Monte Carlo simulation
A Monte Carlo simulation plays through thousands of possible price paths and shows a range instead of a single number. The median is the middle outcome, not the most likely individual value. The result describes the spread of the model, not the future.
- Rarely reached
- In most simulated paths the goal is missed. The levers are savings rate, horizon and target amount, not the return assumption.
- Marginal
- The goal is within reach but depends heavily on the path. A weak start costs disproportionately here.
- Likely
- The clear majority of paths reach the goal. The usual target corridor for long-term savings plans.
- Very likely
- Nearly every path reaches the goal. Check whether the target is set too low or the plan is unnecessarily defensive.
How it is calculated
For each of n paths: value[t+1] = value[t] × ( 1 + random return ) + contribution
Thousands of paths produce a distribution of end values. The percentiles cut it at three points. Between the 5th and the 95th lie 90 % of all simulated outcomes.
- 5th percentile
- worst 5 %
- Median
- the middle
- 95th percentile
- best 5 %
- Range
- 90 % of paths
What the number does not tell you
- The assumptions determine the result entirely. Expected return and volatility are inputs, not findings. Assuming 8 % instead of 6 % produces a friendlier picture without anything changing in the portfolio.
- The median is not an expected value for you. You live through exactly one path, not the average of a thousand. Half of all paths end below the median. Reading only that figure means planning on a coin flip.
- Random returns understate crises. Real markets have crashes that cluster across weeks. Models drawing daily returns independently produce such runs less often and therefore paint the tails too kindly.
- Tax, costs and inflation are usually missing. An end value of 250,000 € in twenty years is nominal. What remains in real terms depends on inflation and taxation, and is considerably less.
Related metrics
How Evergrova calculates it
The projection page simulates paths over the chosen horizon, including savings rate and rebalancing rule. It reports the median, 5th and 95th percentile, the distribution of end values, the maximum drawdown across paths, and the goal-reached rate.
Calculation: Monte Carlo simulation over the chosen horizon; results are model values before tax and are not a forecast.
View the demo portfolioCommon questions
How many simulations make sense?
A few thousand paths suffice for stable percentiles. More runs make the result smoother but not more accurate: the uncertainty sits in the assumptions, not in the number of draws.
Why is the range so wide?
Because volatility compounds over time. At 15 % volatility across twenty years, the 5th and 95th percentile often differ by a factor of three or more. A narrow range would be the less realistic result.
Monte Carlo or backtest?
A backtest shows what happened to a strategy in the past, that is one actual path. Monte Carlo shows how widely possible outcomes spread. For goal planning, the range is the more honest basis.
Last reviewed: 2026-08-08