Machine Learning Approaches in Stock Market Prediction: A Systematic Literature Review

This paper provides a comprehensive review of the use of machine learning (ML) models in stock market prediction, focusing on the effectiveness of various approaches and the integration of external data sources. The review covers widely used ML models, including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Long Short-Term Memory (LSTM) networks, and hybrid models. It compares the performance of these models, highlighting key metrics such as accuracy, precision, and Root Mean Square Error (RMSE).