Directional Forecasting in Financial Time Series Using Support Vector Machines: The USD/EURO Exchange Rate

15 Pages Posted: 31 Jan 2014

See all articles by Vasilios Plakandaras

Vasilios Plakandaras

Democritus University of Thrace

Periklis Gogas

Democritus University of Thrace - Department of Economics

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Date Written: November 10, 2011

Abstract

In this paper, we present a novel machine learning based forecasting system of the EUR/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.

Suggested Citation

Plakandaras, Vasilios and Gogas, Periklis and Papadimitriou, Theophilos, Directional Forecasting in Financial Time Series Using Support Vector Machines: The USD/EURO Exchange Rate (November 10, 2011). Available at SSRN: https://ssrn.com/abstract=2387655 or http://dx.doi.org/10.2139/ssrn.2387655

Vasilios Plakandaras (Contact Author)

Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

Periklis Gogas

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

HOME PAGE: http://www.econ.duth.gr/personel/dep/gkogkas/index.en.shtml

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

HOME PAGE: http://econ.duth.gr/author/papadimi/

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