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Prediction of securities' behavior using a multi-level artificial neural network with extra inputs between layers
KTH, School of Computer Science and Communication (CSC).
KTH, School of Computer Science and Communication (CSC).
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Förutsägelse av värdepapperens beteende med hjälp av ett artificiellt neuralt nätverk med flera nivåer med extra ingångar mellan skikten (Swedish)
Abstract [en]

This paper discusses the possibilities of predicting changes in stock pricing at a high frequency applying a multi-level neural network without the use of recurrent neurons or any other time series analysis, as suggested in a paper byChen et al. [2017]. The paper tries to adapt the model presented in a paper by Chen et al. [2017] by making the network deeper, feeding it data of higher resolution and changing the activation functions. While the resulting accuracy is not as high as other models, this paper might prove useful for those interested in further developing neural networks using data with high resolution and to the fintech business as a whole.

Place, publisher, year, edition, pages
2017. , 27 p.
Keyword [en]
high frequency, neural network, computer science, stock market, finance, fintech, machine learning, yield, prediction, forecast, deep neural network, algo trading, financial instruments, correlation
National Category
Computer Science
Identifiers
URN: urn:nbn:se:kth:diva-210929OAI: oai:DiVA.org:kth-210929DiVA: diva2:1121353
Educational program
Master of Science in Engineering - Industrial Engineering and Management
Supervisors
Examiners
Available from: 2017-10-27 Created: 2017-07-10 Last updated: 2017-10-27Bibliographically approved

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Bachelor.thesis.Tornqvist.Guan(762 kB)9 downloads
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Type fulltextMimetype application/pdf

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
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