Forecasting stock composite index by fuzzy support vector machines regression
Yu-Kun Bao; Zhi-Tao Liu; Lei Guo; Wen Wang
Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Volume 6, Issue , 18-21 Aug. 2005 Page(s):3535 - 3540 Vol. 6
Digital Object Identifier   10.1109/ICMLC.2005.1527554
Summary:Financial time series forecasting methods such as exponential smoothing are commonly used for prediction on stock composition index (SCI) and have made great contribution in practice, but efforts on looking for superior forecasting method are still made by practitioners and academia. This paper deals with the application of a novel neural network technique, fuzzy support vector machines regression (FSVMR), in SCI forecasting. The objective of this paper is not only to examine the feasibility of FSVMR in SCI forecasting but presents our efforts on improving the accuracy of FSVMR in terms of data pre-processing, kernel function selection and parameters selection. A data set from Shanghai Stock Exchange is used for the experiment to test the validity of FSVMR. The experiment shows FSVMR a better method in SCI forecasting.

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