Bull Case, Bear Case, Scenarios: Investing Means Doing the Math on Uncertainty
“Price target: €187.” That is how precise analyst forecasts sound – and how regularly they miss. This is not a dig at analysts; it is simply the nature of the problem. The future of a company is not a number, it is a range of possible paths. Whenever you invest, you decide under uncertainty. The good news: uncertainty is something you can do the math on. That is exactly what scenario thinking is for – bull case, base case, bear case. In this guide you will build that tool for yourself, step by step.
Why single-point forecasts fail
A point forecast (“fair value is €187”) pretends the future can be compressed into a single number. In reality, the value of a stock rests on assumptions that are themselves uncertain: revenue growth, margins, interest rates, competition. Even small deviations compound each other. One example: if a company grows at 8% instead of 10% a year for ten years, revenue ends up roughly 17% lower – and because valuation models such as the DCF method discount future profits, a deviation like that hits fair value disproportionately hard.
Then there is the psychology: a single number creates false precision. It feels like knowledge, even though it describes only one of many possible futures. If you anchor on a point forecast, every deviation catches you by surprise. If you think in scenarios, you have already rehearsed the deviation before it happens.
Bull, base, bear: how to build three scenarios
A scenario is not a piece of fiction. It is a consistent combination of drivers, assumptions and the value that follows from them. Three scenarios are almost always enough: an optimistic path (bull), a realistic one (base) and a pessimistic one (bear).
Step 1: Find the two or three drivers that actually matter
Every investment thesis hangs on a handful of levers. For a software company that might be user growth and pricing power; for an auto supplier, order intake and margin; for a bank, interest rates and loan losses. Ask yourself: which two or three variables will decide, five years from now, whether this investment worked? Everything else is noise. Our guide on how to analyze stocks shows how to derive these drivers systematically from the business model and the key financial metrics.
Step 2: Set concrete assumptions for each scenario
Now each scenario gets numbers. Important: the bull case is not “everything goes perfectly” but a plausibly good path – say, the upper end of what the company has already achieved historically, or what its best competitors demonstrate is possible. The bear case, in turn, is not the end of the world but a plausibly bad path: growth halves, margins fall back to the industry average, a major customer walks away. Extreme scenarios (total loss, a tenfold gain) are worth being aware of, but they do not belong at the center of the calculation.
Step 3: Estimate rough probabilities
Nobody knows exact probabilities – and nobody needs to. A rough, honest weighting in steps of ten is enough, for example 25% bull, 50% base, 25% bear. What matters is less the exact number than the argument you have with yourself: if you want to give the bull case 60%, you need evidence for that – not just enthusiasm. Rule of thumb: the base scenario should be the most likely one; if it is not, it is not your base scenario.
Expected-value thinking: a fully worked example
The expected value ties the three scenarios together into a basis for a decision: each scenario value is multiplied by its probability, and the results are added up. Suppose a stock trades at €100 today and, on a three-year view, you estimate:
| Scenario | Assumption | Value per share | Probability | Contribution |
|---|---|---|---|---|
| Bull | Growth holds, margin expands | €160 | 25% | €40.00 |
| Base | Solid growth, margin stable | €110 | 50% | €55.00 |
| Bear | Growth collapses, margin falls | €60 | 25% | €15.00 |
Expected value: 40 + 55 + 15 = €110 – roughly 10% above today’s price. That is positive, but no free ride: in the bear case you are looking at a 40% loss. Whether a 10% expected return is worth that risk is a second, separate decision – and a question of position sizing, in other words of portfolio diversification.
The real payoff of the calculation shows up when you vary the assumptions. If the bear case sits at €40 instead of €60 (because of heavy debt, say), the expected value drops to €105 – the margin of safety is almost gone. If, on the other hand, the stock trades at €85 instead of €100, the very same estimates deliver close to a 30% expected return. That is how “I like this stock” turns into a testable statement: at what price is this stock a good deal – and at what price is it no longer one?
A second key insight: two stocks with the same expected value are not equally good. A 10% expected return with a range from −10% to +25% is something entirely different from a 10% expected return with a range from −60% to +90%. Asymmetry is what counts: you are looking for situations where the bear case is limited and the bull case is large – not the other way around.
Scenarios as a defense against confirmation bias
Confirmation bias – the tendency to collect only evidence that supports your existing view – is one of the most expensive investing mistakes there is. Scenario thinking counters it almost mechanically: if you have to write out a bear case, you are forced to research the strongest counterarguments yourself instead of brushing them aside. “The bears just don’t understand the business model” becomes a concrete question: what exactly would have to be true in their arguments, and what would it cost you?
A practical tip: write the bear case so convincingly that a skeptic would sign off on it. If you cannot come up with anything substantial, you have either found an exceptional company – or, far more likely, you have not looked hard enough yet.
Pre-mortem: what would have to happen for the thesis to break?
A pre-mortem flips the perspective: imagine it is three years from now and the investment has failed – the stock is down 50%. What happened? Experience shows this backward-looking question produces more honest answers than the forward-looking “what could go wrong?”, because it treats failure as a fact rather than a distant possibility.
Write down two to four concrete breaking points of your thesis, for example: “the largest customer (30% of revenue) does not renew”, “gross margin falls below 40% for two consecutive quarters”, “a competitor undercuts prices by more than 20%”. Here is the trick: these breaking points are observable. The pre-mortem turns into a set of checkpoints you tick off with every quarterly report. If a breaking point occurs, you sell or you rerun the numbers – without drama, because you made the decision back when you were still calm.
The market prices in scenarios – the question is: which ones?
A stock price is itself something like an expected value: the weighted sum of everything market participants consider possible. That is why the right question is never “is this a good company?” but “which scenario is already in the price?” If a quality stock trades at 45 times earnings, the market has largely front-run the bull case: even if it plays out, little return is left – and the base case is already enough for disappointment. Conversely, a stock at 6 times earnings may already have a very dark scenario priced in; in that case you can sometimes make money simply because things turn out bad instead of catastrophic.
This is exactly why structured analysis works with ranges instead of point targets – the AI report from aktienanalyse.ai also outputs a fair-value range with an explicit bull and bear case, so you can apply your own probabilities to it instead of adopting a single number.
Bottom line: calculate instead of guessing
Point forecasts fail because they hide uncertainty. Scenarios make it visible and manageable: identify the two or three drivers that truly matter, set plausible assumptions per scenario, weight them roughly, compare the expected value with the current price – and use a pre-mortem to record how you will know you are wrong. This takes about an hour per stock and improves the quality of your decisions more than any additional price-tracking app. Not because the numbers are exact, but because you are forced to state your assumptions out loud before the market puts them to the test.