A Bayesian Financial Panel Data Model Based on Neural Network Algorithm

With the development and progress of science and technology, financial market forecasting needs to use models to predict the trend of stock prices, and improve the accuracy of stock price forecasts through model construction. Neural network algorithms are widely and effectively used in finance due to their strong self-learning ability. The modeling and forecasting of models is very popular in stock market analysis. Therefore, this paper builds and analyzes financial panel data models based on neural network algorithms. This paper is mainly divided into three parts: the first is a brief introduction to the data mining process and the difficulty of BP neural network in stock price forecasting, followed by the model construction, and finally the model is analyzed.