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Mathematical Problems in Engineering
Volume 2013, Article ID 436795, 11 pages
Research Article

Volatility Degree Forecasting of Stock Market by Stochastic Time Strength Neural Network

Institute of Financial Mathematics and Financial Engineering, School of Science, Beijing Jiaotong University, Beijing 100044, China

Received 18 April 2013; Revised 2 September 2013; Accepted 2 September 2013

Academic Editor: Wei-Chiang Hong

Copyright © 2013 Haiyan Mo and Jun Wang. 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.


In view of the applications of artificial neural networks in economic and financial forecasting, a stochastic time strength function is introduced in the backpropagation neural network model to predict the fluctuations of stock price changes. In this model, stochastic time strength function gives a weight for each historical datum and makes the model have the effect of random movement, and then we investigate and forecast the behavior of volatility degrees of returns for the Chinese stock market indexes and some global market indexes. The empirical research is performed in testing the prediction effect of SSE, SZSE, HSI, DJIA, IXIC, and S&P 500 with different selected volatility degrees in the established model.