. These business models have been analyzed over time (2007-2016), which revealed that the credit institutions have developed in a healthy direction. Thus, credit institutions were more financially reliable in 2016 compared to 2007. According to trends in the data this development is likely to continue.

Abstract [sv]

Kreditinstituten utgör en central del av livet som det ser ut idag och har gjort det under en lång tid. Ett fel inom banksystemet kan orsaka enorma skador för individer likväl som länder. Ett nutida och minnesvärt fel är den globala finanskrisen 2007-2009. Den har påverkat millioner människor på olika vis ända sedan den slog till. Vad som orsakade den är en komplex fråga som inte kan besvaras med lätthet. Men vad har gjorts för att förebygga att något liknande händer igen? Hur har affärsmodellerna för kreditinstituten ändrats sedan krisen? Klusteranalys används i denna rapport för att adressera dessa frågor. Bankdata processerades med Calinski-Harabasz Kriteriet and Wards metod och detta resulterade i att två kluster hittades. Ett kluster är en samling observationer med liknande karakteristik eller affärsmodell i detta fall. De affärsmodeller som klustrena representerar är universella banker med retail fokus samt universella banker med wholessale fokus. Dessa affärsmodeller har analyserats över tid, vilket har avslöjat att kreditinstituten har utvecklats i en hälsosam riktning. Kreditinstituten var mer finansiellt pålitliga 2016 jämfört med 2007. Enligt trender i datan så är det troligt att denna utveckling forsätter.

Place, publisher, year, edition, pages
2017.
Series
TRITA-MAT-E ; 2017:76
Keyword [en]
Business models, modelling, Europe, credit institution, cluster analysis and financial crisis.
Keyword [sv]
Affärsmodeller, modellering, Europa, kreditinstitut, klusteranalys och finanskris.
National Category
Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-219597OAI: oai:DiVA.org:kth-219597DiVA, id: diva2:1165806
External cooperation
Finansinspektionen (FI)
Subject / course
Financial Mathematics
Educational program
Master of Science - Applied and Computational Mathematics
Supervisors
Examiners
Available from: 2017-12-13 Created: 2017-12-13 Last updated: 2017-12-13Bibliographically approved

Open Access in DiVA

fulltext(954 kB)28 downloads
File information
File name FULLTEXT01.pdfFile size 954 kBChecksum SHA-512
a00c148caa75025c7945b3b2fbcd7be3baefa6ca95e80dd9686f633c9bc51d2ae9b6a848321121232580c9701c2db9d23d576ec6d80ee58e8776062807eee5d1
Type fulltextMimetype application/pdf

By organisation
Mathematical Statistics
Computational Mathematics

Search outside of DiVA

GoogleGoogle Scholar
Total: 28 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 124 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf