Event Driven Stock Prediction

The method for event-driven stock prediction presented in this work combines machine learning, sentiment analysis, and natural language processing techniques. The main objective is to create an advanced technique that might accurately predict stock market movements using these approaches. By looking at news stories and financial data and giving each a weight that is particularly significant, the suggested methodology primarily captures sentiment and relevancy. To depict the rather intricate correlations between events and stock price changes, a predictive model, like a recurrent neural network, is generally trained using weighted data. The results greatly outperform conventional time-series forecasting methods in a setting where events primarily affect the stock market. These insights are beneficial to traders, investors, and financial institutions