Monte Carlo Simulation | Investment Risk Analysis Guide
Monte Carlo simulation is a financial modelling technique that projects thousands of possible future portfolio values by sampling random returns from a statistical distribution, rather than assuming a single fixed return each year.
Instead of a single line showing "your portfolio grows at 5% a year", a Monte Carlo simulation runs many hundreds or thousands of scenarios — some good years, some bad — to show you the spread of possible outcomes and the probability of meeting your financial goals.
Why Monte Carlo Simulation Matters for Investors
- Captures uncertainty — markets do not deliver smooth average returns; Monte Carlo reflects the real volatility investors face
- Shows probability of success — rather than a single projected number, you see the likelihood of reaching your target
- Stress-tests your plan — you can see how your portfolio behaves in adverse sequences, such as a market crash in early retirement
- Avoids false precision — deterministic calculators that show "you will have £X in 20 years" overstate certainty; Monte Carlo is more honest
How the Simulation Works
Each trial samples a random return for every year of the projection from a statistical distribution (usually lognormal, which reflects compounding and keeps portfolio values above zero). After running hundreds of trials, the tool shows the range of outcomes — typically the 10th, 50th, and 90th percentile paths — so you can see a realistic best, median, and worst case.
Frequently Asked Questions
- What return model does the simulation use?
- It can use a normal distribution for simplicity or a lognormal distribution that reflects compounding and keeps values above zero. The default is lognormal.
- How many trials should I run?
- A few hundred gives a quick read. One thousand or more gives a more stable picture. More trials take longer to compute.
- Why do results vary each time?
- Results change because each run samples new random paths. If you set a seed and keep inputs the same, you can reproduce a run.
- Does a higher expected return always win?
- Not always. Higher expected return often comes with higher volatility. That extra volatility can reduce the chance of meeting a goal on time even if the average looks better.