Stock Price Prediction Based on BP Neural Network and ARIMA Model

With the progress and development of society, stock investment has become an important part of people's daily lives. High risk and high return are the characteristics of stock investment. Individual investors and institutional investors always pay attention to stock market trends, analyze financial data, and try to predict the development trend of stocks. Studying the future trend of stock prices of listed companies and predicting the stock price of a certain company not only has extremely attractive application value, but also has significant theoretical significance, attracting the attention of investors and academia. The stock market is unpredictable and an extremely complex system with too many influencing factors. It will be very difficult to thoroughly understand the mechanism of stock market changes theoretically. This study investigates the application of time series prediction technology in stock price prediction, proposes stock price prediction models based on ARIMA and BP neural network, and verifies the feasibility and effectiveness of the model prediction through prediction.