Economic and Financial Data Analysis System Based on Deep Learning and Neural Network Algorithm

Abstract

Deep learning and neural network methods can analyze and predict various information performance generated by financial markets. This kind of economic and financial analysis can predict and describe the trend, price, risk and other information of financial markets in a more detailed way. In order to solve the shortcomings of the existing economic and financial data analysis and research, this paper discusses the time series model function equation, convolutional neural network and economic and financial data analysis methods, and briefly discusses the test environment, data collection and indicators of the system designed in this paper. In addition, the functions of economic and financial data analysis system are designed and discussed. Finally, deep learning and neural network CNN, LSTM and RNN technologies are applied to the prediction and analysis of stock opening price, closing price, highest price and lowest price for experiments. The experimental data show that the average prediction accuracy of CNN for stock prices reaches 87.33%. The average accuracy of LSTM for stock price prediction reached 87.37%. The average prediction accuracy of RNN for stock prices reaches 97.36, which verifies that the algorithm in this paper has a good performance effect.