Modeling and computing of stock index forecasting based on neural network and Markov chain.
The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.
School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China.
Yonghui
Y
Dai
School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China ; Shanghai Financial Information Technology Key Research Laboratory, 777 Guoding Road, Shanghai 200433, China.
Dongmei
D
Han
School of Management, Fudan University, 220 Handan Road, Shanghai 200433, China.
Weihui
W
Dai
D000465:Algorithms; D006801:Humans; D007449:Investments / Q000639:trends; D008390:Markov Chains; D008962:Models, Theoretical; D016571:Neural Networks, Computer