Stock price prediction model based on machine learning

This paper reviews the problem of stock price prediction and discusses the current application status of traditional statistical models, machine learning and deep learning methods in financial data processing. By analyzing the limitations of traditional methods such as ARIMA and regression models in capturing market nonlinear characteristics and noise processing, it is pointed out that these methods have deviations in actual predictions; while models such as support vector regression, random forest, and LSTM can automatically extract features using historical data to improve prediction accuracy. The article introduces in detail the improvement ideas based on Bayesian optimization and hybrid models, and discusses how to use attention mechanisms, multimodal data fusion, and real-time dynamic update mechanisms to further improve the stock price prediction system. Finally, the shortcomings of existing methods in terms of data quality, model interpretability, and cross-market adaptability are analyzed, and the future development direction of combining cutting-edge technologies such as reinforcement learning is prospected to provide theoretical support and practical guidance for financial decision-making and risk management.