基于近邻互信息的SVM-GARCH股票价格预测模型研究

doi:10.16381/j.cnki.issn1003-207x.2016.09.002

Abstract

Abstract: In order to overcome the limitations of the traditional linear model in dealing with the nonlinearity in time series, a novel SVM-GARCH forecasting model is proposed based on the neighborhood mutual information. By constructing high dimensional input variables, the proposed nonlinear model not only absorbs the historical information in the time series data but also incorporates the stock market information in different regions through feature selection by the neighborhood mutual information. Empirical studies demonstrate that the proposed model is superior to the traditional linear ARMA-GARCH model in terms of data denosing, trend discrimination and prediction accuracy etc.

Key words: stock price forecasting, SVM-GARCH model, neighborhood mutual information

CLC Number:

F224.12

ZHANG Gui-sheng, ZHANG Xin-dong. A SVM-GARCH Model for Stock Price Forecasting Based on Neighborhood Mutual Information[J]. Chinese Journal of Management Science, 2016, 24(9): 11-20.

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A SVM-GARCH Model for Stock Price Forecasting Based on Neighborhood Mutual Information

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