Study on Forecasting Stock Prices Using Machine Learning Techniques: A Comparative Evaluation of Random Forest, Support Vector Machines, and Back Propagation Neural Network

With the rapid development of big data technology, the application of machine learning algorithms is more and more extensive, especially in the field of real data optimization. This study takes random forest, support vector machine (SVM) and backpropagation neural network (BPNN) as examples, and compares the performance of these three typical machine algorithms in stock price through in-depth experimental comparison and analysis. The experimental verification results show that although each algorithm has its unique advantages and high prediction performance, compared with the other two algorithms, BPNN is relatively excellent in various evaluation indicators, with relatively high prediction accuracy and relatively low error rate.