- Title
- Foreign exchange trading with support vector machines
- Creator
- Ullrich, Christian; Seese, Detlef; Chalup, Stephan
- Relation
- 30th Annual Conference of the Gesellschaft für Klassifikation e.V.. Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation e.V., Freie Universität Berlin, March 8–10, 2006, Part VII (Berlin, Germany 8–10 March, 2006) p. 539-546
- Publisher Link
- http://dx.doi.org/10.1007/978-3-540-70981-7_62
- Publisher
- Springer
- Resource Type
- conference paper
- Date
- 2007
- Description
- This paper analyzes and examines the general ability of Support Vector Machine (SVM) models to correctly predict and trade daily EUR exchange rate directions. Seven models with varying kernel functions are considered. Each SVM model is benchmarked against traditional forecasting techniques in order to ascertain its potential value as out-of-sample forecasting and quantitative trading tool. It is found that hyperbolic SVMs perform well in terms of forecasting accuracy and trading results via a simulated strategy. This supports the idea that SVMs are promising learning systems for coping with nonlinear classification tasks in the field of financial time series applications.
- Subject
- support vector machines; statistical theory; data mining; statistics
- Identifier
- http://hdl.handle.net/1959.13/1303158
- Identifier
- uon:20623
- Identifier
- ISBN:9783540709800
- Rights
- The original publication is available at www.springerlink.com
- Language
- eng
- Full Text
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