Research on Application of Shanghai Stock Index Prediction Model Based on PSO-BP Neural Network

• 1、Finance and Statistics School,Hunan University,Changsha 410006

Abstract：With the rapid development of the national economy and the continuous improvement of the market economy, stock investment has become an important part of modern people\'s investment and financial management. The fluctuation of the stock price directly affects the stability of the stock market and the healthy development of finance and economy. Successful forecasting of stock prices and trends helps investors to make profits, and it also helps government departments provide timely and reasonable market guidance and supervision. Therefore, how to accurately predict the stock market is an important and valuable issue. Nowadays, a large number of artificial intelligence and machine learning technologies have been used in the prediction of the stock market, and back propagation neural network (BPNN) is one of the most widely used technologies in intelligent prediction. However, BP neural network has the disadvantages of strong local search ability and weak global search ability. Therefore, this paper adds a particle swarm optimization (PSO) algorithm with strong global search capability and proposes a hybrid algorithm that uses PSO algorithm to train the initial weights of BP neural network. It is called PSO-BP neural. Network algorithm.This paper uses PSO-BP neural network to empirically analyze the closing price of the Shanghai Stock Exchange Index, discusses the selection of sample indexes and data, the topology structure of the network, the number of hidden layer nodes, and the selection of activation functions. At the same time, we also give GA-BP neural network algorithms that are also intelligent hybrid algorithms and compare them.The experimental results show that the mixed PSO-BP neural network algorithm is superior to the GA-BP neural network algorithm and BP neural network algorithm in the simulation and prediction effect, and can accurately predict the closing price of the Shanghai index in the near future. In the future, the model has a good application prospect.

Keywords： BP neural network PSO algorithm stock prediction