SEC 10-D ABS Scraper & Analytics Pipeline

A Python scraper and analytics suite for extracting financial data from SEC 10-D Distribution Reports for Asset-Backed Securities (ABS) and visualizing lifecycle payment schedules.

Overview

This repository contains tools to extract structured data from various auto-loan ABS trusts (including Ford, CarMax, Ally, Fifth Third, Capital One, and Santander).

It specifically targets:

• Table 2: Available Funds
• Table 3: Distributions
• Table 4: Noteholder Payments
• Table 5: Note Balance

Cleanup Call Analytics

The primary analytics script (honkanen_plots_v6.py) focuses on isolating and analyzing Cleanup Calls. It extracts historical data across all scraped issuers to mathematically model and visualize:

1. The 10% Industry Standard: Validating that cleanup calls are consistently executed when the initial pool size reaches ~10%.
2. Terminal Events: Proving a 1:1 ratio between the Cleanup Call Amount and the Remaining Pool Balance, confirming that the call fully retires the debt.
3. Tranche Waterfalls: Generating individual Case Study plots that trace the lifecycle of subordinated tranches down to their terminal cleanup call date.

Usage

1. Run the Scraper

python scraper.py

Outputs structured CSVs for each table into the output/ directory.

2. Generate Analytics & Visualizations

python honkanen_plots_v6.py

Outputs the following to output/analysis_v6/:

• Master Time-Series of all scraped collections (2006-2024)
• Verification scatter plots
• Time-Series of Cleanup Call ratios
• Visual tranche-level Case Studies (.png) and raw pool data (case_study_pools.csv)

Requirements

pandas
matplotlib
requests
lxml

Data Storage

Note: Due to their size, the raw scraped output/ directories and generated .png plots are explicitly ignored via .gitignore to keep the repository lightweight. Only the python scripts and metadata mappings are tracked.