Lesson 8 of 11 · All lessons in this module
- Disaggregated models: why and how to build a forecast valuation model
- Forecasting revenue: top-down, bottom-up and non-recurring items
- Forecasting operating costs: COGS, SG&A and segment margins
- Expense models: regressions, itemized costs and margin assumptions
- Working capital, capital investments and capital structure
- Asset and financing models: investment, financing needs and FCFE
- Integrated cases: a regional revenue build and richer financing assumptions
- Scenario analysis: risk factors, cost structure and sensitivityVideo lesson
- Matching the model to the company: life cycle, distress and control
- From forecast statements to equity value: FCFF, terminal value and scenarios
- Integrated case: from assumptions to equity value, step by step
This lesson is part of the 2027 curriculum. You are following the 2026 curriculum, where it is not taught in this form. Switch if you are sitting the exam under the 2027 curriculum.
Lesson 8 of 11 · 15 min
Scenario analysis: risk factors, cost structure and sensitivity
Instead of a single point forecast, the analyst builds scenarios around the key risk factors, carries each through revenue and a fixed/variable cost structure to EPS, and tests how sensitive the result is to the most uncertain assumptions.
In short
- Four generic risk factors affect all companies to different degrees: business cycle, competition, inflation/deflation and technological developments.
- Build several scenarios (e.g. bull, base, bear), judge their likelihood, and compare them with other analysts' forecasts and with what the current valuation implies.
- Technology can shift supply right (lower costs) or demand left (attractive substitutes). In a highly competitive industry, cost savings are usually passed on through lower prices.
- Estimate the fixed/variable split: %Δ(total costs) ÷ %Δrevenue ≈ variable share; or assign a fixed % to each cost line and weight them.
- High fixed costs mean operating leverage: lost revenue cuts operating income and margins more than proportionally. A sensitivity table shows results across a grid of assumptions.
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