Mathematical model of back propagation for stock price forecasting

Abstract

Abstract In order to establish a more accurate Stock Price Prediction Model, the Stock Price Prediction mathematical Model SPPM (Stock Price Prediction Model) based on BP neural network with high frequency data is proposed in this paper. The SPPM integrates several neural networks to predict the movement of stock prices over the next few days. The key problems in SPPM—such as data preprocessing, output fusion and the selection of nodes in the hidden layer of neural network—are discussed in detail. The experimental results show that the SPPM predicted the closing price of 2019-03-19 and 2019-03-20 as 207.16 and 207.22, respectively, which is very close to the actual observed value, and the back propagation mathematical model SPPM has a certain practical value. Our conclusion is that the back propagation model can predict the stock price with high accuracy.