Forecasting stock composite index by fuzzy support vector machines regression
Yu-Kun Bao
Zhi-Tao Liu
Lei Guo
Wen Wang
Sch. of Manage., Huazhong Univ. of Sci. & Technol., Wuhan, China;
This paper appears in: Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on
Publication Date: 18-21 Aug. 2005
Volume: 6,
On page(s): 3535-3540 Vol. 6
Location: Guangzhou, China,
ISBN: 0-7803-9091-1
INSPEC Accession Number: 8747489
Digital Object Identifier: 10.1109/ICMLC.2005.1527554
Current Version Published: 2005-11-07
Abstract
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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