An analysis of machine learning's role in stock price prediction

The volatility and uncertainty of the stock market can reflect the economic trends of today's society. With the improvement of computing power and the development of data processing, it has been found that machine learning can improve the accuracy, flexibility and interpretability of stock price predictions. To conduct an in-depth study, this paper introduces three methods in machine learning: neural networks, support vector machines and random forest. These three algorithms can predict the stock market from multiple perspectives, complementing each others weaknesses. The results show that, while neural networks tend to overfit, random forests are resistant to overfitting. By combining neural networks with random forests, researchers can enhance prediction accuracy. Additionally, the random forest algorithm has strong data processing capabilities, and it can effectively address the issue of low computational efficiency in the support vector machine algorithm.