Research on Stock Price Prediction of BP Neural Network Based on Factor Analysis

Abstract

In this thesis, the real estate industry is focused on, the financial data of 27 listed real estate companies in China is chosen, and the method of factor analysis is used to carry out the dimensionality reduction processing for the collected 20 variables. Finally, six main factors are extracted. Besides, the stock prices before and after the factor analysis are predicted and studied respectively based on the BP neural network prediction model. In line with the results, the better prediction accuracy for the BP neural network model established with the main factor determined by factor analysis as the independent variable and the stock price as the dependent variable is embodied. In contrast with the research carried out by other scholars, the BP neural network is established from more dimensions, so the prediction accuracy is greatly enhanced.