Application of Machine Learning Algorithm based on Principal Component Analysis in Stock Data Prediction

With the development of big data technology, machine learning is more and more widely used in the financial field, especially in the stock market prediction. Through machine learning algorithms, it is possible to predict the direction of the stock price based on the historical data of the stock. But the stock market is a complex multi-factor system with many indicators. Therefore, in this study, principal component analysis (PCA) was used to reduce the dimensionality of complex stock data. Then, the data after dimensionality reduction are used to train and test the support vector machine (SVM) and backpropagation (BP) neural network models, and the prediction effect of the two models is compared. The experiment shows that when the learning rate is 0.01, the combination of PCA and BP neural network has the highest prediction accuracy.