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The Scientific World Journal
Volume 2014, Article ID 124523, 9 pages
Research Article

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

1School of Information Management and Engineering, Shanghai University of Finance and Economics, 777 Guoding Road, Shanghai 200433, China
2Shanghai Financial Information Technology Key Research Laboratory, 777 Guoding Road, Shanghai 200433, China
3School of Management, Fudan University, 220 Handan Road, Shanghai 200433, China

Received 30 August 2013; Accepted 10 March 2014; Published 23 March 2014

Academic Editors: J. Shu and F. Yu

Copyright © 2014 Yonghui Dai et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.