Find the mispricing with a reverse-DCF
Back out what the market is implying, see where the assumptions break, and defend a target you can take to the IC.
The problem
A forward DCF is only as good as your guesses. The sharper question is the reverse one — what does the current price already assume? — but reconstructing that by hand for every name is slow and error-prone.
How Valuein does it
- 1
Solve for the implied expectations
compute_dcf runs a reverse-DCF that backs out the revenue growth and margin the current price requires — and a two-stage forward DCF when you want an intrinsic estimate.
- 2
Ground the inputs in filings
get_company_fundamentals feeds the historical FCF base; get_peer_comparables calibrates the discount rate against real peer leverage — no hand-keyed assumptions.
- 3
Export something you can defend
generate_dcf_xlsx ships a sensitivity-heatmap workbook (15-min presigned download) you can drop into an IC memo.
In practice
You ask
Reverse-DCF this name: what revenue CAGR and terminal margin does today's price imply, and is that realistic vs its 10-year history?
Valuein returns
Price implies ~X% CAGR for 10 years at a Y% margin — above anything in its filing history. Flagged as priced for perfection; downside to base case is Z%.
The outcome
You see exactly where the market's assumptions are heroic — and walk into the IC with a target and a workbook behind it.
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Frequently asked
Does it do reverse-DCF or forward DCF?
Both. compute_dcf solves for the market-implied growth/margin (reverse) and can also run a two-stage forward DCF for an intrinsic estimate.
Can I get a workbook out of it?
Yes — generate_dcf_xlsx returns a formatted Excel model with a sensitivity heatmap via a short-lived presigned link (Pro+).
Related use cases
Do this with your own data — free.
The free S&P 500 tier needs no credit card. One token works across MCP, the Python SDK, and the Workspace.