The full methodology behind every Techdamentals verdict, no black box.
1. Where the numbers come from
Financial data (price, cash flow, shares, debt, revenue) comes from established market-data
providers. But we don't take any single provider's word for it. Every analysis runs a
data-integrity gauntlet:
Independent price witness, the primary provider's price must agree with a
second, independent exchange-grade source within 5%. If the two disagree, the analysis
is marked unverified. We'd rather admit doubt than sound confident on a bad number.
Internal consistency, sales/share × share count must reconcile with reported revenue;
the price must sit within its own 52-week range; multi-class share structures are re-based to
the market-cap-implied total.
Special cases, ETFs and funds are refused (you can't DCF a basket); fresh IPOs are
labeled provisional; anything that fails a check is shown with the failure, not hidden.
2. The five valuation models
Discounted Cash Flow (20-year). Projects free cash flow twenty years out with a
growth rate derived from the company's own FCF history (sanity-capped), discounts each year back to
today (~9% discount rate, region-based), adds cash, subtracts debt, divides by shares.
Discounted Cash Flow (10-year). Same engine, shorter horizon, less dependent on
far-future guesses.
Discounted EPS. Projects earnings per share instead of cash flow, the classic
earnings-power lens.
Mean Price-to-Book. What the stock would cost at its historical book-value
multiple. Best for asset-heavy businesses; marked N/A when history is unavailable.
PSG (Price-to-Sales-Growth). Values revenue with the company's net margin and
growth: fair P/S ≈ net margin × growth%. The lens that still works for low-profit growth companies.
3. The consensus verdict
Applicable models are blended into a weighted fair value, with a fair-value range of
±10% around it. The verdict follows the price's position: below the range =
undervalued, inside = fair value,
above = overvalued.
We also show Wall Street's consensus target next to ours, not because analysts are right,
but because you deserve to see when we disagree with them.
4. Where AI fits (and where it doesn't)
AI does judgment: choosing reasonable growth assumptions within caps and writing the
plain-English takeaways. Plain, tested code does all arithmetic, every projection, discount,
and blend is deterministic and unit-tested. AI models are excellent writers and unreliable
calculators, so we never let one do math.
5. The honesty rule
When the data doesn't reconcile, the app says so: the verdict is labeled
unverified, the headline percentage is suppressed, and the ticker can't be saved to a watchlist
or alerted on. A confident number built on bad data is worse than no number. This is the product's
founding principle.
6. What this is not
Techdamentals is an educational research tool. It produces impersonal, algorithmic estimates that
are identical for every user. It is not investment advice, not a recommendation, and not a substitute
for your own research or a licensed professional. See our Terms of Service
and Privacy Policy.