Research on Stock Market Prediction Methods Based on Deep Learning

With the continuous increase in the complexity of the financial market, traditional stock market prediction methods face many challenges when dealing with nonlinear and high-dimensional data. Deep learning, with its advantages of automatic feature extraction, powerful nonlinear modeling ability and adaptive learning, has become a research hotspot in the field of stock market prediction. This paper proposes a stock market trend prediction model based on generative adversarial networks. By comparing with other control models, the results show that this model can give full play to the prediction advantage of the Bi-LSTM network for time series and the extraction advantage of the 1 DCNN network for features. The prediction error is reduced by 6 % compared with other models. It can accurately predict the trend changes of stocks in the future. The research of this article provides important support for investors' decision-making and the risk management of financial institutions.