Practice · graded on a filing
Build it from NVDA’s latest annual filing. Every line you enter is checked by code against the filing.
Re-run the single-stage DCF arithmetic nine times: the stated discount rate one percentage point below, at, and above its base, crossed with terminal growth half a point below, at, and above its own. Each cell of the 3 × 3 grid is the implied enterprise value at that pair of stated rates; the centre cell is the single-stage DCF drill's own sum. The grid shows how the arithmetic responds when a stated assumption moves — it stops at enterprise value in every cell and never reaches a per-share number. Every figure you are given comes from NVDA’s latest annual filing and is cited below; the assumptions are handed to you and are never graded. Nothing here is a view on the company.
Reconcile NVDA's model from the filed figures below (as of 2026-01-25). Derive each linked line to the dollar — the model must reconcile end to end.
NVIDIA CORP 10-K (FY2026, period ended 2026-01-25), accession 0001045810-26-000021, via SEC EDGAR
Educational use only — not investment advice.
Very. Build a grid: discount rate on one axis, terminal growth on the other, and re-run the whole DCF at each pair. Value falls as the discount rate rises and climbs as terminal growth rises, and the effect is largest where the two are close, because the terminal value divides by their difference.
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Operating cash flow, interest expense, the effective tax rate and capital expenditures, as filed in the latest annual report, bridged to unlevered free cash flow — the same base the single-stage DCF grounds on, so this drill needs nothing new from the filing. Each cell is checked against the value the same pipeline derives from that filing, inside a small tolerance band, and the model has to reconcile end to end. There is no language model in the grade — it is arithmetic over filed figures, so it returns the same verdict every time. The correct values are not in this page: they are recomputed on the server when you submit, and your entries are read as a submission, never as the answer.
Every attempt ends in a review. Each line is marked Best, Inaccuracy, Mistake or Blunder by how far it is off, and the first line that went wrong is flagged, so an error carried through the rest of the model is fixed once, where it started.
Signed out, nothing is recorded: work it as many times as you like. Signed in, each graded attempt is saved to your account so your dashboard can track it.
Each one is built from that company's own annual filing and graded the same way.
Educational use only — not investment advice. Figures come from public SEC filings; Echelon teaches you to analyze data, it never recommends buying or selling any security.