Abstract
The stock and foreign exchange markets are the two fundamental financial markets in the world and play acrucial role in international business. This paper examines the possibility of predicting the foreign exchangemarket via machine learning techniques, taking the stock market into account. We compare prediction modelsbased on algorithms from the fields of shallow and deep learning. Our models of foreign exchange marketsbased on information from the stock market have been shown to be able to predict the future of foreignexchange markets with an accuracy of over 60%. This can be seen as an indicator of a strong link between thetwo markets. Our insights offer a chance of a better understanding guiding the future of market predictions.We found the accuracy depends on the time frame of the forecast and the algorithms used, where deeplearning tends to perform better for farther-reaching forecasts
Original language | English |
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Title of host publication | PEFnet 2017 |
Subtitle of host publication | Proceedings |
Editors | Jana Stávková |
Publisher | Mendel University Press |
Pages | 7-13 |
ISBN (Electronic) | 978-80-7509-555-8 |
Publication status | Published - 12 Jun 2018 |
Event | 21st European Scientific Conference of Doctoral Students - Brno, Czech Republic Duration: 30 Nov 2017 → … |
Conference
Conference | 21st European Scientific Conference of Doctoral Students |
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Abbreviated title | PEFnet 2017 |
Country/Territory | Czech Republic |
City | Brno |
Period | 30/11/17 → … |