Stock Market Trend Forecast Algorithm Based on Energy Computational Model of Bayesian Networks
ZHANG Run-Mei1,2, HU Xue-Gang1, WANG Hao1, YAO Hong-Liang1
1.School of Computer and Information, Hefei University of Technology, Hefei 230009 2.School of Electronic and Information Engineering, Anhui Jianzhu University, Hefei 230022
Abstract There are inconsistencies between the technical indexes and the trend of stock market, and therefore the trend of stock market is difficult to predict. Through the energy characteristics extraction and the feature fusion of technical indexes, a stock market trend forecast algorithm based on energy calculation of Bayesian networks (E-STF) is proposed. Firstly, the trend information inside technical indexes is extracted from the point of energy, the energy calculation model of technical indexes is designed and its probability distribution is given. Then, the inconsistency of energy distribution between technical indexes is analyzed. Next, Bayesian networks are used to fuse the features of technical indexes. The time-sharing state feature energy is introduced and fused with technical indexes energy to build the stock market trend structure model. Finally, based on the conditional probability function between the stock market trend and some relative characteristic energy, energy constraints relationship is introduced into support vector machine to predict the stock market trend. Through comparison and analysis on the Shanghai Stock Exchange indexes in recent 3 years, the experimental results show that the prediction accuracy is improved effectively by E-STF algorithm.
ZHANG Run-Mei,HU Xue-Gang,WANG Hao等. Stock Market Trend Forecast Algorithm Based on Energy Computational Model of Bayesian Networks[J]. , 2015, 28(12): 1137-1146.
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2015, Vol. 28 
