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Scenario analysis around a DCF

Sensitivity analysis changes one or two variables at a time. Scenario analysis is more ambitious: you build multiple complete DCF models—bull case, base case, bear case—where assumptions move together in economically coherent ways. A bull case assumes strong revenue growth, margin expansion, and a higher exit multiple because the competitive position strengthens. A bear case assumes slower growth, margin compression, and a lower exit multiple because competition intensifies. Each scenario tells a story; numbers follow the narrative. Scenario analysis is the bridge between math and judgment.

Quick definition​

Scenario analysis in DCF modeling means building multiple complete valuations for different plausible futures. The bull case is optimistic but defensible. The base case is your most likely outcome. The bear case is pessimistic but realistic. You then assign probabilities to each scenario and compute an expected value. This forces coherent storytelling and prevents you from arbitrarily combining optimistic revenue assumptions with pessimistic margin assumptions.

Key takeaways​

  • Scenarios are narratives first, numbers second: each scenario should tell a coherent story about the business, competitive landscape, and market conditions.
  • Bull, base, and bear cases should be plausible and defensible, not fantasy. A 20% average probability per scenario across many bulls is overconfidence.
  • Assign probabilities based on historical frequencies, competitive dynamics, and management track record. Be honest about base-case probability; it's often lower than investors think.
  • Compute an expected value across scenarios using probability weighting. This is more realistic than relying on a point estimate from the base case alone.
  • Scenario analysis is a forcing function: it prevents you from building a DCF that's too optimistic and then anchoring to it.

The three-scenario framework​

Bull case: favorable dynamics, all bets win. Revenue grows faster than consensus because the company captures share in a growing market or enters adjacent markets successfully. Operating margins expand as the company achieves scale and pricing power. The exit multiple is higher because the competitive position has strengthened and the business trades at a premium to peers. Probability: 20–35%.

Base case: most likely path, realistic growth and margins. Revenue grows in line with the market or slightly faster due to incremental share gains. Margins stabilize near current or slightly elevated levels. The exit multiple reflects current peer comparables. Probability: 40–50%.

Bear case: headwinds and execution risks materialize. Revenue growth disappoints due to market saturation, new competitors, or product failure. Margins compress from pricing pressure or operational inefficiencies. The exit multiple declines because the competitive position weakens. Probability: 15–30%.

Notice the probabilities sum to 100%. This forces honesty: you cannot assign 50% to the bull case and 50% to the base case if you want a coherent probability distribution.

Building a narrative for each scenario​

Start with the story, not the numbers. For each scenario, write down:

  1. Market conditions. What's the economic environment? Is demand growing or shrinking? Are interest rates stable or rising?

  2. Competitive dynamics. How does the company's competitive position change? Does it gain or lose share? Do new entrants emerge?

  3. Operational execution. Does management execute the strategy? Does the business reach the unit economics it's targeting?

  4. Margin evolution. Why do margins expand or compress? Is it pricing power, cost management, mix, or leverage?

  5. Multiple re-rating. Why does the exit multiple change? Is it because risk has declined, growth has accelerated, or the business has become more defensive?

Example: Tesla bull case narrative (hypothetically, early 2020s).

  • Market: Electric vehicle adoption accelerates globally due to climate policy and consumer preference shift.
  • Competition: Tesla maintains technology and cost advantage; limited direct competition despite new entrants trying.
  • Execution: Factories ramp, production costs decline faster than peers forecast, manufacturing becomes a moat.
  • Margins: EBITDA margins expand from 15% (year 3) to 22% (year 10) due to scale and mix shift to higher-margin vehicles.
  • Multiple: Tesla exits at 18x FCF because the market recognizes it as a durable compounder, not a cyclical auto manufacturer.
  • Probability: 25% (optimistic but not fantasy).

