For years, I've wanted to automate Damodaran's valuation methodology industry betas, ERP, country risk premiums, the whole thing.
His data is free on the NYU page, but turning it into an actual valuation takes hours per stock.
StockValuation.io does it in minutes:
- Pulls live financials via yFinance
- Uses Damodaran's published datasets for cost of capital inputs
- Runs deterministic DCF math (every assumption is visible)
- Layers an LLM on top for bull/bear narratives and assumption overrides
The math never changes based on AI mood. The LLM only touches research and storytelling.
Self-host: https://github.com/stockvaluation-io/stockvaluation_io (one Docker command, MIT license)
Happy to discuss the methodology, assumption choices, or why I think Deterministic math + probabilistic narratives is the right architecture for this.
