UNDERSTAND THE METHOD
How Monte Carlo simulation turns financial assumptions into a range of outcomes
Monte Carlo simulation repeatedly draws values from specified input distributions and recalculates a financial model. The resulting distribution describes outcomes under those assumptions. It broadens a single forecast into a range, but it does not turn uncertain assumptions into verified probabilities about the real world.
| Stage | Financial meaning | Question to review |
|---|---|---|
| Input distributions | Represent the uncertainty you choose to model | What evidence supports the range, shape and dependence assumptions? |
| Repeated calculations | Pass each draw through the connected model | Do the financial relationships remain valid across the tested range? |
| Output distribution | Summarize valid modeled outcomes | Which threshold or tail matters for the decision? |
On this page
A small operating example makes the mechanism visible
A synthetic business sells 100 units. Price is assumed uniformly distributed between 9 EUR and 11 EUR. Variable cost is 5 EUR per unit and fixed cost is 300 EUR. Volume and costs are not uncertain in this example. All monetary amounts below are EUR.
Input distribution → sampled price → revenue and linked costs → EBITDA. With these assumptions, EBITDA = 100 × Price − 800. The three rows below are selected deterministic recalculations through the native model, not three random samples or three equally likely scenarios.
| Selected price | EBITDA | Meaning |
|---|---|---|
| 9 EUR | 100.00 EUR | Lower edge of the assumed price range |
| 10 EUR | 200.00 EUR | Base assumption |
| 11 EUR | 300.00 EUR | Upper edge of the assumed price range |
What a distribution adds to a financial discussion
A range alone says which values are allowed. A distribution also assigns relative weight within that range. Uniform means equal probability for equal-width intervals; it does not mean every future price is known to be equally plausible.
Repeated calculations can answer a conditional question such as how often modeled EBITDA falls below a chosen threshold. For a cash target, review both the threshold and its period: a year-end cash distribution does not establish whether cash remained sufficient during every earlier week.
Professional use begins with a decision, not a histogram
CFA Institute presents simulation as a way to examine investment strategies in hypothetical environments. RiskNET describes risk-oriented planning through distributions of financial outcomes. These sources illustrate professional contexts; they do not validate this synthetic company or imply that BalanceCheat implements every method they discuss.
| Context | Useful question | Necessary qualification |
|---|---|---|
| Operating planning | How wide could modeled earnings vary? | Document the input evidence and cost relationships |
| Liquidity review | How often does modeled cash miss a chosen threshold? | Check timing, funding availability and invalid results |
| Investment analysis | How does an assumed uncertainty change an output? | Keep model risk separate from a measured market probability |
Limits that remain after thousands of runs
More runs reduce sampling noise; they do not repair an unsuitable distribution, a missing risk or an incorrect model. Extreme events outside the input support cannot appear. The output may look precise even when the assumptions are weak.
BalanceCheat samples configured uncertain inputs independently. Existing modeled dependencies, such as variable cost linked to volume, still recalculate. An explicit correlation matrix is not available. This is not a derivatives-pricing service or an investment recommendation.
Choose the next question
To configure the actual example, use the Monte Carlo financial-model workflow. For method selection, see Monte Carlo vs sensitivity. For the meaning of the result labels, see P10, P50 and P90.
Implementation details belong in Stochastic and stochastic dependencies. The Analyze overview places simulation alongside the other analysis Layers.
Sources and conventions
Synthetic examples. Model-based results were reconciled with the current BalanceCheat calculations; assumptions, units and boundaries are specified in each example.