Tesla bear case narrative:

  • Market: EV adoption plateaus in developed markets; regulatory support weakens; economic recession suppresses demand.
  • Competition: Traditional OEMs deploy capital and begin to compete on price; Tesla's cost advantage erodes.
  • Execution: Factory ramps disappoint; production growth misses targets; quality issues persist.
  • Margins: EBITDA margins compress from 15% to 9% by year 10 as Tesla trades on price to defend share.
  • Multiple: Tesla exits at 10x FCF because it becomes a cyclical auto business with thin margins, not a tech compounder.
  • Probability: 20% (pessimistic but plausible given auto-industry histories).

Linking narrative to numbers​

Once you have a narrative, the numbers follow logically.

Bull case assumptions:

  • Revenue CAGR years 1–5: 25%
  • Year 5–10 CAGR: 18%
  • Year 10 EBITDA margin: 22%
  • Exit multiple: 18x FCF
  • WACC: 8% (slightly lower because execution risk has declined)

Base case assumptions:

  • Revenue CAGR years 1–5: 15%
  • Year 5–10 CAGR: 10%
  • Year 10 EBITDA margin: 16%
  • Exit multiple: 13x FCF
  • WACC: 9%

Bear case assumptions:

  • Revenue CAGR years 1–5: 5%
  • Year 5–10 CAGR: 3%
  • Year 10 EBITDA margin: 9%
  • Exit multiple: 8x FCF
  • WACC: 10% (higher because execution risk has materialized)

Notice: bull case margins are higher because scale has been achieved; bear case WACC is higher because risk has increased. The numbers reflect the narrative coherently.

Computing expected value across scenarios​

Once you have three valuations and probabilities:

Expected value = (Bull EV × Bull probability) + (Base EV × Base probability) + (Bear EV × Bear probability)

Example:

  • Bull: $120B × 25% = $30B
  • Base: $80B × 50% = $40B
  • Bear: $40B × 25% = $10B
  • Expected value: $80B

Notice: your expected value ($80B) equals your base case valuation ($80B) in this example because the base case is weighted at 50%. If you had weighted it at 40%, the expected value would be lower: ($120B × 30%) + ($80B × 40%) + ($40B × 30%) = $76B.

Scenario analysis reveals overconfidence​

Many investors build a "base case" that's actually a bull case and anchor to it. Scenario analysis prevents this. If you run a full bull, base, and bear DCF and realize your so-called base case valuation is above the expected value of all three scenarios (properly weighted), you've been overconfident.

Example: You build a "base case" DCF that values the company at $100B. You assume 12% revenue growth and 18% margins. You claim this is 50% probable. But when you build a true base case (10% revenue growth, 16% margins) and price it at $75B, and a bull case at $130B, suddenly your three-scenario expected value is $87.5B—below your anchored estimate. This tells you the market's $80 stock price might not be as cheap as you thought.

When scenarios diverge sharply​

If your bull case is $150B and your bear case is $30B, that 5x spread suggests you're highly uncertain about the business. Use this as a forcing function: which key assumption explains the divergence? Is it revenue growth (bull assumes 20%, bear assumes 5%)? Is it margins (bull assumes 20%, bear assumes 5%)? Once you identify the key driver, you can research it more deeply.

Scenario analysis with wide divergences is not a bug; it's useful information. It tells you where to focus diligence.

Real-world example: Netflix scenario analysis (2021 era)​

Bull case narrative: Streaming dominance globally, price increases accepted, ad tier reaches scale, operating margin expands to 22%, exits at 20x FCF.

  • EV: $210B
  • Probability: 30%

Base case narrative: Streaming penetration matures in developed markets, modest growth in emerging markets, price increases modest, operating margin stabilizes at 18%, exits at 15x FCF.

  • EV: $120B
  • Probability: 50%

Bear case narrative: Subscriber growth flattens, churn accelerates due to competition, price reductions necessary, margin compression to 12%, exits at 10x FCF.

  • EV: $55B
  • Probability: 20%

Expected value: ($210B × 30%) + ($120B × 50%) + ($55B × 20%) = $63B + $60B + $11B = $134B

If Netflix trades at $170 (implying ~$210B market cap), the stock looks expensive relative to your expected value of $134B, even though your bull case is $210B. The probability weighting reflects your actual conviction, not the bull case alone.

