Monte Carlo Portfolio Simulator

By Jay Chang, VP, Wealth Advisor

Last updated July 6, 2026

What Is a Monte Carlo Simulation in Retirement Planning?

It is a way of testing a retirement plan against uncertainty. Instead of assuming one smooth average return, the simulator generates hundreds of randomized market sequences from your return and volatility assumptions, then counts how many of those futures your plan survives.

The output is a range: a median path, a great scenario, a tough scenario, and a success rate. That range is more useful than any single projection, because retirement fails at the edges, not at the average.

When Does Monte Carlo Analysis Matter Most?

Whenever the order of market returns could break a plan that looks fine on average. The situations where I lean on it hardest:

• The five years around retirement. This is peak sequence-of-returns risk. A bear market right after you stop earning does damage a later one never could.
• Retiring earlier than planned. A longer drawdown period gives bad sequences more chances to appear. After a layoff or package offer, this analysis belongs in the 60-day decision window.
• Retiring before benefits begin. Someone who stops working at 58 funds years of spending before Medicare and Social Security, exactly when the portfolio is most fragile. I wrote about retiring at 58 in a system built for 65.
• Setting a sustainable withdrawal. The simulator shows how the success rate moves as monthly spending changes, which is a more useful dial than any fixed rule.
• Choosing how much risk to keep. Raising the assumed return usually means raising volatility with it. The bands show what that trade actually buys and costs.

If you have not sized the goal yet, start with my retirement readiness assessment, then bring the numbers here to stress-test them. Turning a passing simulation into an actual withdrawal, tax, and allocation strategy is the heart of my retirement planning work.

How to Use This Simulator

1. Set your current age, planned retirement age, and the age you want the money to last to.
2. Enter your starting balance, monthly contributions while working, and planned monthly withdrawal in retirement.
3. Set an assumed pre-retirement return, a more conservative post-retirement return, and annual volatility. A diversified portfolio often sits in the 12 to 18 percent volatility range.
4. Read the plan success rate, the median path, and the 10th and 90th percentile outcomes. If the tough scenario breaks the plan, adjust spending, timing, or savings and rerun.

Monte Carlo Questions I Hear Most

What is a Monte Carlo simulation in retirement planning?

A stress test against uncertainty. The simulator generates hundreds of randomized market sequences from your return and volatility assumptions, then counts how many futures your plan survives. You get a range of outcomes and a success rate instead of a single projection.

What is a good Monte Carlo success rate?

There is no universal target, because the rate is a dial, not a grade. Near 100 percent often means you are spending less or working longer than you need to. A low rate means some mix of spending, timing, and savings needs to change. The rate also shifts with your assumptions, so test conservative inputs before trusting any number.

Why do two plans with the same average return get different results?

Sequence of returns risk. Once you are withdrawing, the order of returns matters as much as the average. A bear market in the first years of retirement forces you to sell more shares at low prices, and the portfolio may never recover even if later returns are strong. Monte Carlo analysis exists largely to expose this risk.

What assumptions does this simulator use?

Your inputs drive everything: an assumed average return before and after retirement, annual volatility, monthly contributions while working, and monthly withdrawals in retirement. Returns are randomized around your assumptions each year. It's an educational model, and it does not capture taxes, fees, or how real people cut spending in bad markets.

How many simulations do I need?

This tool runs 200, 500, or 1,000. More runs smooth the percentile bands and stabilize the success rate between reruns. The shape of the range matters more than any single line, so once the bands stop shifting between runs, you have enough.