Can Leaders Voices Affect Financial Markets? Exploring NASDAQ, NSE, and Beyond

This paper presents a novel approach to predicting financial market trends by integrating deep learning models with natural language processing (NLP) techniques applied to Twitter data from influential leaders. Unlike traditional models reliant solely on historical financial data, our method leverages real-time social media information to enhance predictive accuracy. Key contributions include the development of a versatile algorithm capable of generating models for any Twitter handle and financial component, as well as predicting the temporal window during which tweets affect stock prices. We also explore the combined effects of multiple Twitter handles on trend prediction. Through a comprehensive survey, we identify research gaps, collect necessary data, and propose a state-of-the-art algorithm with a complete implementation environment. Our results demonstrate significant improvements facilitated by NLP analysis of Twitter data on financial market components. We focus on the Indian and USA financial markets, with potential for extension to other markets. In conclusion, we discuss the socio-economic implications and utility of our approach in informing decision-making processes within financial markets.