Modeling Scenario Based Valuations

Modeling Scenario Based Valuations

Builds probability-weighted valuation models with multiple exit scenarios, timing assumptions, and risk-adjusted returns for growth equity and late-stage investments.

When To Use

• Evaluating a growth equity or expansion capital investment where the outcome range is wide (e.g., IPO vs. secondary sale vs. down-round recap)
• Stress-testing an existing valuation under different macro, operational, or exit-timing assumptions
• Building an investment committee memo that requires a probability-weighted expected value and IRR/MOIC sensitivity
• Comparing risk-adjusted returns across multiple deal opportunities in a portfolio context
• Modeling management-case vs. investor-case divergence to frame negotiation anchors on entry price

Inputs To Gather

• Company financials: Last 3 years of revenue, EBITDA/operating income, gross margin, and net revenue retention (for SaaS/recurring models). Trailing-twelve-month run-rate if available.
• Growth plan: Management projections or consensus forecasts for revenue and margin trajectory over the hold period (typically 3–7 years).
• Comparable multiples: Current and historical EV/Revenue, EV/EBITDA, or P/E multiples for public comps and recent transactions in the sector. [VERIFY] sector-specific multiple ranges against current market conditions.
• Capital structure: Existing equity ownership, option pool, outstanding convertible instruments, liquidation preferences, and any participating preferred terms that affect waterfall distributions.
• Exit assumptions: Realistic exit channels (IPO, strategic M&A, sponsor-to-sponsor, secondary, recap/dividend) with indicative timing windows and probability weights.
• Discount rate / hurdle: Fund-level target return (e.g., 20–30% gross IRR for growth equity) and any LP-driven constraints on hold period or concentration.

Workflow

1. Define Scenarios

Construct at minimum three discrete scenarios — each must differ on at least two independent variables:

Optionally add a Downside/Wipeout scenario (5–10%) for capital-loss cases (e.g., liquidation preference recovery only). Probability weights must sum to 100%.

2. Build Per-Scenario Cash Flows

For each scenario:

• Project revenue, EBITDA, and free cash flow through the exit year
• Apply the relevant exit multiple to the terminal metric (Revenue or EBITDA) to derive enterprise value at exit
• Run the equity waterfall: subtract net debt, apply liquidation preferences and participation caps, distribute remaining proceeds to common/preferred holders
• Calculate gross proceeds to the fund's position, then compute MOIC and IRR for that scenario
• If interim cash flows exist (dividends, recaps), include them in IRR computation

3. Compute Probability-Weighted Returns

• Weighted MOIC = Σ (scenario probability × scenario MOIC)
• Weighted IRR: solve for the discount rate that equates the probability-weighted cash flow stream to zero NPV (do not simply average IRRs — this is mathematically incorrect)
• Report both the weighted expected return and the full range (min IRR to max IRR)

4. Run Sensitivity Analysis

Build two-way sensitivity tables on the most impactful variable pairs:

• Entry multiple vs. exit multiple (shows valuation discipline impact)
• Revenue growth rate vs. exit timing (shows execution risk)
• Probability weight shifts (e.g., shift 10% from Bull to Bear — how does weighted MOIC move?)

Highlight the break-even entry price at which the weighted IRR hits the fund's hurdle rate.

5. Validate and Stress-Test

• Cross-check implied exit valuations against public comp ceilings — flag any scenario where the implied exit is above the 75th percentile of comps [VERIFY]
• Confirm that the Bear case is genuinely adverse, not just "slightly below plan"
• Test for liquidation preference drag: in the Bear case, does the preferred structure consume most of the equity value?
• Verify IRR math by independently discounting cash flows at the computed rate

Output

The deliverable should include:

• Scenario summary table: Each scenario with key assumptions (growth rate, exit multiple, exit year, probability weight) and resulting MOIC / IRR
• Probability-weighted return: Single expected MOIC and IRR with range band
• Sensitivity tables: At least two two-way tables with conditional formatting highlighting cells above/below hurdle
• Waterfall exhibit: Showing how exit proceeds distribute across the cap table under each scenario
• Key assumptions register: Every input assumption listed with its source (management, investor estimate, market data) and confidence level
• Narrative summary: 1–2 paragraphs interpreting the results, calling out the primary risk factors and the entry price sensitivity

Quality Checks

• Probability weights sum to exactly 100%
• IRR is solved iteratively (not averaged across scenarios)
• Exit multiples are benchmarked to current comps, not aspirational peak multiples [VERIFY]
• Bear case models genuine downside — not just a 10% haircut on the base case
• Liquidation preferences and participation correctly modeled in the waterfall for every scenario
• All assumptions sourced and labeled; unsourced assumptions marked [VERIFY]
• Terminal values do not imply growth rates above the nominal GDP growth rate in perpetuity
• Model ties out: entry equity check size × MOIC = gross proceeds in each scenario