Directional forecasting in financial time series using support vector machines: The USD/Euro exchange rate
Vasilios Plakandaras (),
Theophilos Papadimitriou () and
Periklis Gogas ()
No 5-2012, DUTH Research Papers in Economics from Democritus University of Thrace, Department of Economics
In 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.
Keywords: Machine Learning; Support Vector Machines; Exchange Rates; Forecasting (search for similar items in EconPapers)
JEL-codes: C52 C59 F31 G17 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-eec, nep-ets and nep-for
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Persistent link: https://EconPapers.repec.org/RePEc:ris:duthrp:2012_005
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