Stock value and currency exchange rate prediction using an artificial neural network trained by a genetic algorithm

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27510%2F19%3A10242772" target="_blank" >RIV/61989100:27510/19:10242772 - isvavai.cz</a>
Result on the web
<a href="https://www.ekf.vsb.cz/smsis/en" target="_blank" >https://www.ekf.vsb.cz/smsis/en</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Stock value and currency exchange rate prediction using an artificial neural network trained by a genetic algorithm
Original language description
Prediction of a stock value or a currency exchange rate is a complex problem which has benefited from recent advancements and research in machine learning. Very popular model for the predictions are deep neural networks. This paper discusses two training algorithms for the feedforward neural networks the backpropagation algorithm and a genetic algorithm. Although the backpropagation algorithm is a reliable way to train a neural network, it can be very demanding on computational resources for lager datasets which are common in some types of trading. Heuristics like the genetic algorithms can help to lower the demands of the training process. The discussed genetic algorithm is an implementation of a classical genetic algorithm with Rank selection of parents for crossover and genes represented as a real number. The accuracy of predictions made by the network trained using this algorithm is then compared to the network trained by backpropagation. (C) 2019 VSB-Technical University of Ostrava. All rights reserved.
Czech name
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Czech description
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Publication year
2019
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings of the 13th International Conference on Strategic Management and its Support by Information Systems: May 21th-22th, 2019, Ostrava, Czech Republic
ISBN
978-80-248-4305-6
ISSN
2570-5776
e-ISSN
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Number of pages
10
Pages from-to
348-357
Publisher name
VŠB - Technical University of Ostrava
Place of publication
Ostrava
Event location
Ostrava
Event date
May 21, 2019
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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