When it comes to retirement planning, most tools give you a single line going up and to the right. Contribute X, earn Y%, and in 30 years you'll have Z. It's clean, optimistic, and almost certainly wrong.
The real world doesn't move in straight lines. Markets crash, recoveries vary, inflation spikes, and sequence-of-returns risk can devastate a portfolio at exactly the wrong time. Monte Carlo simulations address this by modeling uncertainty directly.
What is a Monte Carlo simulation?
A Monte Carlo simulation runs your financial plan through hundreds or thousands of randomly generated market scenarios. Instead of assuming a fixed 8% annual return, it might model one scenario where you get 15% in year 1 and -20% in year 2, another where you get 3% for five years straight, and so on.
Each scenario uses statistically realistic return distributions based on historical market data, accounting for:
- Return volatility — Markets don't return a steady 8%. They swing wildly.
- Correlation between assets — Stocks and bonds don't always move independently.
- Sequence risk — A crash early in retirement is far more damaging than one late.
- Inflation variation — Purchasing power erosion isn't constant.
Why a single projection fails
Consider two investors who both average 8% returns over 20 years:
Investor A: Gets steady 8% every year. Portfolio grows predictably.
Investor B: Gets -30% in year 1, then recovers with higher returns later. Same average, but the early loss means their portfolio is permanently smaller because all future gains compound on a reduced base.
Both "averaged 8%." But Investor B ends up with significantly less money. A linear projection would have shown them as identical. A Monte Carlo simulation would have flagged the risk.
Reading Monte Carlo results
Monte Carlo output is typically expressed as probability bands or confidence intervals:
- Median (50th percentile): Half the scenarios ended above this, half below. Your "most likely" outcome.
- 25th percentile: In 75% of scenarios, you did at least this well. A moderately pessimistic case.
- 10th percentile: Only 10% of scenarios were this bad or worse. Your stress-test number.
- 75th percentile: You'd need favorable markets to do this well.
- 90th percentile: The optimistic scenario — don't plan on this.
The spread between these bands tells you how much uncertainty your plan carries. A narrow band means your outcome is relatively predictable. A wide band means there's significant risk of both upside and downside surprises.
What Monte Carlo is good (and bad) for
Good for:
- Stress-testing whether your plan survives bad markets
- Comparing the risk profiles of different asset allocations
- Understanding the probability of reaching a specific goal (e.g., 90% chance of having $1M by age 60)
- Evaluating whether a higher withdrawal rate is sustainable
Not good for:
- Predicting exact future returns (it can't)
- Accounting for behavioral risk (panic selling, timing changes)
- Modeling one-time life events (inheritance, health costs, job loss)
Practical application: contribution decisions
Monte Carlo is especially powerful for answering "what if" questions about your own behavior:
- What happens if I increase my monthly contribution by $200?
- How much does shifting 10% from bonds to equities change my 30-year outcome?
- If I retire 2 years earlier, what's the probability I run out of money?
These aren't hypothetical exercises. They're the decisions that compound into massive differences over decades.
How Infnits runs Monte Carlo
Infnits runs Monte Carlo simulations directly on your connected portfolio. You choose a time horizon (1 to 30 years), set optional monthly contributions, and the simulation generates probability bands based on your actual holdings — not generic assumptions. Because it uses your real positions and their historical volatility profiles, the results reflect your portfolio's risk characteristics, not a model portfolio.
The simulation view shows percentile bands you can interact with, letting you explore what different market conditions would mean for your specific plan. It's designed to turn abstract probability into something you can see and act on.