FINANCE AND ECONOMICS: STOCK PRICE PREDICTION USING HISTORICAL DATA AND SENTIMENT ANALYSIS.

ABSTRACT Stock price prediction is a critical aspect of financial analysis, influencing investment decisions, risk management, and economic planning. Traditional forecasting methods rely heavily on historical stock prices and technical indicators. However, recent advancements in artificial intelligence and natural language processing (NLP) have enabled the integration of sentiment analysis to improve predictive accuracy. This project explores a hybrid approach that combines historical stock market data with sentiment analysis of financial news, social media, and investor opinions. The study leverages machine learning algorithms, including regression models and deep learning techniques, to analyze past trends and extract sentiment-driven insights. By incorporating textual sentiment alongside quantitative data, the model aims to enhance stock price predictions beyond conventional statistical approaches. The research also examines the impact of market sentiment on stock fluctuations, identifying key sentiment indicators that influence price movements. The results demonstrate that integrating sentiment analysis can significantly improve prediction accuracy compared to traditional methods. The study highlights the potential of AI-driven financial forecasting, offering valuable insights for investors, traders, and policymakers. Keywords: Stock Price Prediction, Sentiment Analysis, Machine Learning, Historical Data, Finance, Economics, Artificial Intelligence