Monte Carlo Simulation

A Monte Carlo simulation is a mathematical modeling method that evaluates a financial plan by running it through a large number of randomized trials — commonly 1,000 or more — each drawing a different possible sequence of investment returns (and sometimes inflation and lifespan). Rather than projecting a single future based on average returns, it produces a distribution of outcomes and reports the percentage of trials in which the plan met its goal, most often whether retirement savings lasted for life. The name comes from the casino district of Monaco, a nod to the method's reliance on chance.

The technique exists because straight-line projections hide a critical risk. A plan assuming a steady 6% every year can look identical to one that averages 6% with brutal early losses — but the retiree living through the second path may run out of money, because withdrawals taken during a downturn permanently remove shares that would have fueled the recovery. That is sequence-of-returns risk, and randomizing thousands of return orderings is precisely how Monte Carlo surfaces it.

Reading the output takes judgment. A 100% success rate is not the goal — reaching it usually means oversaving or underspending, paying real, certain lifestyle costs to eliminate hypothetical tail risks. Many planners regard results in the 70s–90s as a workable zone depending on the client's flexibility, because the model quietly assumes the retiree never adapts — never trims spending in a bad market, never earns another dollar — when real people adjust constantly. Limitations to keep in view: results swing with the assumed returns and volatility (garbage in, garbage out); many models draw from statistical distributions that understate extreme events; and a "failure" in the model often means falling slightly short at 95, not destitution at 80. The number is a conversation starter about trade-offs, not a verdict.

The planner encodes the plan — current savings, contributions, retirement date, spending, Social Security, asset allocation — and the software generates thousands of randomized return sequences, tallying the share of paths where the money lasts.

A hypothetical example: Ray and Elena, both 60, have $1,200,000 saved and want to spend $60,000 a year from the portfolio starting at 62. A simulation of 1,000 randomized paths finds the money lasts to age 95 in 790 of them — a 79% success rate. The model then makes trade-offs concrete: cutting planned spending to $54,000 lifts success to roughly 88%; delaying retirement to 64 (two more years of contributions, two fewer of withdrawals) pushes it past 90%; doing both approaches the high 90s. (All figures hypothetical, for illustration.) None of these numbers is a guarantee — but they let the couple compare options by impact instead of by gut feel.

Pros

• Captures sequence-of-returns risk that single-average projections mathematically cannot show.
• Turns vague worry ("will we be okay?") into a measurable dial that responds to specific decisions.
• Makes trade-offs comparable — one more working year vs. $6,000 less annual spending — in a common currency of success probability.

Cons

• Output quality is hostage to input assumptions; optimistic return assumptions manufacture false comfort.
• Common implementations understate rare, extreme markets and assume the retiree never adapts behavior mid-plan.
• A precise-looking "82%" invites false confidence — two software packages with different assumptions can score the same plan very differently.
• Chasing 100% success typically means unnecessarily working longer or spending less for protection against already-remote scenarios.