Enhancing Demand Forecasting Accuracy Through Market Trend Analysis: Leveraging NLP Algorithms for Data-Driven Insights

In this study, we investigate how Natural Language Processing (NLP) approaches have revolutionized demand forecasting in the stock market. We show a constant gain in prediction accuracy when using NLP-derived features in forecasting models, such as attitudes, subjects, and entities. Our research shows that models driven by natural language processing are more accurate overall. Case studies highlight how NLP insights have been put to use in the real world to improve stock demand forecasts. These learnings, gleaned through textual data analysis and real-time market emotions, provide investors the ability to make better judgments. The next steps for this study will be to improve natural language processing techniques for more accuracy, to broaden data sources to incorporate alternative data streams, and to investigate cutting-edge machine learning approaches. These developments have the potential to significantly improve stock market forecasting, giving investors more accurate resources for navigating the financial markets.