Senior Vice President, Advisor, Stephanie Richman, CFP®, explains how Monte Carlo simulations can help you test your financial plan against a range of market conditions and make more informed decisions about retirement, gifting, and more.
As a financial advisor, I’ve had the pleasure of helping many clients comfortably reach and enjoy their retirement years. During my career, a few consistent themes have emerged. Clients generally want to know if their money will last their lifetime, or they have a good idea that it will but want to validate those assumptions. They may also be curious about how else the money could potentially be deployed, such as for traveling, buying a second home, or making monetary gifts during their retirement years.
Experienced financial advisors can help answer all of these questions. An important tool at our disposal is Monte Carlo analysis, which assesses the mathematical probability that individuals will meet their goals. In the process, the simulations can help reassure those planning for retirement to stay the course through market ups and downs so that they are more likely to achieve their goals.
Over the past two decades, a few stretches have certainly tested clients’ confidence — the dot-com bubble of 2000-2002, the financial crisis in 2008-09, 2020’s pandemic volatility, and the inflation-and-rate shock of 2022. Part of my job as an advisor is to help people maintain perspective, so they can persevere through difficult economic times by thinking long term. Monte Carlo simulations assist in this endeavor by testing variable outputs, i.e., testing your unique situation under varying market conditions.
Monte Carlo simulations utilize market indices to represent the components of a portfolio, while factoring in income, expenses, and life expectancy. Each simulation then picks a random starting point between 1926 and the end of the previous calendar year. The methodology runs a thousand simulations to determine if a person’s money would last through different market conditions, taking into account various asset class returns, inflation, standard deviation, market volatility, etc. In the process, very poor market conditions such as the Great Depression of the 1930s are accounted for, along with very strong conditions such as the prolonged bull market of the 2010s.
When running different scenarios, Monte Carlo simulations can incorporate adjustments to several important variables, including:
Adjusting even one of these inputs can meaningfully change the results, which is why scenario testing is often an ongoing process rather than a one-time exercise.
As a complement to the simulations, it’s important to note that during the period from 1945 to 2020, the average contraction was only about 10 months while the average expansion was about 64 months. In recent years, many clients have had questions about market performance. They ask me how markets can recover even during times when the economic news feels volatile or negative.
The answer is because markets tend to discount the future.
Through financial services websites, investors may find calculation tools that incorporate Monte Carlo simulations. But being able to see the numbers is only half the battle, and an important way that financial advisors can add value is by making sense of all that data. The resulting impact can be immense.
Another appealing aspect of Monte Carlo simulations is how they can offer insight into whether a certain action would be sensible or not. Here are a few examples of how I've used them with clients.
One example is gifting to children or grandchildren, perhaps for educational purposes. Here, an individual may like the idea of seeing the benefit of that gift during their lifetime versus only bequeathing money upon their passing.
In these cases, we can conduct scenario planning such as testing whether a client could afford to gift a certain amount to their grandchildren every year until each grandchild turns 18. Monte Carlo simulations show how a person’s long-term plan would fare with and without that gifting, helping them to make an informed decision.
In my experience with clients, simulations have also played an important role for those looking to move when they retire. For example, one couple was planning to move to Oregon from the San Francisco Bay Area. Having a specific town picked out, they told me the average cost of homes there and asked about affordability, as well as whether a mortgage or outright purchase might make more sense.
We were able to factor those variables into Monte Carlo simulations and determine that they’d be on target to meet their goals either way; however, choosing a mortgage would likely be a better option. By choosing to take out a mortgage, more of their liquid assets would remain invested to potentially generate more money via asset growth to enjoy in retirement or leave to heirs.*
*The example referenced above is for illustrative purposes only, and there is no guarantee that a Monte Carlo Simulation is applicable to each individual client. Scenario analysis and Monte Carlo simulations rely on assumptions and historical data and are not guarantees of future performance or outcomes. Individual circumstances will vary.
Another area where Monte Carlo simulations can be valuable is in evaluating when to begin taking Social Security benefits. Claiming at 62 could mean a smaller monthly benefit over a longer period, while waiting until 67 or 70 results in a larger monthly amount but fewer years of payments. The right choice depends on a number of personal factors, including your health, other sources of retirement income, and how your overall portfolio is positioned.
Monte Carlo simulations may also help shed light on sequence-of-returns risk, which is the danger that the timing of withdrawals from a retirement account can negatively impact the returns it will generate over time. This can potentially be more pronounced if you begin retirement in a bear market.
Using a simple illustration, picture taking monthly withdrawals from your portfolio. When the market is up, there is not too much concern, but when you take withdrawals when the market is down, you’re likely to sell more shares to take the same income. This leaves fewer shares to move up when the market recovers. In short, withdrawals hurt an account’s long-term ability to provide returns more if they are made during a down market versus an up market.
Monte Carlo simulations stress test various sets of returns so you can see how your asset allocation may impact how long your portfolio may last.
In the scenario of buying a new home, utilizing a mortgage can be a better option than paying outright because a large amount withdrawn early in retirement can negatively impact a portfolio’s longevity. In other words, with a mortgage, the purchaser wouldn’t be taking as much money out of liquid assets early in retirement. So even though interest must be paid on the mortgage, the market’s performance may outweigh those payments and generate greater assets.
Applying sequence-of-returns risk to this concept, if money is taken out of the market early in a person’s retirement years to spend on a house, it doesn’t have the opportunity to stay invested, compound and possibly provide greater flexibility and liquidity over time.
Sequence-of-returns risk can also inform decisions about which accounts to draw from and when. Retirees often have assets spread across different account types, such as taxable brokerage accounts, tax-deferred accounts like traditional IRAs and 401(k)s, and tax-free accounts like Roth IRAs. Monte Carlo simulations can help test different withdrawal sequences under varying market conditions.
For example, in a down market, drawing from a taxable account or Roth assets rather than selling investments in a tax-deferred account at a loss may potentially help preserve the growth potential of those assets. These are the kinds of nuanced decisions where running multiple scenarios can help inform a more thoughtful approach.
A Monte Carlo simulation doesn't predict what will happen. It tests your plan against many possible sequences of returns to estimate the likelihood of meeting your goals. The result is typically expressed as a probability, such as an 85% or 90% success rate.
It's important to understand that number in context. A high probability doesn't mean your plan is set in stone, and a lower probability doesn't necessarily mean something is wrong. These scores are a planning tool, not a promise. They give us a starting point for conversation, helping to identify where a plan may have room for flexibility and where adjustments might be worth considering. In my experience, clients find it helpful to see how even small changes to spending, timing, or asset allocation can shift those probabilities in meaningful ways.
In many cases, a Monte Carlo simulation is most useful when it reflects your current financial picture. Life doesn't stand still, and the assumptions that went into last year's simulation may not hold today.
I generally recommend revisiting your simulation when something meaningful has changed, such as:
Even without a specific trigger, reviewing your simulation annually as part of your broader retirement planning or financial planning check-in can help keep your plan aligned with where things actually stand.
In conclusion, a picture is worth a thousand words. As an advisor, I could talk all day about whether a client is likely to meet their retirement goals but being able to show them through the use of Monte Carlo analysis has an almost magical effect.
It’s so powerful when people can see all of their relevant financial information on one page, including how their cash flow may look from year to year. Contact an advisor today to see how Monte Carlo analysis can help bring clarity to your financial plan.
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Scenario analysis and Monte Carlo simulations rely on assumptions and historical data and are not guarantees of future performance or outcomes.
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