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Analysing multifactor investing & artificial neural network for modern stock market prediction
Jönköping University, Jönköping International Business School, JIBS, Business Administration.
Jönköping University, Jönköping International Business School, JIBS, Business Administration.
2019 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this research we investigate the relationship between multifactor investing and Artificial Neural Network (ANN) and contribute to modern stock market prediction. We present the components for multifactor investing i.e. value, quality, size, low volatility & momentum as well as a methodology for ANN which provides the theory for the results. The return for the multifactor funds tested in this research is recorded below the benchmark used. However, the factors do have a dynamic relationship when testing for correlation and the multifactor regression analysis showed a high explanatory power (R2) for the funds. Based on the methodology of an ANN we establish that it is possible to use the knowledge from multifactor investing to train the technology with. When summarizing peer reviewed journals, we find that momentum have already been recurrently used in previous stock market prediction systems based on ANN, but the remaining factors have not. We conclude that there is an opportunity to use several factors to train an ANN due to their dynamic relationship and unique characteristics.

Place, publisher, year, edition, pages
2019. , p. 75
Keywords [en]
Multifactor Investing, Stock Market Prediction, Artificial Neural Network, Regression Analysis
National Category
Business Administration
Identifiers
URN: urn:nbn:se:hj:diva-43875ISRN: JU-IHH-FÖA-2-20190831OAI: oai:DiVA.org:hj-43875DiVA, id: diva2:1319251
Subject / course
JIBS, Business Administration
Supervisors
Examiners
Available from: 2019-06-20 Created: 2019-05-30 Last updated: 2019-06-20Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • 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