Wang, Y.H. (2009) Nonlinear Neural Network Forecasting Model for Stock Index Option Price: Hybrid GJR-GARCH Approach. Expert Systems with Applications, 36, 564-570.
https://doi.org/10.1016/j.eswa.2007.09.056

has been cited by the following article:

• TITLE: Comparative Study on Forecasting Model for Stock Index Future Price

  AUTHORS: Wenbo Wang, He Nie

  KEYWORDS: Forecasting Model, Stock Index Future Price, Wavelet Analysis, PSO, Neural Networks

  JOURNAL NAME: Modern Economy, Vol.9 No.4, April 26, 2018

  ABSTRACT: The wavelet neural network has been widely applied in the forecasting field. However, due to its disadvantages of local minimal and slow convergence speed, its forecasting precision is limited in some extent. A forecasting model for stock index future price based on wavelet analysis and the improved PSO-based neural network would be put forward in this essay using the improved PSO-based algorithm to optimize the neural network. By using the Shanghai and Shenzhen 300 stock index futures’ statistics as the samples, the sym 8 wavelet transformation techniques to denoise the statistics and then use the denoised stats to train and test the improved PSO-based neural networks. The study results show that the model in this essay could efficiently improve the forecasting quality of the stock index futures price.