LLM-Based Event Tracker

Analyzing Event Impact on U.S. Financial Markets

Live Event Tracker

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Tech Company X Announces New AI Product

Sentiment: Positive   Predicted Magnitude: 3%

This event is expected to boost stock prices due to strong market demand.

Read Article →

TCX

Actual Impact: N/A

Energy Sector Hit by Supply Shortage

Sentiment: Negative   Predicted Magnitude: -2%

Shortage in oil supply is causing market uncertainty and price drops.

Read Article →

ENR

Actual Impact: N/A

Market Analytics Dashboard

Current Sentiment

Projected Impact by Industry

LLM Prediction vs. Actual Market Move

Abstract

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.

Methodology

Data Collection

Aggregating news articles, social media feeds, and financial reports using APIs and web scraping techniques.

Event Extraction

Utilizing LLMs to identify and extract relevant events from unstructured text data.

Market Analysis

Correlating extracted events with market data including stock prices, indices, and trading volumes.

Impact Assessment

Quantifying the magnitude and duration of market reactions to specific event categories.

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Research Team

Team Member

Mohamed Eshan

Team Member

Liam Deacy

Team Member

Daniel Ceballos

Team Member

Olamide Ogunjobi