Abstract: Stock forecasting research has always been a problem that plagued investors.In the past,investors used traditional analysis methods such as K-line diagrams and Yin-Yang lines to predict stock trends.However,with the advancement of science and technology,the development of economic markets,and changes in economic policies,the price trend of a stock is disturbed by various factors.Traditional analysis methods are far from being able to analyze theinformation in volatility of a stock.So prediction accuracy is greatly reduced.In order to improve the prediction accuracy of stock prices,this paper proposes a stock price prediction model based on PCA,LASSO,and LSTM neural networks.Based on the data of the five major categories of technical indicators of Ping An Bank (000001) from 2015 to 2019,the five major categories of indicators are reduced and screened using the PCA and LASSO methods,and the LSTM model is used to predict the closing price of Ping An Bank's stock,compared with the stability and accuracy ofthe previous two methods and using LSTM alone.The experimental results show that the PCA-LSTM model significantly reduces data redundancy and obtains better prediction accuracy than the LASSO-LSTM model and LSTM model.

Key words: LASSO, LSTM, PCA, Ping An bank, Technical index