Daily Prediction of Foreign Exchange Rates Based on the Stock Marke

Bohdan Andrusyak, Thomas Kugi, Roman Kern

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandBegutachtung

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
Originalspracheenglisch
TitelPEFnet 2017
UntertitelProceedings
Redakteure/-innenJana Stávková
Herausgeber (Verlag)Mendel University Press
Seiten7-13
ISBN (elektronisch)978-80-7509-555-8
PublikationsstatusVeröffentlicht - 12 Juni 2018
Veranstaltung21st European Scientific Conference of Doctoral Students - Brno, Tschechische Republik
Dauer: 30 Nov. 2017 → …

Konferenz

Konferenz21st European Scientific Conference of Doctoral Students
KurztitelPEFnet 2017
Land/GebietTschechische Republik
OrtBrno
Zeitraum30/11/17 → …

Fingerprint

Untersuchen Sie die Forschungsthemen von „Daily Prediction of Foreign Exchange Rates Based on the Stock Marke“. Zusammen bilden sie einen einzigartigen Fingerprint.

Dieses zitieren