Stock price forecasting based on LLE-BP neural network model

Most of the factors affecting stock prices have data redundancy and nonlinear characteristics. Classical linear mapping dimensional reduction methods such as principal component analysis (PCA) and linear discriminant analysis (LDA) cannot get good results for nonlinear problems. In this paper, a local linear embedding dimensional reduction algorithm (LLE) is selected to reduce the dimension of the factors affecting the stock price. The data after dimensional reduction is used as the new input variable of Back Propagation (BP) neural network to realize the stock price prediction. The prediction results are compared with the BP neural network model, PCA-BP model, and the traditional ARIMA (3,1,1) model. The results show that LLE-BP neural network model has higher prediction accuracy in stock price prediction, and it is an effective and feasible stock price prediction method. (C) 2020 Elsevier B.V. All rights reserved.