Monte Carlo simulation (retirement)
Also: Monte Carlo simulation, Monte Carlo retirement, stochastic retirement simulation
A retirement projection technique that runs thousands of randomized market scenarios against a portfolio and spending plan, producing a success probability rather than a single deterministic outcome.
Monte Carlo simulation projects retirement outcomes by running many randomized paths of returns, typically drawn from historical return distributions or a capital-markets assumption set. Each path applies to the portfolio net of withdrawals, and the software records whether the plan survives to the ending horizon. The output is a success rate: “87% probability of meeting goals” for example. A planner can vary inputs, retirement age, spending, Social Security claim date, asset allocation, to see how each changes the success rate.
Example: a couple plans to retire at 55 with $4.5 million, spending $180,000 per year adjusted for inflation, to age 95. A Monte Carlo simulation with 1,000 paths produces a 72% success rate. Reducing spending to $160,000 raises success to 88%. Adding $500,000 of part-time work income for five years raises it to 94%.
Common mistake: treating a Monte Carlo success rate as a precision output. The results are only as valid as the input distributions, and most consumer tools use symmetrical normal distributions that understate tail risk. Use them for direction, not precision.
Monte Carlo matters at pre-retirement stress-testing, at major career decisions, and at annual retirement plan updates.