A Comprehensive Review of Stock Market Prediction Techniques Using Machine Learning and Deep Learning

Abstract

Stock market prediction has been a challenging task due to its dynamic and volatile nature. This paper reviews seven research studies that employ various machine learning (ML) and deep learning (DL)[5] techniques for stock market forecasting, including LSTM, ARIMA[6] , ensemble methods, and sentiment analysis. The results indicate that deep learning models such as LSTM[1, 2]. and hybrid approaches outperform traditional ML models, while sentiment-based techniques and ensemble learning enhance prediction accuracy. Additionally, the impact of feature selection[9] and external factors such as economic indicators and news sentiment is explored.