Monte Carlo Simulation Calculator guide
Monte Carlo simulation samples uncertain assumptions repeatedly to show a range of possible outcomes rather than one forecast.
The preserved calculator includes distributions, percentiles, correlations, risk metrics, charts, and scenarios.
How to use the Monte Carlo simulation calculator
- Enter current amounts: Use current, documented values from the same relevant period.
- Enter assumptions: Use realistic rates, percentages, periods, and costs where applicable.
- Review the full result: Review the primary estimate together with its supporting measures.
- Stress-test risk: Model less favorable timing, value, cost, or rate assumptions.
Formula and variables
The estimate applies the entered values and assumptions to the stated formula.
Simulated profit = sampled revenue − sampled costs- Inputs — Entered values
- The amounts, percentages, or periods supplied to the calculator.
- Result — Calculated output
- The estimate produced by applying the formula to the entered values.
Worked example: Monte Carlo simulation calculator
A user enters a representative set of values and assumptions.
- Key inputs
- Amounts, percentages, periods, and costs
- Apply the stated formula.
- Include all relevant entered values and constraints.
- Compare the result with an alternative scenario.
Result: Mean and median profit, percentiles, loss probability, confidence range, and distribution charts.
Use the estimate as a planning input and verify important decisions with current records or qualified guidance.
Understanding your results
Primary estimate
Mean and median profit, percentiles, loss probability, confidence range, and distribution charts.
Risk measures
Use supporting payment, leverage, cost, and cash figures together.
Assumptions
- Entered rates and costs remain constant.
- Payments and cash flows occur on schedule.
Limitations
- Taxes, legal terms, accounting treatment, and transaction-specific costs may differ.
- Future values, timing, and rates are uncertain.
Common mistakes
- Reviewing only the headline result.
- Ignoring relevant costs, timing, or supporting measures.
- Using optimistic timing or value assumptions.
- Treating an estimate as a guaranteed outcome.
Practical use cases
Compare scenarios consistently
Change one assumption at a time or enter each alternative using the same basis.
Plan cash requirements
Estimate funds needed before committing.
Planning and decision guide
Stress-test the assumptions
Simulation quality depends on assumptions and distributions.
Review the important risks
Correlation between variables can materially change risk.
Verify the source values
Results are scenarios, not predictions.
Frequently asked questions
What is Monte Carlo simulation?
Repeated random sampling of uncertain inputs to estimate an outcome distribution.
How many simulations?
More runs generally stabilize estimates but do not fix poor assumptions.
What is loss probability?
The share of modeled outcomes below zero profit.
Sources and review
- Risk and return — Investor.gov. Accessed 2026-07-10.
Reviewed 2026-07-10.