Building an institutional valuation platform from SEC filings
What it takes to move from raw XBRL facts to a valuation process that is auditable, reusable, and decision-ready.
The hardest part of valuation automation is not the DCF formula. It is preserving meaning as financial data moves from filings into forecasts, assumptions, and an investment conclusion.
The real problem is data meaning
SEC XBRL is structured, but it is not automatically investment-ready. Companies use different concepts, reporting periods, dimensions, and presentation conventions. A useful system must reconcile those differences without hiding them.
I treated data lineage as part of the model itself. Each important number should be traceable to a filing, a transformation, or an explicit analyst assumption.
Architecture before automation
The platform separates common research infrastructure from company-specific operating logic. Shared components handle ingestion, validation, valuation, and reporting; adapters capture how a particular business actually creates revenue and margins.
That boundary makes the system reusable without pretending every company can be understood through the same generic forecast template.
A model should support a decision
DCF and comparables are necessary outputs, but they are not the finish line. Reverse DCF, scenario attribution, market-implied expectations, and thesis diagnostics help explain why an apparent valuation gap exists—and what would cause the thesis to fail.