Stock Market Sentiment Analysis (Internal R&D Project)
Traders and analysts struggled to process unstructured news and social media feeds in real-time, missing sentiment shifts that directly affect asset prices. Traditional quantitative models ignored this qualitative data, leading to delayed decision-making.
Client
Stock Market R&D
Year
2022
Role
Full-Stack
Tech
The Challenge
Traders and analysts struggled to process unstructured news and social media feeds in real-time, missing sentiment shifts that directly affect asset prices. Traditional quantitative models ignored this qualitative data, leading to delayed decision-making.
The Solution
We built a natural language processing (NLP) pipeline that ingests financial news feeds, classifies real-time sentiment, and merges it with historical price charts. Developed as an internal research project, the system demonstrated a measurable improvement in predicting short-term market movements.
