Analyzing Event Impact on U.S. Financial Markets
This research project explores the application of Large Language Models (LLMs) in tracking and analyzing real-world events to determine their impact on U.S. financial markets. By leveraging natural language processing and machine learning techniques, we aim to build a comprehensive event tracker that identifies, categorizes, and correlates significant events with market movements, providing insights into causal relationships and predictive patterns.
Aggregating news articles, social media feeds, and financial reports using APIs and web scraping techniques.
Utilizing LLMs to identify and extract relevant events from unstructured text data.
Correlating extracted events with market data including stock prices, indices, and trading volumes.
Quantifying the magnitude and duration of market reactions to specific event categories.
Research findings and visualizations will be published here as the project progresses. Our preliminary analysis indicates significant correlations between geopolitical events, policy announcements, and market volatility.
Eshan Mohamed
Liam Deacy
Daniel Ceballos
Olamide Ogunjobi