★ 20 Ways to Generate Stock Investment Profits with AI - 15. NLP-Powered Fundamental Analysis Automation

 

15. NLP-Powered Fundamental Analysis Automation

Introduction
Parsing lengthy SEC filings manually is laborious. NLP pipelines can extract key financial metrics and highlight red flags in minutes.

Pipeline Overview

1. Filing Retrieval: Download 10-K and 10-Q documents via the EDGAR API.

2. Text Extraction: Use PDF parsers (e.g., pdfplumber) to isolate MD&A and footnotes.

3. Named Entity Recognition: Fine-tune spaCy on financial entities—“Revenue,” “Debt,” “EBITDA.”

4. Anomaly Detection: Flag YoY metric changes exceeding ±30%.

Implementation Steps

• Build a scheduler to poll EDGAR daily for new filings.

• Process documents through your NLP pipeline; store extracted metrics in a database.

• Generate automatic alerts for anomalies and generate summary reports.

Impact
Automated fundamental scraping cut analysis time from 4 hours to 15 minutes per company, uncovering aggressive revenue recognition issues in real time.

Conclusion
By automating fundamental data extraction, you gain faster, deeper insights—allowing you to act before the market fully digests complex filings.