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A comparative study of hybrid artificial neural network models for one-day stock price prediction
KTH, School of Computer Science and Communication (CSC).
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]

Prediction of stock prices is an important financial problem that is receiving increased attention in the field of artificial intelligence. Many different neural network and hybrid models for obtaining accurate prediction results have been proposed during the last few years in an attempt to outperform the traditional linear and nonlinear approaches.

This study evaluates the performance of three different hybrid neural network models used for one-day stock close price prediction; a pre-processed evolutionary Levenberg-Marquardt neural network, Bayesian regularized artificial neural network and neural network with technical- and fractal analysis. It was also determined which of the three outperformed the others.

The performance evaluation and comparison of the models are done using statistical error measures for accuracy; mean square error, symmetric mean absolute percentage error and point of change in direction.

The results indicate good performance values for the Bayesian regularized artificial neural network, and varied performance for the others. Using the Friedman test, one model clearly is different in its performance relative to the others, probably the above mentioned model.

The results for two of the models showed a large standard deviation of the error measurements which indicates that the results are not entirely reliable.

Place, publisher, year, edition, pages
2015.
Keyword [en]
artificial neural network, hybrid, comparative study
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-166641OAI: oai:DiVA.org:kth-166641DiVA: diva2:811673
Supervisors
Examiners
Available from: 2015-05-12 Created: 2015-05-12 Last updated: 2015-05-12Bibliographically 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