Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques

14 Pages Posted: 22 Jun 2017 Last revised: 28 Jun 2017

See all articles by Vasilios Plakandaras

Vasilios Plakandaras

Democritus University of Thrace

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace

Periklis Gogas

Democritus University of Thrace - Department of Economics

Date Written: June 21, 2017

Abstract

We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States Dollar (USD), USD/Japanese JPY (JPY), Australian Dollar (AUD)/Norwegian Krone (NOK), New Zealand Dollar (NZD)/Brazilian Real (BRL) and South African Rand (ZAR)/Philippine Peso (PHP) exchange rates. After the decomposition of the original exchange rate series with EEMD into a smoothed and a fluctuation component, MARS selects the most informative from the plethora of variables included in our initial data set. The selected variables are fed into two distinctive SVR models for forecasting each component separately one period ahead with the summation providing exchange rate forecasts. The above implementation exhibits superior forecasting ability in exchange rate forecasting and high Sharpe Ratios compared to various models taking data snooping bias in consideration, rejecting the Efficient Market Hypothesis for all foreign exchange markets. Overall the proposed model a) is a combination of empirically proven effective techniques in forecasting time series, b) is data driven, c) relies on minimum initial assumptions and d) provides a structural aspect of the forecasting problem.

Keywords: Exchange rate forecasting, Support Vector Regression, Multivariate Adaptive Regression Splines, variable selection, Ensemble Empirical Mode Decomposition, time series forecasting

JEL Classification: G15

Suggested Citation

Plakandaras, Vasilios and Papadimitriou, Theophilos and Gogas, Periklis, Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques (June 21, 2017). Available at SSRN: https://ssrn.com/abstract=2990344 or http://dx.doi.org/10.2139/ssrn.2990344

Vasilios Plakandaras

Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

Theophilos Papadimitriou

Department of Economics, Democritus University of Thrace ( email )

University Campus
Komotini, 69100
Greece

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

Periklis Gogas (Contact Author)

Democritus University of Thrace - Department of Economics ( email )

Komotini, 69100
Greece

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

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