%0 Journal Article %A DENG Jiali %A ZHAO Fengqun %A WANG Xiaoxia %T MTICA-AEO-SVR Model for Stock Price Forecasting %D 2022 %R 10.3778/j.issn.1002-8331.2108-0433 %J Computer Engineering and Applications %P 257-263 %V 58 %N 8 %X In order to improve the stability and separation efficiency of traditional Fast ICA algorithm, a new nonlinear function based on Tukey M estimation is constructed in this paper, and then a MTICA algorithm is obtained. Furthermore, a novel MTICA-AEO-SVR model for stock price forecasting is established combining MTICA and SVR algorithms. Firstly, the original stock data is decomposed into independent components by MTICA algorithm for sorting and denoising, and then different SVR models are selected to predict the independent components and the stock price respectively. Artificial ecosystem optimization is introduced into the SVR algorithm to select parameters, as to improve the model prediction accuracy. The empirical results of the Shanghai B-share index show that MTICA-AEO-SVR model is more accurate and efficient than ICA-AEO-SVR model and ICA-SVR model in stock price prediction. %U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.2108-0433