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Testing the predictability of stock markets on real data
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2014 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Stock trading, one of the most common economic activities in the world where the values of stocks change quickly over time. Some are able to turn great profits while others turn great losses on stock trading. Being able to predict changes could be of great help in maximising chances of profitability. In this report we want to evaluate the predictability of stock markets using Artificial Neural Network models, Adaptive Neuro-Fuzzy inference systems and Autoregressive-moving-average models. The markets used is Stockholm, Korea and Barcelona Stock Exchange. We are using two test scenarios, one which consists of incrementing the initial 25 days of training with 5 days until the end of the stock year, and the other one consisting of moving the 25 training days, 5 days until the stock year is over. Our results consists of showing how the predictions look like on the Stockholm market for all the methods and test scenarios and also show graphs of the error rate (percentage) of all methods and test cases in each of the markets. Also a table showing the average error of the methods and test cases, to be able to evaluate which one will perform the best. Our results shows that the ANFIS with at least 50 days of training will perform the best.

Place, publisher, year, edition, pages
2014.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-157691OAI: oai:DiVA.org:kth-157691DiVA: diva2:771126
Examiners
Available from: 2014-12-12 Created: 2014-12-12 Last updated: 2015-08-27Bibliographically 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