A straight-line projection compounds one assumed return and gives you one number. It is easy to read and it is almost always wrong, because markets do not deliver the average — they deliver a sequence, and the sequence matters.
Monte Carlo replaces the single path with thousands. Each run draws a different sequence of returns from a model of how markets behave, applies your contributions and withdrawals, and records where the plan ends up. The output is a distribution: a median, a range, and the share of runs in which the money lasted.
The value is that it converts a false precision into an honest range. A projection of $4.3 million becomes "the median is $2.9 million, and $4.3 million is around the 71st percentile" — which is a materially different thing to plan against.
The method is only as good as the return model behind it. If each year is drawn independently from one distribution, the simulation will understate the clustered downturns that actually break plans. Regime-switching models, which let markets persist in calm or stressed states, produce fatter and more realistic bad tails.
Research on this
See also
- Probability of success — The share of simulated runs in which a plan funds its spending through the full horizon without exhausting the portfolio.
- Sequence of returns risk — The risk that the order in which investment returns arrive damages a plan, even when the average return is unchanged. It matters most when money is being withdrawn.
- Volatility drag — The gap between the average of a series of returns and the compound growth actually achieved. Higher volatility widens the gap, even with an unchanged average.