Stock Trend Prediction and Analysis Based on Machine Learning

Abstract

Stock trend prediction is a critically important activity in the financial sector. Investors aim to gain a better understanding of market dynamics, make wiser investment decisions, and manage investment risks more effectively by forecasting stock price trends. This is the focus of our upcoming research. This paper aims to utilize machine learning methods such as Support Vector Machines (SVM), based on spatial interval maximization strategies, Random Forest, based on multi-decision tree parallel learning strategies, and eXtreme Gradient Boosting (XGBoost), based on classification functions, to devise accurate classification function strategies for predicting and analyzing fluctuations in Dow Jones stock prices. Principal Component Analysis (PCA) will then be used to select some suitable features, identifying key factors with the greatest impact and minimal inter-correlation related to stock prices. Through a combination of various classification models, the research aims to forecast the future rise and fall of Dow Jones stocks. It has been observed that performing feature curation through PCA in advance enhances the predictive accuracy of different machine learning methods. Notably, the combination of PCA and XGBoost achieves the highest predictive rate. This approach provides a robust and scientifically grounded method for future stock price predictions and the formulation of trading strategies.