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European Exchange Trading Funds Trading with Locally Weighted Support Vector Regression

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

European Journal of Operational Research, 2017, vol. 258, issue 1, 372-384

Abstract: 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.

Keywords: Locally Weighted Support Vector Regression; Support Vector Regression; Kernels; Trading; Exchange Traded Funds (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1016/j.ejor.2016.09.005

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