Please use this identifier to cite or link to this item: https://doi.org/10.1016/S0305-0483(01)00026-3
Title: Application of support vector machines in financial time series forecasting
Authors: Tay, F.E.H. 
Cao, L.
Keywords: BP neural network
Generalization
Structural risk minimization principle
Support vector machines
Issue Date: Aug-2001
Source: Tay, F.E.H., Cao, L. (2001-08). Application of support vector machines in financial time series forecasting. Omega 29 (4) : 309-317. ScholarBank@NUS Repository. https://doi.org/10.1016/S0305-0483(01)00026-3
Abstract: This paper deals with the application of a novel neural network technique, support vector machine (SVM), in financial time series forecasting. The objective of this paper is to examine the feasibility of SVM in financial time series forecasting by comparing it with a multi-layer back-propagation (BP) neural network. Five real futures contracts that are collated from the Chicago Mercantile Market are used as the data sets. The experiment shows that SVM outperforms the BP neural network based on the criteria of normalized mean square error (NMSE), mean absolute error (MAE), directional symmetry (DS) and weighted directional symmetry (WDS). Since there is no structured way to choose the free parameters of SVMs, the variability in performance with respect to the free parameters is investigated in this study. Analysis of the experimental results proved that it is advantageous to apply SVMs to forecast financial time series. © 2001 Elsevier Science Ltd.
Source Title: Omega
URI: http://scholarbank.nus.edu.sg/handle/10635/59570
ISSN: 03050483
DOI: 10.1016/S0305-0483(01)00026-3
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