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Monte Carlo Retirement Simulation

Traditional retirement calculators give you one answer: with 5% annual returns, you'll have X in 30 years. But markets don't deliver average returns every year — some years +20%, some −15%. Monte Carlo runs 1,000 random scenarios and reports success probability, not a single number.

Inputs

Amount withdrawn each year; scaled by inflation during the simulation.

Market assumptions

Standard deviation reflects market volatility. Historical 60/40 portfolio: ~10–12%; all-equity: ~15–18%.

Monte Carlo uses random sampling, so each run varies slightly.

Simulation results
Success rate
65.9%
659 of 1000 simulations did not exhaust the portfolio within 30 years.
Asset distribution after 30 years
Worst 10% (P10)NT$0
Median (P50)NT$6.7M
Best 10% (P90)NT$50.39M
How to read this
  • Success rate ≥ 90%: most scenarios sustainable.
  • Success rate 75–90%: elevated risk; consider lower withdrawals or delayed retirement.
  • Success rate < 75%: current plan may be hard to maintain long term.

Monte Carlo assumes normally distributed, independent yearly returns. Real markets have fat tails (black swans) and sequence-of-returns risk; treat results as a probability reference only.

Asset trajectory (median with 10th–90th percentile band)

The line is the median path across 1,000 simulations; the shaded band spans the 10th to 90th percentiles — 80% of simulated outcomes fall within it. A wider band means greater market uncertainty.

Success rate interpretation

  • ≥ 95%: robust plan, executable as-is
  • 85–95%: acceptable, small risk of adjustment
  • 75–85%: meaningful risk, consider backup plans
  • < 75%: unlikely to sustain, reduce withdrawals or delay retirement

The 4% rule aims for 95%+ success — see Trinity Study article.

Frequently asked

Why is Monte Carlo better than a fixed-return projection?
Fixed-return models assume markets deliver exactly 5% (or whatever) every year, which never happens. Monte Carlo models returns as a distribution (mean + std dev), draws a fresh number each year, and runs 1,000 independent paths. The output is a probability your plan survives — much closer to real-world risk than a single number.
What success rate counts as acceptable?
≥ 95%: robust, executable as-is. 85-95%: acceptable with minor adjustment risk. 75-85%: meaningful risk, build backup plans. < 75%: unlikely to sustain — cut withdrawals, delay retirement, or save more. Most retirement plans target 90-95%.
What return and standard deviation should I plug in?
Global equities historically: ~7-10% nominal return, 15-18% std dev. 60/40 stock/bond: ~6-7% / 10-12%. 100% US equities: ~9-10% / 18-20%. Subtract 2-3% for real (after-inflation) returns. The tool ships sensible defaults — adjust to your portfolio.
What is Sequence of Returns Risk?
The order of returns matters enormously in early retirement. Even with the same average, a retiree who hits −20% in year 1 has a much higher failure rate than one who hits −20% in year 25. Monte Carlo captures this naturally by running 1,000 independent paths.
Why 1,000 runs and not 1 million?
1,000 runs stabilize the 5th and 95th percentiles; more runs don't materially shift the conclusion. Browser-side simulation runs in 100-300ms — more iterations hurt UX without changing the answer. If you want 100K+ runs, run Python + numpy locally.
Does the tool store my data?
No. Simulation runs entirely in your browser. No inputs or outputs are sent to a server.

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