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Testing Stock Market Efficiency Using Historical Trading Data and Machine Learning
KTH, School of Computer Science and Communication (CSC).
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Stock forecasting is a problem that is important in finance because it aids investors in financial decision making. According to the efficient market hypothesis stock markets are efficient in such a way that it's impossible to gain excess returns over the market by making decisions based on current available information. This paper evaluates the usage of machine learning algorithms and historical trading data for stock price prediction combined with investment strategies in order to test the efficient market hypothesis. The results show that none of the tested machine learning algorithms managed to gain excess returns over the market which confirms the efficient market hypothesis.

Place, publisher, year, edition, pages
2015. , 40 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-166583OAI: oai:DiVA.org:kth-166583DiVA: diva2:811389
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Available from: 2015-05-12 Created: 2015-05-12 Last updated: 2015-05-12Bibliographically approved

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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
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Output format
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