Predicting Green Connectivity Stock Process, A Hybrid Machine Learning Approach for Sustainable Finance Using Financial and News Sentiment

Combining traditional financial data with real-time news sentiment analysis allows you to anticipate market fluctuations in green connectivity stocks. We combine financial data such as historical pricing, trade volume, and important financial ratios with sentiment scores obtained from news articles about sustainability and green technologies. to increase prediction accuracy, we employ a multistage machine learning pipeline that incorporates natural language processing (NLP) techniques for sentiment analysis and advanced regression models for price forecasting. The hybrid model leverages the complimentary properties of both structured financial data and unstructured news information to provide a more complete view of the elements impacting stock performance in the context of sustainable finance. The experimental results demonstrate the utility of the model in catching short-term stock price changes, providing valuable data for investors looking.