Stock Price Prediction using Machine Learning

Abstract

Stock price prediction, a vital aspect of modern financial analytics, leverages historical data, market trends, and advanced analytical techniques. Machine learning methods, including deep learning, time series analysis, and regression models, have significantly enhanced forecast accuracy. These models also incorporate external factors such as market sentiment from news and social media. Accurate predictions aid investment strategies, risk management, and market stability. This study highlights the synergy between sentiment analysis and technical models, showing their combined potential to improve predictions. Beyond investment decisions, this integration informs economic research, market dynamics, and policy making, advancing smarter and more efficient financial markets.