equity-valuation-framework

To build a bull base bear equity valuation, provide financial statements, price history, peer set, and macro news. The framework generates explicit assumptions, ranks risks, and frames conclusions as attractiveness, watchlist, or caution with a margin of safety.

A decision-ready investment memo converts market inputs into a structured report with a required section order. It applies multiples and optional DCF modules, assigning a confidence tier to control valuation precision and disclosing gaps that could change the investment view.

Assessing if a stock is cheap or expensive uses relative valuation multiples from a peer set. The framework validates freshness and completeness of financial inputs, assigns a confidence tier, and outputs a fair-value estimate indicating attractiveness or caution.

You can generate a DCF valuation with sensitivity analysis when cash-flow visibility is sufficient. The framework requires an input bundle of financials and market data, automatically validating consistency before producing the optional DCF module outputs.

Before running an equity valuation, data quality checks must validate freshness, completeness, and consistency of the input bundle. The framework assigns a confidence tier based on these checks to control how much precision the final valuation should claim.

You should not use a DCF model for stock valuation when cash-flow visibility is insufficient. The framework defaults to relative valuation via multiples and explicitly discloses data gaps that could change the investment view in the final memo.