The Forecasting Model of Stock Price Based on PCA and BP Neural Network

Abstract

Based on Principal Component Analysis (PCA) and Back Propagation neural network, this paper establishes stock forecast model, and takes the Yunnan Baiyao (000538) as example, 29 indicators are selected from stocks technical analysis, and the neural network is input after dimension reduction and further confirms number of hidden layer nodes, learning rate, activation function and training function of the network in accordance with comparison and analysis of Mean Square Error (MSE) and Mean Absolute Error (MAE) in different parameter data experiments. Lastly, the model with steadiness and accuracy is obtained.