Financial Data Trend Prediction Through Deep Learning Model

Abstract

This paper discusses the application of deep learning technology in financial data prediction. First, the background of deep learning and its wide application in various fields are introduced, with special emphasis on its advantages in dealing with complex nonlinear relationships and high-dimensional data. Then, the characteristics of financial data, including randomness, high noise, low signal-to-noise ratio, non-stationarity and nonlinearity, are described in detail, and the limitations of traditional time series models such as AR, ARMA, and ARIMA in processing these data are analyzed. Then, the structure and working principle of BP neural network and recurrent neural network (RNN), especially long short-term memory network (LSTM), are introduced, and their advantages in capturing long-term dependence of time series data are explained. Finally, through the examples of stock price prediction, market risk management and portfolio optimization, the practical application of deep learning model in financial data prediction and its remarkable effect are demonstrated. This paper aims to provide new ideas and tools for financial market analysis and prove the potential and effectiveness of deep learning models in the financial field.