Extracting clean data from old SEC filings with small models

What they built

u/luvs_spaniels studies financial bubbles. The useful filings are late-1990s and early-2000s text files with little metadata, and a small local LLM turns them into clean, usable data.

They also strip company names and tickers before asking about red flags, so the model isn't biased by knowing it's Enron.

How it works

1. Break the target information into small components (a single table or paragraph)
2. Use many narrow prompts per table, sometimes ten
3. Strip identifying names and tickers before analysis

Results & numbers

• Tabular extraction/matching around 97% on the latest random sample
• Some 7B-13B models hallucinated less on this task than larger ones

Gotchas

• Broad prompts like 'summarize file x' don't work
• Assigning meaning to abnormal wording is the hard part

“You might end up with 10 prompts for a single table. That's okay because you're not paying by the token.”— u/luvs_spaniels

Do it yourself

1. Chunk each filing so one prompt sees one table or paragraph
2. Ask for one field or table at a time, in JSON
3. Remove company names and tickers first
4. Spot-check a random sample to measure accuracy