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Analysis of stock forum texts to examine correlation to stock prices
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Computing Science.
2016 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this thesis, four methods of classification from statistical learning have been used to examine correlations between stock forum discussions and stock prices. The classifiers Naive Bayes, support vector machine, AdaBoost and random forest, were used on text data from two different stock forums to see if the text had any predictive power for the stock price of five different companies. The volatility and the direction of the price - whether it would go up or down - over a day was measured. The highest accuracy obtained for predicting high or low volatility came from random forest and was 85.2 %. For price difference the highest accuracy was 69.2 %, using the support vector machine. The average accuracy for predicting the price difference was 58.6 % and the average accuracy for predicting the volatility was 73.4 %. This thesis was made in collaboration with the company Scila which works with stock market security.

Place, publisher, year, edition, pages
2016. , 47 p.
Series
UPTEC F, ISSN 1401-5757 ; 16030
Keyword [en]
statistical learning, machine learning
National Category
Computer and Information Science Engineering and Technology
Identifiers
URN: urn:nbn:se:uu:diva-298484OAI: oai:DiVA.org:uu-298484DiVA: diva2:946691
External cooperation
Scila
Educational program
Master Programme in Engineering Physics
Supervisors
Examiners
Available from: 2016-07-05 Created: 2016-07-05 Last updated: 2016-07-05Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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  • Other locale
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Output format
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  • asciidoc
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