Most of us have spent our lives being evaluated on a scale of 0 to 100. From elementary school through college, we learned that higher scores meant better results. A 95 was an A, a 75 was a C, and anything below 60 usually meant we had some work to do.
That way of thinking tends to stay with us. When we see a number expressed as a percentage, our instinct is to interpret it as a grade. The closer we get to 100%, the better we assume we’re doing. So, when your financial advisor runs a retirement projection and tells you that your plan has a 95% probability of success, it is natural to feel pretty good about that result. You’ve earned an A in retirement planning.
But a Monte Carlo simulation isn’t a test, and while a higher probability of success is generally desirable, it doesn’t necessarily mean one retirement strategy is better than another. Understanding why requires looking at what Monte Carlo analysis actually measures and, just as importantly, what it doesn’t.
What Your Monte Carlo Score Actually Measures
Monte Carlo simulations are widely used in financial planning to evaluate whether a retirement portfolio can support your spending throughout retirement. Rather than assuming your investments earn the same return every year, the software models thousands of potential market scenarios with different sequences of investment returns. The analysis incorporates assumptions about your portfolio, spending needs, inflation, Social Security benefits, and other income sources. It then evaluates how many of the scenarios leave you with sufficient financial resources to cover your projected spending through the end of the planning period.
If your plan has a 95% probability of success, it generally means that the plan met the software’s definition of success in 95% of the simulated scenarios. In the remaining 5%, the plan experienced a shortfall.
While Monte Carlo analysis offers insight into the durability of your plan, there are two important limitations. First, the result depends on the assumptions used in the analysis. Expected investment returns, inflation, spending patterns, and the age through which you plan all influence the outcome. A 95% result does not guarantee a 95% chance of meeting your retirement goals in the real world.
Second, the probability of success measures how often a plan succeeds, not how well it performs when it succeeds or how badly it fails. This is particularly important when evaluating retirement income strategies.
A 95% Success Rate Doesn’t Tell You How Bad Failure Could Be
Take two retirees, each with $1.5 million invested and the same annual retirement spending goal, but with different investment strategies. Both run Monte Carlo simulations through age 95, and both receive a 95% probability of success. On the surface, their retirement plans appear equally strong. But when we look at what happens in the unsuccessful scenarios, the differences can be substantial.
For the first retiree, the portfolio generally lasts until age 93 or 94 in most of the unsuccessful scenarios. The shortfalls occur near the end of the planning period, and the amount of spending that cannot be supported is relatively modest. For the second retiree, the portfolio may become depleted at age 82 in the unsuccessful scenarios, leaving more than a decade of retirement spending unsupported. Both plans receive the same score, yet the consequences of failure are dramatically different.
This is why retirement planning needs to account for both the probability and the magnitude of a shortfall. The probability indicates how often the plan fails under the modeled conditions, while the magnitude tells us how much spending may go unfunded. The timing of those shortfalls matters, too, particularly when a retiree may need to support spending for many additional years.
A Monte Carlo success rate doesn’t capture those differences. When comparing plans using the same assumptions, a higher probability of success generally means fewer scenarios result in a shortfall. But that doesn’t tell us how severe those shortfalls might be or whether one retirement strategy is more appropriate for your circumstances.
The Assumptions Behind the Number Matter
The assumptions behind a Monte Carlo simulation can significantly impact the results. A plan that assumes strong future investment returns may have a higher probability of success than one that uses more conservative expectations. The retirement plan itself hasn’t changed, but assumptions about market performance can make it look considerably stronger or weaker.
The length of the planning period can have an equally significant impact. A projection that assumes you live to age 85 may look much more favorable than one that extends to age 95 or 100. Those additional years mean more spending to support, and the longer your retirement lasts, the greater the risk of unfavorable market conditions affecting your portfolio.
Even relatively small differences in success rates deserve some perspective. A plan that scores 95% in one analysis and 93% in another hasn’t necessarily become less secure. Depending on the software and methodology, the difference may reflect changes in the underlying assumptions or variations in the simulated market scenarios. A two-percentage-point difference may be meaningful, but the numbers alone don’t tell us whether it is.
This is why understanding the assumptions behind the analysis matters as much as understanding the result. The same retirement plan can look considerably stronger or weaker on paper without anything about your actual financial circumstances having changed.
Retirement Planning Isn’t About Getting the Highest Grade
Treating Monte Carlo results like grades can shift our attention toward improving the score rather than evaluating whether the plan supports the retirement we want. A higher probability of success is generally desirable, but the decisions required to achieve it may not always be worth the tradeoffs. Reducing projected retirement spending, for example, will generally increase the probability of success. But if that means giving up travel, hobbies, or experiences you’ve spent years looking forward to, the higher score comes at a cost. Working a few additional years may also improve the results, but that means sacrificing time you could have spent enjoying retirement. Neither decision is necessarily wrong, but a higher score alone doesn’t make it the right choice.
Your ability to adjust spending also matters. Essential expenses such as housing, groceries, and healthcare generally need to be covered regardless of market conditions, while discretionary expenses like travel and entertainment may be easier to adjust when investment returns disappoint. Someone with enough reliable income from Social Security, pensions, or annuities to cover essential expenses may be comfortable taking more investment risk with the remaining portfolio. Another retiree who relies heavily on portfolio withdrawals to cover basic living expenses may prefer greater protection against market downturns. Even with similar Monte Carlo results, these two retirees face different risks and may benefit from different retirement income strategies.
Other planning measures, such as the funded ratio, can provide additional perspective. A funded ratio compares the financial resources available for retirement with the estimated cost of future spending goals. Used alongside Monte Carlo analysis, it can help assess whether you have sufficient resources to support your retirement spending without relying exclusively on the probability of success. Our workshop on How Much Do I Need to Retire walks through how to evaluate whether your resources can support the retirement you want, rather than relying on a single score.
Looking Beyond the Score
Monte Carlo analysis is a valuable retirement planning tool, but its usefulness depends on understanding what the results actually tell you. A 95% probability of success shows how frequently your plan met its goals under a particular set of assumptions. It doesn’t tell you how severe a shortfall might be, how much spending is protected by reliable income, or how easily you could adjust if circumstances change.
After a lifetime of being taught that higher grades mean better results, it’s natural to want the highest possible Monte Carlo score. But improving that number may involve tradeoffs, from spending less to working longer, that don’t necessarily make your retirement better.
Retirement planning isn’t about earning an A. It’s about finding the right balance between enjoying the retirement you’ve worked toward and protecting yourself against the financial risks you may encounter along the way. A Monte Carlo score can help you understand those risks, but it can’t tell you what tradeoffs are worth making to live the retirement you want.
Want to learn more? Listen to Episode 250 of the Retire With Style Podcast.
