Stock Market Prediction Using Machine Learning: A Review

This review article explores the advancements in using machine learning algorithms to forecast the stock market. Various techniques are used to select features, evaluate models, and pre-process data. Examples of machine learning models discussed include support vector machines, random forests, and artificial neural networks (ANNs). The article also examines the challenges and limitations of using machine learning for stock market forecasting and identifies important components that improve the accuracy of these models. The study's objectives are to present a thorough evaluation of the state of the field today and to pinpoint potential areas for further investigation. Overall, while machine learning has great potential for market forecasting, more work is needed to improve the accuracy and reliability of these models.