Dalarna University's logo and link to the university's website

du.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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
Investors Segmentation and Engagement Prediction: The Case of An Investment Application in The Swedish Financial Market.
Dalarna University, School of Information and Engineering.
2021 (English)Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The use of virtual advice tools in asset management is a modern topic that needs further research and development to make its adoption successful among retail investors. This study, segment investors into different groups using k-means clustering algorithm for an investment application in the Swedish financial market. Results show four well separated clusters, with each cluster characterized by a specific assets combination, risk attitudes and investment’s sophistication level. The results are going to help the company improve its efficiency in marketing campaigns and in financial advisory of investors. Besides, the need to increase engagement among users towards the application and proactively keep disengaged ones, led to model, and predict different levels of engagement reflecting the frequency and churn rates and use of sophisticated features of the application. Data mining methodology are applied including the use of logistic regression for prediction and classification of engagements levels. The results show a general high disengagement rates from the application.

Place, publisher, year, edition, pages
2021.
Keywords [en]
Investor’s clustering, financial assets, sophistication level, User’s engagement
National Category
Other Social Sciences
Identifiers
URN: urn:nbn:se:du-37473OAI: oai:DiVA.org:du-37473DiVA, id: diva2:1571179
Subject / course
Microdata Analysis
Available from: 2021-06-22 Created: 2021-06-22

Open Access in DiVA

No full text in DiVA

By organisation
School of Information and Engineering
Other Social Sciences

Search outside of DiVA

GoogleGoogle Scholar

urn-nbn

Altmetric score

urn-nbn
Total: 86 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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