Professional Equity Valuation Modeling

Problem

Equity valuations often fail because the inputs, model, and conclusion are not tightly coupled: FCFF is paired with the wrong WACC, FCFE mixes in the equity cost incorrectly, exam-style shortcuts leak into deal materials, or a single valuation point is presented without parameter sources, key assumptions, or sensitivity bounds. equity-valuation treats valuation as an auditable engineering workflow rather than a one-off answer.

How It Works

The skill is a valuation modeling engine and does not fetch prices, financials, β, Rf, or comparable multiples by itself; inputs must come from the user or an upstream connector such as ifind-finance-data. It classifies the target (public/private, cyclical/growth, financial/non-financial), selects a primary model plus a cross-check, and normalizes one-off gains, abnormal taxes, and accounting-policy noise. It supports DDM, FCFF, FCFE, relative multiples, and RI, with explicit formulas, WACC or cost-of-equity build-up, parameter sources, key assumptions, and sensitivity ranges. For semiconductor or private targets, it mandates data-trust checks via semiconductor-equity-investment-dd before modeling.

Boundaries

It does not execute trades and does not provide buy/sell advice. Missing inputs must be flagged as assumptions rather than filled with placeholder values. Report generation is local; .md, .html, and .docx outputs are produced in the workspace, with DOCX depending on python-docx.