research product . 2020

Using recurrent neural network models and financial news for predicting stock market movements

Suikkanen, Saku;
Open Access English
  • Published: 01 Jan 2020
  • Country: Finland
Abstract
In this work, the utilization of financial news alongside machine learning for predicting stock market movements is examined. The news are handled with various natural language processing methods for finding correlation between the derived attributes and stock market movements. The novelty of this work lies in the application of BNS and LDA methods as well as 2-word combinations alongside with LSTM neural network. The main point of the work is to examine the usefulness of the results achieved with the formerly mentioned methods and neural networks as well as comparing the results with market efficiency. In the research it was concluded that the models containing...
Subjects
free text keywords: neural network, stock markets, natural language processing, neuroverkko, osakemarkkinat, luonnollisen kielen käsittely, fi=Datatiede|en=Data science|, fi=School of Engineering Science, Tietotekniikka|en=School of Engineering Science, Computer Science|
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Digital Humanities and Cultural Heritage
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2020
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