Premium
Forecasting Daily and Monthly Exchange Rates with Machine Learning Techniques
Author(s) -
Plakandaras Vasilios,
Papadimitriou Theophilos,
Gogas Periklis
Publication year - 2015
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/for.2354
Subject(s) - hilbert–huang transform , computer science , support vector machine , artificial neural network , multivariate adaptive regression splines , artificial intelligence , econometrics , machine learning , exponential smoothing , test set , multivariate statistics , regression , variable (mathematics) , foreign exchange market , exchange rate , regression analysis , statistics , bayesian multivariate linear regression , mathematics , economics , mathematical analysis , filter (signal processing) , computer vision , macroeconomics
In this paper we propose and test a forecasting model on monthly and daily spot prices of five selected exchange rates. In doing so, we combine a novel smoothing technique (initially applied in signal processing) with a variable selection methodology and two regression estimation methodologies from the field of machine learning (ML). After the decomposition of the original exchange rate series using an ensemble empirical mode decomposition (EEMD) method into a smoothed and a fluctuation component, multivariate adaptive regression splines (MARS) are used to select the most appropriate variable set from a large set of explanatory variables that we collected. The selected variables are then fed into two distinctive support vector machines (SVR) models that produce one‐period‐ahead forecasts for the two components. Neural networks (NN) are also considered as an alternative to SVR. The sum of the two forecast components is the final forecast of the proposed scheme. We show that the above implementation exhibits a superior in‐sample and out‐of‐sample forecasting ability when compared to alternative forecasting models. The empirical results provide evidence against the efficient market hypothesis for the selected foreign exchange markets. Copyright © 2015 John Wiley & Sons, Ltd.