Time Series Forecasting for Stock Price Movement: Leveraging NLP for Stock Market Prediction

This paper delves into the critical realm of stock market prediction, exploring the innovative role of Natural Language Processing (NLP). Traditionally, stock market forecasting has faced challenges due to reliance on historical data and economic indicators, often resulting in limited accuracy. NLP, however, offers a transformative approach by analysing vast volumes of textual data from sources like news articles, social media, and financial reports. Through sentiment analysis, trend identification, and company health assessment, NLP uncovers valuable insights that influence stock prices. The paper reviews existing research methodologies, integrating NLP with machine learning and deep learning models to predict market trends and sentiments. It highlights NLP's strengths in analysing unstructured data, uncovering hidden relationships, and providing real-time insights. Despite its advantages, NLP faces limitations such as biases in training data and challenges in interpreting nuanced language. The paper concludes by proposing future directions like explainable AI and incorporating alternative data sources, emphasizing NLP's potential to revolutionize stock market prediction while acknowledging the need for integrated approaches in financial analysis.