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A Comparative Analysis of Multiple Linear Regression Models and Neural Networks for Stock Price Prediction - Take BYD as an Example

In the context of global efforts to combat the climate warming, energy saving and emission reduction, China has also put forward the goal of achieving carbon peak by 2030 and carbon neutrality by 2060, and the new energy industry has received key support and development, and is also favored by capital, so it is important to analyze and study the factors influencing stock prices, both for the industry and for investors. BYD is the leading company in the new energy industry, it is typical. Therefore, this paper chooses to take BYD as an example to analyze and study the influencing factors of stock price through BP neural network and multiple linear regression model, and finally finds that the company's stock price has a statistically significant correlation with USD/CNH exchange rate, CPI, P/E ratio and Dow Jones Industrial Index. The BP neural network has better explanation ability than the multiple linear regression model.