# Monte Carlo vs. Straight-Line: Why Your Retirement Number Is a Range
A straight-line retirement projection says: "assuming 7% annual returns, your $1M portfolio will last 30 years with $40,000/year in withdrawals." This is a useful first approximation and a misleading guide to actual retirement risk.
Markets do not deliver 7% every year. They deliver highly variable annual returns that average 7% over long periods. The sequence of those returns — which years are good, which are bad — dramatically affects whether a portfolio survives. Two portfolios with identical average returns over 30 years can have vastly different outcomes depending on when the bad years fell.
The problem with straight-line projections
A straight-line projection applies your assumed return uniformly each year. It answers the question: "what happens in the average scenario?" But retirement planning is not about the average — it is about the range. A retiree who runs out of money in the bottom 10% of outcomes has failed, regardless of what the average scenario looked like.
Straight-line projections also cannot model the most dangerous retirement risk: a bear market in the first 3–5 years of retirement, when your portfolio is at its largest and a large percentage of withdrawals come from a declining base. This is sequence-of-returns risk, and it is invisible in straight-line math.
What Monte Carlo simulation does
Monte Carlo simulation runs thousands of hypothetical market sequences using the statistical properties of historical returns — mean, standard deviation, and correlations. Each run is a different possible future. The output is a distribution of outcomes: not one number, but a fan of outcomes ranging from the best historical sequences to the worst.
The useful output is a probability: "there is an 85% probability your portfolio survives 30 years at this withdrawal rate." You can then make decisions about whether 85% is enough certainty, or whether you want to adjust the withdrawal rate, add income sources, or build in a spending buffer to improve the odds.
Interactive Model
Monte Carlo Retirement Simulator
500 simulated market sequences. See the range of outcomes — not just the average.
Probability of success
80%
Acceptable — consider a spending buffer or part-time income.
Portfolio balance fan chart — 500 simulated sequences
Simplified Monte Carlo using annual normal distribution. Does not model fat tails, correlation shifts, or inflation volatility. Real-world outcomes vary. Not a guarantee.
How to read a fan chart
A retirement fan chart shows portfolio balance trajectories over time. The median line (50th percentile) runs through the middle — this is what happens in the average scenario. Bands above and below show higher and lower percentiles.
The 90th percentile line shows a great sequence — good early returns that compound for decades. The 10th percentile line shows a bad sequence — early losses that deplete the portfolio faster than it can recover.
The key insight: if even the 10th percentile line stays above zero through your planning horizon, you have robust coverage. If the 10th percentile depletes in year 15, you have meaningful tail risk that deserves attention.
What "probability of success" actually means
A 90% probability of success means 10% of simulated scenarios failed — the portfolio ran out before the end of the horizon. Whether that is acceptable depends on:
- **What "failure" means in practice.** Running out of money at year 28 of a 30-year horizon is very different from running out at year 12. Some Monte Carlo tools show conditional failure details.
- **What flexibility you have.** A retiree who can cut discretionary spending by 15% in bad years has dramatically better survival odds than one with fixed expenses equal to their withdrawal.
- **Whether you have backup income.** Social Security, part-time work, or rental income that activates if the portfolio weakens changes the failure scenario entirely.
The planning implication
Use straight-line projections to get a rough FIRE number and gut-check your spending assumptions. Use Monte Carlo (or a probabilistic withdrawal model) to pressure-test the number before you actually retire. The goal is not a 100% success rate — that would require such a conservative withdrawal rate that you accumulate far more than you need. The goal is a success rate high enough that the failure scenarios are manageable.
Most financial planners target 85–90% success rates, with the understanding that clients will adjust spending if early-retirement returns are poor.
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*Related: [Sequence of returns risk](./sequence-of-returns-risk) explains the specific mechanism that drives bad retirement outcomes. [The 4% rule](./real-math-behind-4-percent-rule) is the starting point that Monte Carlo testing validates or challenges.*