This project is a High-Throughput, AI-powered financial document analyzer designed to rapidly extract actionable investment insights from large regulatory filings (like 10-K and 10-Q reports).
It converts hours of manual review into automated data signals, empowering analysts and quantitative models with faster intelligence.
The solution is engineered for speed and efficiency using a Natural Language Processing (NLP) Pipeline built on Python and the Hugging Face transformers library.
The project was optimized using GPU acceleration, reducing the analysis time for a full 10-K document from over 60 minutes (on CPU) to under 2 minutes (on a T4 GPU). This ~30x speed increase is essential for scalable, daily document processing.
- Summarization: Utilizes the lightweight
sshleifer/distilbart-cnn-12-6model to efficiently condense document chunks. - Signal Extraction: Uses the
distilbert-base-cased-distilled-squadmodel for Question Answering (QA) to precisely identify high-value information.
The final stage of the pipeline focuses the QA model on extracting specific factors critical to investment decisions:
- Revenue Jumps
- Cash-Flow Risks
- Buy/Sell Signals (General Risk Factors)
- Dependencies: Ensure a T4 GPU runtime is active in Colab or a local environment is configured for CUDA.
- Document: Place the target SEC filing (e.g.,
apple10k.pdf) in the working directory.
!pip install PyPDF2 transformers accelerate torch --quiet