Run Your Retirement Stress Test
Unlike a simple average-return calculator, this Monte Carlo model runs thousands of market paths with good years and bad years mixed together. Enter your assumptions below.
What is a Monte Carlo retirement calculator?
A Monte Carlo retirement calculator is a probability-based planning tool. Instead of assuming your portfolio earns the same return every year, it models many possible market outcomes with randomness baked in. That better reflects real life, where returns are uneven and sequence risk matters.
In short: traditional calculators answer, “What happens if everything goes according to average assumptions?” Monte Carlo calculators answer, “How likely is my plan to survive real-world uncertainty?”
Why this approach is more realistic than a straight-line projection
Average returns can be misleading. Two portfolios might both average 6% over 30 years, but if one experiences major drawdowns right before or just after retirement, that portfolio can fail much sooner. This is known as sequence of returns risk.
- Accumulation phase: bad early returns are often recoverable if you keep contributing.
- Withdrawal phase: bad early returns are dangerous because withdrawals lock in losses.
- Inflation: spending usually rises over time, increasing pressure on the portfolio.
How this calculator works
This tool runs thousands of simulated lifetimes:
- Before retirement: savings grow (or shrink) based on random annual returns plus your annual contributions.
- After retirement: the model subtracts inflation-adjusted spending needs after accounting for guaranteed income.
- If the portfolio reaches zero before your life expectancy in a simulation, that run is counted as a failure.
At the end, you get a success probability and distribution metrics like median and percentile outcomes.
How to interpret your results
There is no universal “perfect” score, but here is a practical framework:
- 90%+ success: conservative plan with a strong safety margin.
- 75% to 90%: workable for many people, but you may want backup levers.
- Below 75%: typically a signal to revise savings, retirement age, spending, or asset mix.
Remember, this is a planning model, not a guarantee. The goal is to understand risk and make better decisions, not predict one exact future.
Levers you can pull to improve retirement odds
1) Save more while working
Even modest increases in annual contributions compound meaningfully over decades.
2) Delay retirement by 1 to 3 years
This can help in three ways at once: more contributions, fewer years of withdrawals, and potentially higher guaranteed income.
3) Reduce initial spending
Withdrawal rate is often the strongest predictor of success. Trimming spending by even 5% can materially improve outcomes.
4) Build flexible spending rules
Instead of fixed spending forever, plan to reduce discretionary expenses after poor market years. Flexibility improves survival rates.
5) Revisit asset allocation and risk
Higher expected return can help, but only if you can tolerate volatility without abandoning the plan during downturns.
Important assumptions and limitations
- The model uses a simplified normal return process and constant volatility.
- It does not include taxes, fees, required minimum distributions, or changing asset allocation over time.
- Life expectancy is a single input, not a mortality curve.
- Real-world spending may not track inflation evenly year to year.
Use this as a decision-support tool and pair it with a broader retirement plan.
Final takeaway
A good retirement strategy is not about guessing one return number correctly. It is about building a plan that can survive many possible markets. Monte Carlo analysis gives you that lens. Run scenarios, test assumptions, and focus on robust decisions you can stick with through uncertainty.