Probability-Weighted Scenario Valuation
Rather than forcing an uncertain future into one "base case" DCF, this method builds several distinct futures, values each one, and blends them by how likely each is judged to be.
Prerequisites: FCFF vs FCFE: Which Cash Flow to Discount
A biotech company's value depends almost entirely on whether its single drug candidate passes a Phase 3 trial. A single "base case" DCF has to pretend this is one smooth, average outcome — but the real world isn't smooth here: the drug either works and the company is worth billions, or it fails and the company is worth close to nothing. Averaging those two extremes into one continuous forecast hides exactly the information that matters most.
Probability-weighted scenario valuation builds a small number of distinct, internally consistent future scenarios — not gentle variations on one story, but genuinely different outcomes — values the company under each one, assigns each a probability, and combines them into a single expected value. It is the natural method whenever a company's future hinges on a small number of discrete, resolvable events.
Building scenarios that actually differ
A common mistake is building "optimistic," "base," and "pessimistic" cases that are really just the same story with growth rates nudged up or down a point or two — that's sensitivity analysis wearing a scenario-analysis costume, and it misses genuinely different futures. Real scenarios should correspond to distinct, identifiable events or states of the world: the drug trial succeeds versus fails; the company wins versus loses a major litigation; a key patent is upheld versus invalidated; a regulator approves versus blocks a merger.
Each scenario gets its own full valuation — its own cash flow forecast, sometimes its own discount rate if the risk profile genuinely differs by scenario — and its own explicit probability, ideally grounded in some evidence (historical trial success rates for a given drug class, base rates for similar litigation outcomes) rather than a round number picked for convenience.
In words: the company's expected value is the sum, across every scenario, of that scenario's probability multiplied by what the company would be worth if that scenario came true.
Worked example
A clinical-stage biotech is valued under three scenarios, each with its own full DCF.
- Trial fails, 60% probability: company survives on existing assets, valued at $2.00 per share.
- Trial succeeds, narrow approval, 25% probability: valued at $18.00 per share.
- Trial succeeds, broad approval, 15% probability: valued at $45.00 per share.
Probability-weighted value: (0.60 \times 2.00) + (0.25 \times 18.00) + (0.15 \times 45.00) = 1.20 + 4.50 + 6.75 = \12.45$ per share.
Note this expected value doesn't equal any single scenario's outcome — it's a genuinely blended number that only makes sense as a probability-weighted average across mutually exclusive futures, not as "what will happen."
What this means in practice
This method is standard in biotech and pharma (trial outcomes), litigation-heavy situations (settlement versus loss), regulatory-dependent businesses (approval versus rejection), and any company facing a single dominant, binary source of uncertainty. It also naturally produces a full distribution of possible outcomes, not just one number, which is often more useful to a decision-maker than a single point estimate that hides how much risk sits underneath it.
The expected value from this method can be a number that no single scenario actually produces, and it's easy to mistake it for "the most likely outcome" — it isn't. In the example above, the single most likely outcome is the 60%-probability failure case at $2.00 per share, not the $12.45 blended figure — the blend is pulled upward by two lower-probability, high-payoff scenarios. Report the full scenario table alongside the blended number, not the blended number alone.
Related concepts
Practice in interviews
Further reading
- Damodaran, Investment Valuation (ch. 'Probabilistic Approaches in Valuation')