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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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Copyright © 2020, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC-BY-NY-SA). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Corresponding Author: K. Sujatha, sujathakota29@gmail.com
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Conflict of interest: The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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