Financial markets are complex ecosystems influenced by a myriad of factors. This project aims to harness the power of sentiment analysis from daily news articles to predict the stock volatility of publicly traded companies. By leveraging natural language processing techniques and established time-series models, we intend to create a robust framework for forecasting daily stock volumes traded and closing prices.