Predicting stock price trends by machine learning of listed companies on the Ho Chi Minh City Stock Exchange

Abstract

This research explores the potential of machine learning techniques to forecast stock price trends of entities on the Ho Chi Minh City Stock Exchange, focusing on non-banking, insurance, and securities sectors. The study spans seven years, from 2015 to 2022, scrutinizing historical stock data. By implementing advanced machine learning algorithms like Support Vector Classification, Logistic Regression, and Random Forest, the research aims to determine the most effective method for accurate trend prediction. The findings are significant, revealing that the Random Forest algorithm outperforms others, offering a balanced approach in precision and recall rates. This insight is crucial for investors and financial analysts in making informed decisions, especially in the context of a developing and dynamic market like Vietnam. The research underscores the power of machine learning in financial forecasting, highlighting its potential to revolutionize investment strategies. The study's conclusion emphasizes the importance of integrating machine learning tools, particularly Random Forest, in financial analysis and decision-making processes. This research not only offers a practical tool for investors but also contributes significantly to the academic literature on financial market predictions using machine learning methodologies.