Monte Carlo simulation explained for retirement planning

A steady-return projection assumes your investments grow by the same amount every year. Real markets rise strongly in some years and fall in others. The average may be similar, but the path is not, and the path matters once you are adding or withdrawing money.

Historical studies, such as the research behind the 4% rule, test a plan against the sequences of returns that actually happened. A simulation generates many more sequences than history provides, including ones that have not happened yet.

Our guide to the expected path and Monte Carlo compares the two approaches in detail.

1. Start from your plan: current wealth, contributions, return and inflation assumptions, target and retirement income.
2. Draw varying returns: for each month, pick a return at random from a distribution centred on your assumption.
3. Run one path: apply those returns month by month, adding contributions and taking withdrawals, to produce one possible future.
4. Repeat many times: each path uses a different sequence of returns.
5. Summarise: count how many paths reach the target, when they reach it, and whether the money lasts.

Yield Clarity runs 10,000 paths in monthly steps. The exact assumptions, including volatility, fees and inflation, are set out in the methodology rather than repeated here.

The line in the middle is the median: half of paths are above it and half below. The bands widen over time because small differences in returns compound. A wide fan means the outcome is uncertain; it does not mean the plan is wrong.

A probability of about half is normal when you test the expected-path date itself: that date sits near the middle of the outcomes. Testing a date a few years later gives a higher probability; testing an earlier one gives a lower probability.

The FI window is the range of dates by which the middle half of successful paths reach the target. In the illustrative plan it runs from October 2033 to November 2037, with a median of August 2035. A quarter of paths that reach FI do so earlier than the window and a quarter later.

A simulation can also continue past retirement, drawing your income each month, to see how often the money lasts. In the illustrative plan, spending stays funded for 15 years in 99% of paths, through May 2050.

The paths that run out are mostly those with poor returns early in retirement. Our guide to sequence-of-returns risk explains why.

• Assumptions drive results. A slightly different return or volatility can move the probability noticeably.
• Smooth models miss extremes. Real markets have more sudden crashes and long slumps than most simulations produce.
• History may not repeat. Long-run returns have differed widely between countries (Pfau, 2010).
• People adapt. Simulations usually keep spending fixed, while most people cut back after a bad year.

The methodology limitations list exactly what Yield Clarity's simulation does and does not model.

Monte Carlo is most useful for comparisons. Asking whether saving an extra £500 a month meaningfully improves your probability is more informative than debating whether 48% is good enough.