I RECENTLY SPOKE with a colleague. I’d expected him to be retired by now. He told me that he’d planned to retire last spring, but his employer offered him a three-day-a-week part-time schedule with full benefits. He discussed it with his financial planner.
The planner told him that, if he retired, he had an 85% chance of meeting his retirement goals. By working part-time for two more years, his chances of meeting his goals went up to 95%. My colleague enjoyed his work and figured he’d still be able to schedule four-day weekends. He decided to continue working part-time.
If you work with a financial planner, you may have heard assessments similar to my friend’s. The higher the percent chance of success, the better you’re supposed to feel. But what do these numbers really mean?
There are two common methods that planners use to assess your portfolio’s probability of success—historical averages and Monte Carlo simulations. Using historical averages is more straightforward. You take historical market returns for each asset class and compute a weighted average annual return based on your portfolio’s asset allocation.
Let’s say you have a classic 60% stock-40% bond mix. The stock portion is in an S&P 500 index fund, which has an historical annual return of 9.6%. The bond return is calculated using a long-term U.S. bond fund, which has an historical annual return of 5.6%. Their weighted average is (0.6 x 9.6%) + (0.4 x 5.6%), which comes to 8%.
Starting with this assumption, a retirement income projection is built for each year of retirement. It compares all sources of income—pension, Social Security, portfolio growth, withdrawals—against all anticipated expenses. The analysis can incorporate other variables, such as inflation, required minimum distributions and taxes.
If your total spending is less than total income, your portfolio continues to grow. If you have money left over at the end of your whole retirement period, your retirement plan is considered a success.
What if your spending is greater than your income? The analysis assumes you make up the difference by taking larger withdrawals from your portfolio. Your portfolio will shrink each year and may eventually be depleted. If your savings are projected to run dry before the assumed end of your retirement, your retirement plan is—needless to say—considered a failure.
The weakness of this analysis: The model uses the same investment return for each year of the analysis. Your retirement success will hinge on how much your actual investment results differ from the assumed average return.
A Monte Carlo simulation attempts to avoid the shortcomings of the “historical averages” method by varying the returns of each asset class in each time period, often focusing on annual results. Once again, you start with the historical averages for each asset class. But a Monte Carlo simulation throws in historical volatility and a random number generator, thereby calculating a distinct asset return for each time period in the retirement projection.
The Monte Carlo method repeats the whole process hundreds or even thousands of times, each with a different set of returns for each asset class and each time period. The total number of iterations that produce a positive portfolio value at death is computed. This number is then compared to the total number of iterations attempted.
Say the Monte Carlo software ran 1,000 iterations of a retirement projection. If 950 of those cases showed a positive portfolio value at the end of the projection, the probability of success for that retirement plan would be 95%. In other words, each of the 1,000 scenarios considered a different stock and bond market performance and, in 95% of those random cases, your portfolio carried you all the way through your retirement.
The math is more complex than my explanation here. You can also use the statistical methods to randomize more than asset returns. For example, you could vary inflation randomly as well. But varying asset returns suffices for many of my friends and colleagues who aren’t math aficionados.
What if this analysis shows a lower probability of success than you’re comfortable with? You can often adjust your plan to reach an acceptable level. For example, you could reduce retirement spending. Alternatively, you might adjust your asset allocation, increasing risk until you meet your success objective. Just don’t overdo that extra risk.
Michael Kitces has written a paper that challenges the need for a 95% probability of success. The paper brings up several good points that can help us better understand the results of these experiments.
Let’s say you only achieve a 50% probability of success. Small adjustments may translate into a dramatic improvement. For instance, if you have ongoing sources of income, like pension and Social Security, that cover the great majority of your spending, cutting your spending just a bit could significantly improve the odds of success.
The bottom line: If you’re working with a financial planner and she says your probability of success is 50%, don’t panic. It’s likely not a bad starting point as you approach retirement. The key is to understand the source and magnitude of the failure, and what can be done to overcome it. Consider my friend at the start of this article: A few years of part-time work put him squarely in the 95% success range.
Richard Connor is a semi-retired aerospace engineer with a keen interest in finance. He enjoys a wide variety of other interests, including chasing grandkids, space, sports, travel, winemaking and reading. Follow Rick on Twitter @RConnor609 and check out his earlier articles.