Stock price forecasting is a critical task in financial analytics, aiming to predict future price movements and assist investors in making informed decisions. Traditional statistical approaches often struggle to capture the complex, nonlinear, and dynamic nature of financial markets. Supervised learning methods, a subset of machine learning, have shown significant potential in modeling these intricate relationships by learning from historical data. This paper reviews various supervised learning techniques applied in stock price trend forecasting, including Linear Regression, Support Vector Machines (SVM), Decision Trees, Random Forests, and Artificial Neural Networks (ANN). Each method’s performance is analyzed in terms of prediction accuracy, robustness, and computational efficiency. The study also highlights the importance of feature selection, data preprocessing, and time-series modeling in enhancing prediction reliability. Furthermore, the integration of hybrid and ensemble models is discussed as a promising direction for improving trend forecasting. The review concludes that supervised learning offers a powerful framework for understanding stock market behavior and developing intelligent financial prediction systems.
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