Skip to main content
Article thumbnail
Location of Repository

European exchange trading funds trading with locally weighted support vector regression

By Georgios Sermpinis, Charalampos Stasinakis, Rafael Rosillo and David de la Fuente


In this paper, two different Locally Weighted Support Vector Regression (wSVR) algorithms are generated and applied to the task of forecasting and trading five European Exchange Traded Funds. The trading application covers the recent European Monetary Union debt crisis. The performance of the proposed models is benchmarked against traditional Support Vector Regression (SVR) models. The Radial Basis Function, the Wavelet and the Mahalanobis kernel are explored and tested as SVR kernels. Finally, a novel statistical SVR input selection procedure is introduced based on a principal component analysis and the Hansen, Lunde, and Nason (2011) model confidence test. The results demonstrate the superiority of the wSVR models over the traditional SVRs and of the v-SVR over the ε-SVR algorithms. We note that the performance of all models varies and considerably deteriorates in the peak of the debt crisis. In terms of the kernels, our results do not confirm the belief that the Radial Basis Function is the optimum choice for financial series

Publisher: Elsevier
Year: 2017
OAI identifier:
Provided by: Enlighten

Suggested articles

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.