AI-Powered Financial Footnotes Predictor

This project, developed during the Technica 2023 hackathon, tackles the Bloomberg Industry Group's challenge of creating an AI-centric solution to parse, display, and extract value from a corpus of text. Specifically, we focus on summarizing footnotes of SEC 10-K filings to predict next year’s footnote for Apple based on their historical fiscal data.

Features

• Natural Language Understanding (NLU) to interpret financial data.
• A web-based interface for inputting queries and viewing predictions.
• Utilizes Vector DB for content retrieval and GPT-4 for generating predictions.

Getting Started

1. Clone the repository.
2. Install dependencies using pip install -r requirements.txt.
3. Run the Flask app using python scripts/app.py.

Directory Structure

• static/: Contains CSS and font files.
• scripts/: Contains the Flask application script.
• index.html: The main HTML file for the web interface.
• requirements.txt: Lists the Python dependencies.

Technologies Used

• Flask for the web application framework.
• GPT-4 for generating predictions.
• Vector DB for content retrieval.

Acknowledgements

This project was inspired by the Bloomberg Industry Group challenge at Technica 2023. The challenge emphasized creating AI-powered solutions to derive value from text data.

License

This project is open source, under the MIT License.

For more information on the challenge and the hackathon, refer to the Technica 2023 DevPost.