DCF Model Assumption Reviewer
Investment analysts, M&A advisors, and CFOs use this prompt to pressure-test DCF model assumptions before presenting a valuation to a board, investment committee, or acquirer. It is especially useful when assumptions have been built over time by different team members and need a unified consistency check.
Prompts
You are a senior investment analyst specializing in discounted cash flow valuation for [INDUSTRY] companies. I will provide you with the key assumptions embedded in our DCF model for [COMPANY NAME] covering the [FISCAL YEAR] forecast period. The model uses [CURRENCY] as the reporting currency. Here are the assumptions under review: - Revenue growth rates by year: [REVENUE GROWTH RATES] - EBITDA margin progression: [EBITDA MARGINS] - Weighted Average Cost of Capital (WACC): [WACC %] - Terminal growth rate: [TERMINAL GROWTH RATE %] - Capital expenditure as % of revenue: [CAPEX %] - Tax rate: [TAX RATE %] - Net working capital changes: [NWC ASSUMPTIONS] For each assumption, perform the following analysis: 1. **Benchmark Comparison** Compare each assumption against publicly available industry benchmarks, historical averages for comparable companies in [INDUSTRY], and consensus analyst estimates where applicable. Classify each as: Aggressive / Reasonable / Conservative. 2. **Internal Consistency Check** Assess whether the assumptions are mutually consistent β e.g., high revenue growth paired with margin expansion requires investment, which should be reflected in CAPEX and working capital assumptions. 3. **Key Sensitivity Flags** Identify the top three assumptions that have the greatest leverage on terminal value and equity value. Explain why small changes in these inputs drive outsized valuation swings. 4. **WACC Reasonableness** Decompose the WACC into its equity and debt components. Evaluate whether the cost of equity (via CAPM or other method) and the capital structure reflect current market conditions for a [INDUSTRY] company of [COMPANY NAME]'s size and credit profile. 5. **Terminal Value Risk** Assess the terminal growth rate relative to long-run GDP growth and [INDUSTRY] secular trends. Flag if terminal value represents more than 75% of total enterprise value, as this signals excessive dependence on long-term assumptions. 6. **Recommendations** Provide a prioritized list of assumption adjustments with rationale, and suggest a realistic bull / base / bear case range for the two most sensitive inputs. Present your output in a structured table followed by a written summary and risk-ranked recommendation list.
Prompt Variables
Replace each placeholder with your specific information:
[INDUSTRY][COMPANY NAME][FISCAL YEAR][CURRENCY][REVENUE GROWTH RATES][EBITDA MARGINS][WACC %][TERMINAL GROWTH RATE %][CAPEX %][TAX RATE %][NWC ASSUMPTIONS]What You'll Get
A structured table classifying each assumption as aggressive, reasonable, or conservative with benchmark references; an internal consistency assessment; a ranked list of the top sensitivity drivers; a WACC decomposition review; a terminal value risk flag; and a prioritized set of recommended adjustments with bull/base/bear ranges.
π‘ Pro Tip
Include the source of each assumption (e.g., 'management guidance', 'analyst consensus', 'historical average') alongside the figure β the AI will weight its critique accordingly and focus scrutiny on the least-anchored inputs.
Compatible AI Tools
Excels at multi-assumption consistency checks and narrative risk framing. Paste assumptions as a structured list or table. Claude surfaces internal contradictions that simpler tools miss.
Effective for structured assumption benchmarking. Use GPT-4 or later for best financial reasoning; consider uploading a comparable company dataset via Code Interpreter to anchor benchmark comparisons.
Useful when your model is in Google Sheets β Gemini can reference live spreadsheet data and flag assumption outliers inline without copy-pasting figures.
Best when the DCF model lives in Excel. Copilot can read cell ranges directly and annotate assumption cells with benchmark commentary without disrupting the model layout.
Run financial models and sensitivity analysis scripts directly from terminal with full calculation support
Build and iterate financial models in an AI-powered code editor with Python finance library support
Execute financial calculations and data analysis workflows from terminal with 1M token context for large datasets