Vol. 2 No. 03 (2025): AI-Driven Stock Market Forecasting: Leveraging News Sentiment and NLP for Predictive Analytics

Abstract

This project delves into the innovative realm of stock price prediction by leveraging the insights derived from news articles. In an era characterized by the abundance of financial information and the rise of natural language processing techniques, the project aims to develop a predictive model that harnesses the power of textual data to forecast stock price movements.

The project involves the collection of relevant news articles from diverse sources, with a focus on financial news that could impact stock markets. Natural language processing algorithms are implemented to analyze the sentiment and content of these articles, extracting key features that may influence stock prices. The dataset is then utilized to train machine learning models, employing techniques such as sentiment analysis and time-series analysis.

The predictive model's performance is evaluated using historical stock data, measuring its accuracy in forecasting price movements against actual market outcomes. The project also explores the impact of various external factors, such as market trends, economic indicators, and geopolitical events, on the accuracy of predictions.

Through this project aims to contribute to the evolving field of financial technology by providing a comprehensive exploration of the feasibility and effectiveness of utilizing news articles for stock price prediction. The project's findings may offer valuable insights into the integration of natural language processing and machine learning techniques in financial analysis, paving the way for advancements in predictive modeling within the context of dynamic and unpredictable stock markets.

Index terms

Stock price prediction, News articles, Natural language processing (NLP), Financial information, Machine learning models, Sentiment analysis, Time-series analysis, Historical stock data, Market trends, Economic indicators, Geopolitical events, Financialtechnology (FinTech), Predictive modeling, Financial analysis, Integration of NLP and machine learning, Dynamic stock markets.