Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate
AbstractIn this paper, we present a novel machine learning based forecasting system of the EU/USD exchange rate directional changes. Specifically, we feed an overcomplete variable set to a Support Vector Machines (SVM) model and refine it through a Sensitivity Analysis process. The dataset spans from 1/1/1999 to 30/11/2011; the data of the last 7 months are reserved for out-of-sample testing. Results show that the proposed scheme outperforms various other machine learning methods treating similar scenarios.
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Bibliographic InfoPaper provided by Democritus University of Thrace, Department of Economics in its series DUTH Research Papers in Economics with number 5-2012.
Length: 19 pages
Date of creation: 27 Jan 2012
Date of revision:
Contact details of provider:
Postal: Department of Economics, University Campus, Komotini, 69100, Greece
Phone: (25310) 39.503
Fax: (25310) 39.502
Web page: http://www.econ.duth.gr/
More information through EDIRC
Machine Learning; Support Vector Machines; Exchange Rates; Forecasting;
Find related papers by JEL classification:
- C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
- C59 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Other
- F31 - International Economics - - International Finance - - - Foreign Exchange
- G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-10-13 (All new papers)
- NEP-EEC-2012-10-13 (European Economics)
- NEP-ETS-2012-10-13 (Econometric Time Series)
- NEP-FOR-2012-10-13 (Forecasting)
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