Stock price analysis and forecasting based on machine learning

With the rapid development of the global economy and the stock market, stock investment has become one of the most commonly used financial management methods, and more and more investors have begun to enter the stock market. At the same time, the high risk of stocks has also become a problem faced by many investors who are new to the market. Traditional stock price research methods mainly use technical indicator analysis or time series analysis, which has the problem of not being able to fit nonlinear data well, while machine learning methods can handle the randomness, chaos and nonlinearity of stock prices well. Therefore, this paper chooses machine learning algorithms as methods for stock price prediction, namely neural networks and support vector machines. After feature engineering processing of historical stock data, relevant machine learning algorithm models are established to predict stock prices, and the analysis and prediction results are presented in a visual way.