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Understanding the characteristics of financial time series through neural network and SVM approaches

Understanding the characteristics of financial time series through neural network and SVM approaches Exchange rate has been always a focal point for researchers within international scope. Globalisation and the role of exchange rate create a challenging market where short-term prediction is concerned. The ability to predict the exchange rate is a challenging topic for professionals and practitioners. This paper proposes a method to address the current issues of predicting the market changes using characteristics of financial time series. The main idea is that neural network and support vector machine (SVM) approaches are employed to train and test the results in different instances. Findings indicate the superiority of correct sets over incorrect, while criteria sets had been sometimes better results. Furthermore, linear kernel was more likely to encounter convergence problems than other types which oppose to primary dataset. Finally, the accuracy of the proposed prediction methods is analysed and compared with related works. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Electronic Finance Inderscience Publishers

Understanding the characteristics of financial time series through neural network and SVM approaches

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd
ISSN
1746-0069
eISSN
1746-0077
DOI
10.1504/IJEF.2019.099045
Publisher site
See Article on Publisher Site

Abstract

Exchange rate has been always a focal point for researchers within international scope. Globalisation and the role of exchange rate create a challenging market where short-term prediction is concerned. The ability to predict the exchange rate is a challenging topic for professionals and practitioners. This paper proposes a method to address the current issues of predicting the market changes using characteristics of financial time series. The main idea is that neural network and support vector machine (SVM) approaches are employed to train and test the results in different instances. Findings indicate the superiority of correct sets over incorrect, while criteria sets had been sometimes better results. Furthermore, linear kernel was more likely to encounter convergence problems than other types which oppose to primary dataset. Finally, the accuracy of the proposed prediction methods is analysed and compared with related works.

Journal

International Journal of Electronic FinanceInderscience Publishers

Published: Jan 1, 2019

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