How to Use Monte Carlo Simulations to Stress Test Your Retirement Plan

Monte Carlo simulations offer a way to assess risk

Man testing a fuel storage tank.
Stress test your portfolio to see how well it will hold up. Dave and Less Jacobs / Kolostock / Getty Images

A Monte Carlo simulation is a method of testing an outcome over a range of possible variables. It can be something like a stress test for your financial future. Monte Carlo simulations are used in retirement planning to predict the likelihood that you will have a particular level of retirement income through life expectancy.

The typical Monte Carlo simulation for retirement involves five variables:

  • Portfolio size
  • Portfolio allocation
  • Annual income to be withdrawn
  • Inflation increases to be applied to the income withdrawn
  • Time horizon

The simulation tests the outcome over possible combinations of portfolio returns considering these variables. You can change spending, inflation, your time horizon, and your annual withdrawals and see how that affects your likelihood of success.

Why Monte Carlo Simulations Are Important to Your Retirement Plan

These simulations are important because you can't know what your future portfolio returns will be. Looking at historical data, you can see that returns for stocks and bonds can vary widely over 20-year return time periods.

You can follow the same allocation model as another retiree who stopped working five or 10 years earlier or later than you did or you intend to. You'll experience a completely different outcome, even though you made identical choices. This is referred to as sequence risk.

Monte Carlo simulations test your outcomes over a wide combination of possible market returns, and they typically deliver an answer in terms of your probability of success. The goal in retirement is to have a high probability of success—which is a slightly different goal than that of a younger person who wants to accumulate wealth and assets.

Most financial planning software used by professionals incorporates some type of Monte Carlo simulation. You can also use an online tool such as the one offered by Flexible Retirement Planner for free.

Understanding the Results of a Monte Carlo Simulation

The financial planning software by Finance Logix offers a free trial so you can get your feet wet and learn what's involved. It incorporates an analysis that also includes other variables, such as Social Security or pension income.

This test case assumes the following:

  • $300,000 of investments in non-retirement accounts
  • $700,000 of investments in retirement accounts
  • $80,000 a year of living expenses (not including taxes) increasing by 3 percent a year to keep pace with inflation
  • Retirement time horizon 2015 through 2047
  • Investment allocation: 5 percent cash, 55 percent bonds, and 45 percent stock index funds
  • An expected pension of about $24,000 a year and Social Security benefits of about $10,000 a year.

The results indicate that this person has a 95-percent chance of success of having at least $80,000 a year of inflation-adjusted income through the year 2047.

What about the 5 percent of the time where the plan fails? In this case, it assumes this individual makes no changes to his lifestyle and keeps right on spending the same amount of money that he did pre-retirement.

It's important to continuously test your plan after you retire because you can identify in advance if your probability of failure is increasing. 

A Tax Warning

Keep in mind that if you're withdrawing $50,000 of the $80,000 from your retirement accounts and you're in the 25-percent tax bracket, your gross withdrawal would actually be about $66,667 with $16,667 being withheld for federal taxes. The net $50,000 would come to you.

A Final Word

If you encounter a very poor set of economic circumstances in your early retirement years, you may need to make some adjustments to your spending to ensure that the 5-percent failure scenario does not occur or increase. Much like with many health situations, you'll have time to remedy a small potential problem when you identify it early on. It's sometimes difficult to identify these potential problems without the proper testing that involves some form of Monte Carlo simulation.