Abstract:The current stock price forecast is a hot issue in research. People are paying more and more attention to the establishment of stock price forecasting model, and improving the accuracy of stock price forecast has practical application value for stock investors. At present, the forecasting methods of stock prices are endless, among which the typical ones are traditional technical analysis and ARMA models. In order to improve the accuracy of prediction and consider the nonlinearity of stock market, this study proposes an improved stock price forecasting model of echo state neural network. The improved particle is applied to the characteristics of Echo State Neural Network (ESN). The group algorithm (GTPSO) searches the output connection weight of the ESN, and finally obtains the optimal solution, i.e., the optimal output connection weight of the ESN. The GTPSO algorithm is generally in the traditional Particle Swarm Optimization (PSO) algorithm. Based on the idea of taboo in the Tabu Search algorithm (TS) and the idea of mutation in the Genetic Algorithm (GA), the PSO is reduced to a local minimum during the learning process, and the ability of the PSO to search globally is improved. The forecasting model is used in the daily closing price forecast of individual stocks, and the closing price of the 11th day is predicted using the closing price of every 10 days. The correctness of the model is verified by experiments, and it is proved that the model has a good prediction effect.