All Technologies Used
Motivation
To create a machine learning-based solution capable of analyzing news articles and predicting stock market trends based on narrative and sentiment analysis.
Main Challenges
The team lacked a sufficient dataset for training the models and had to manually collect, clean, and process data using custom web scrapers.
Unstructured text data required manual mapping and labeling. Two data entry specialists were involved to highlight key phrases influencing stock market trends.
Training models to understand sentiment across different industries required extensive resources, making it difficult to generalize for various use cases.
Key Features
- Custom data preprocessing scripts
- Sentiment and narrative analysis using LSTM neural networks
- Trend prediction with a probabilistic output
- Scalable architecture for data processing
Our Approach
Project Impact
Outcome: The MVP achieved an average prediction accuracy of 65%. While not suitable for real-time API integration due to processing time, it demonstrated the feasibility of using news articles to predict stock trends.
Improvements: Enhanced data collection and cleansing techniques, increased prediction accuracy with larger datasets, and improved algorithm stability and scalability.