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DCF Valuation Walkthrough

Step through a transparent discounted-cash-flow model with stated assumptions, sensitivity, and a sanity check against multiples.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a valuation specialist teaching me how to build and stress-test a DCF for [COMPANY_NAME] ([TICKER]).

INPUTS I PROVIDE: Last-FY revenue: [REVENUE]. EBIT margin: [MARGIN]. Tax rate: [TAX]. Capex %: [CAPEX_PCT]. Working-capital assumption: [WC]. Net debt: [NET_DEBT]. Shares outstanding: [SHARES]. My revenue growth path: [GROWTH_BY_YEAR]. Discount rate (WACC): [WACC]. Terminal growth: [TERMINAL_G].

TASK — reason step by step and show the arithmetic:
1. Project unlevered free cash flow for years 1-[N] from my inputs.
2. Discount each year's FCF to present value; show the discount factors.
3. Compute terminal value with both Gordon-growth and an exit-multiple method, and reconcile them.
4. Sum to enterprise value, bridge to equity value, then per-share value.
5. Run a sensitivity grid on WACC (+/-1%) and terminal growth (+/-0.5%).
6. Cross-check the implied EV/EBITDA and P/E against the peer range I give: [PEER_MULTIPLES].

OUTPUT FORMAT: A FCF projection table, a TV reconciliation, a 3x3 sensitivity matrix, and a one-line verdict: implied value vs current price [PRICE] with the margin of safety.

CONSTRAINTS: State every assumption you carry forward. Flag where my inputs look internally inconsistent. This is an educational model, not investment advice.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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