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Technical analysis inspired machine learning for stock market data
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

In this thesis we evaluate four different machine learning algorithms, namely Naive Bayes Classifier, Support Vector Machines, Extreme Learning Machine and Random Forest in the context of stock market investments. The aim is to provide additional information that can be beneficial when creating stock market models to be used in a machine learning setting. All four algorithms are trained on different configurations of data, based on concepts from technical analysis. The configurations contain closing prices, volatility and trading volume in different combinations. These variables are taken from past trading days, where the number of days from which data is to be collected ranges from 2 to 30. The resulting predictors attained from the various algorithms and configurations above reach accuracy rates between $50-54\%$. This thesis concludes that the effect of the different evaluated features vary depending on which algorithm is used as well as how many past trading days are included. Concluding, it is ascertained that the usage of volatility features should at least be considered when building a machine learning model in a stock market context.

Place, publisher, year, edition, pages
2016.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-186863OAI: oai:DiVA.org:kth-186863DiVA: diva2:928205
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Examiners
Available from: 2016-05-18 Created: 2016-05-15 Last updated: 2016-05-18Bibliographically approved

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CiteExportLink to record
Permanent link

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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