AI-Infused Finance: Predicting Stock Prices Through News and Market Data Analysis

This research contributes to the evolving field of Stock Price Prediction by addressing the critical influence of market sentiment and news on stock movements. Recognizing the profound implications of news in shaping stock prices, we propose a novel model designed to anticipate these movements by leveraging both market data and sentiment analysis of news. The envisioned framework holds significant potential for investors and corporate entities seeking to safeguard shareholder interests. In the pursuit of accurate predictions, our methodology involves meticulous data preprocessing, thoughtful feature engineering, and training on diverse classification models. The selection of the most accurate model, based on rigorous evaluation, is a key facet of our approach. Notably, our model demonstrates a commendable accuracy of approximately 77% in predicting stock prices for the next day. Furthermore, we extend our analysis to forecast stock price movements up to 10 days in advance, revealing a gradual decline in predictive accuracy over an extended timeframe. This research distinguishes itself by providing a comprehensive solution that incorporates market data and sentiment analysis of news to enhance the precision of stock price predictions. Our findings not only contribute to the academic discourse on stock price prediction but also offer a practical tool for investors and firms aiming to make informed decisions in the dynamic financial landscape. Our model showcases a notable advancement in accuracy, underscoring its potential significance for strategic decision-making in the realm of stock investments.