Scenario probability assignment: common mistakes​

Mistake one: assigning probabilities that don't reflect conviction. If you assign 50% to the base case but your research is 70% convinced the bull case will happen, you're not being honest. Adjust probabilities to your actual beliefs. You're allowed to have 70% base case and 30% bull case if that's your conviction.

Mistake two: using equal weightings (33%, 33%, 33%). This suggests you have no view on which outcome is most likely. Almost never true. Be specific.

Mistake three: assigning probabilities that sum to 95% or 105%. They should sum to 100%. If you're unsure, round them until they do.

Mistake four: treating historical frequency as current probability. If a company has grown 15% CAGR over the past five years, that doesn't mean it will grow 15% in the next five. Your scenarios should reflect forward-looking competitive dynamics, not past performance.

Scenario analysis and margin of safety​

Your expected value from scenario analysis is your intrinsic valuation. A margin of safety is a discount to that valuation. If your expected value is $80B and the stock trades at $60B (market cap), the margin of safety is 25%. You can feel confident buying because even moderate downside doesn't sink the position.

If expected value is $80B and the stock trades at $75B, the margin of safety is only 7%. That's thin. You'd need high conviction in your base and bull cases to invest at that price.

Scenario analysis in a changing environment​

Periodically update your scenarios as new information arrives. If your bear case assumed a recession and the economy is accelerating, update the bear case to reflect a less severe recession or a new bear thesis (e.g., competitive disruption). Scenarios are not static; they evolve with the business and market.

FAQ​

Q: How many scenarios should I build?
A: Three (bull, base, bear) is standard and sufficient. Some analysts add additional scenarios (e.g., turnaround case for a distressed company), but three capture the key range.

Q: Should my base case be the middle of the bull and bear cases?
A: Not necessarily. Your base case should be your single best estimate of the most likely outcome. It may be closer to the bear than the bull if you're pessimistic, or vice versa.

Q: What probability should I assign to the base case?
A: Typically 40–60% depending on confidence. If you're very uncertain, 40%. If you have high conviction, 60%. The remaining 40–60% is split between bull and bear, often in a 2:1 ratio (bull 25–30%, bear 15–20%).

Q: Can I have four or five scenarios?
A: You can, but each additional scenario dilutes clarity. More common is to add a scenario if the business has a specific non-linear outcome (e.g., a cash compounder that can either invest for growth or return capital, creating two very different outcomes). Stick with three unless there's a clear reason not to.

Q: Should the base case probability be higher than bull and bear combined?
A: Usually. The base case is your best guess; it should be more probable than the two extreme scenarios. But not always: if you have high conviction in an upside scenario and lower confidence in the current state, the bull case can be your most probable outcome.

Q: How do I communicate probabilities to others?
A: Be explicit: "I assign 50% to the base case, 30% to the bull, and 20% to the bear." This removes ambiguity. Show your working for how probabilities were derived (e.g., "The base case reflects management's guidance; the bull case reflects 2019–2020 performance levels").

  • Sensitivity analysis — changing individual assumptions in a grid; scenario analysis moves multiple assumptions together coherently.
  • WACC and risk — scenarios should reflect different risk profiles (bear case has higher WACC due to increased risk).
  • Terminal value — each scenario should have its own terminal value assumption reflecting the long-term competitive position.
  • Margin of safety — the difference between your expected intrinsic value and the market price.
  • Base-case forecast period — the explicit forecast period may differ across scenarios; bull case may have higher revenue growth early.

Summary​

Scenario analysis forces you to build multiple coherent narratives for the business's future, each with its own assumptions and valuation. Bull cases are optimistic but defensible; base cases reflect your most likely outcome; bear cases are pessimistic but plausible. Assign probabilities that reflect your conviction, compute an expected value, and use that as your intrinsic valuation. The wide gap between scenarios (if it exists) reveals key assumptions you should research more deeply. Scenario analysis prevents overconfidence in a single base case and ensures your investment thesis is robust to misses in growth, margins, or competition.

Next​

Monte Carlo simulations on a DCF — Read article 23

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