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Sequence-to-sequence learning of financial time series in algorithmic trading
University of Borås, Faculty of Librarianship, Information, Education and IT.
University of Borås, Faculty of Librarianship, Information, Education and IT.
2017 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Sekvens-till-sekvens-inlärning av finansiella tidsserier inom algoritmiskhandel (Swedish)
Abstract [en]

Predicting the behavior of financial markets is largely an unsolved problem. The problem hasbeen approached with many different methods ranging from binary logic, statisticalcalculations and genetic algorithms. In this thesis, the problem is approached with a machinelearning method, namely the Long Short-Term Memory (LSTM) variant of Recurrent NeuralNetworks (RNNs). Recurrent neural networks are artificial neural networks (ANNs)—amachine learning algorithm mimicking the neural processing of the mammalian nervoussystem—specifically designed for time series sequences. The thesis investigates the capabilityof the LSTM in modeling financial market behavior as well as compare it to the traditionalRNN, evaluating their performances using various measures.

Abstract [sv]

Prediktion av den finansiella marknadens beteende är i stort ett olöst problem. Problemet hartagits an på flera sätt med olika metoder så som binär logik, statistiska uträkningar ochgenetiska algoritmer. I den här uppsatsen kommer problemet undersökas medmaskininlärning, mer specifikt Long Short-Term Memory (LSTM), en variant av rekurrentaneurala nätverk (RNN). Rekurrenta neurala nätverk är en typ av artificiellt neuralt nätverk(ANN), en maskininlärningsalgoritm som ska efterlikna de neurala processerna hos däggdjursnervsystem, specifikt utformat för tidsserier. I uppsatsen undersöks kapaciteten hos ett LSTMatt modellera finansmarknadens beteenden och jämförs den mot ett traditionellt RNN, merspecifikt mäts deras effektivitet på olika vis.

Place, publisher, year, edition, pages
2017.
Keyword [en]
deep learning, machine learning, quantitative finance, algorithmic trading, blackbox trading, lstm, rnn, time series forecasting, prediction, tensorflow, keras, forex, neural network, econometrics
Keyword [sv]
finans, algoritmisk handel, tidsserier, prediktion, maskininlärning, forex, neurala nätverk, tensorflow, keras, kvantitativ finans, lstm, rnn, ekonometri
National Category
Information Systems
Identifiers
URN: urn:nbn:se:hb:diva-12602OAI: oai:DiVA.org:hb-12602DiVA: diva2:1142444
Subject / course
Informatics
Supervisors
Examiners
Available from: 2017-09-21 Created: 2017-09-19 Last updated: 2017-09-21Bibliographically approved

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CiteExportLink to record
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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