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Understanding the Evolution of Monte Carlo Analysis in Financial Planning
• Matt Rogers • August 18, 2021
Monte Carlo analysis is a powerful tool for financial planners working to secure a client’s trust in their financial plan. When clients know their plan is on track to succeed, especially in times of market turmoil, financial professionals can keep them focused on long-term goals instead of short-term volatility.
But the way Monte Carlo is deployed in financial planning is changing. New methods reveal far more than a single probability of success at an estimated end date. With the evolution of Monte Carlo analysis comes a number of new considerations for financial planners.
How to Manage Longevity Risk with Monte Carlo Analysis
One way to understand longevity risk with Monte Carlo is to calculate a plan’s probability of success at every age, instead of just once at a singular end date. Assessing probabilities of success over time reveals important trends in a plan’s resiliency, as well as the client’s sensitivity to increased longevity.
With traditional Monte Carlo scores, where there is one probability of success at a set end date, there is no actionable information into success rates before or after the set end date, nor is there any insight into how quickly the plan could weaken as the client ages. This information could make a world of difference in the advice given to the client.
New approaches into reporting and visualizing Monte Carlo results reduce some of the traditional shortcomings of Monte Carlo and arm the advisor with much more holistic and actionable information.
Monte Carlo Simulation FAQs
My colleagues, John Costello, Financial Planning Development Consultant at eMoney, and Brett Tharp CFP®, Financial Planning Education Consultant at eMoney, recently hosted a webinar on the topic of Monte Carlo shortcomings and solutions. If you haven’t seen it, I’d highly recommend taking a look as they go deep into how Monte Carlo has changed over the years and how you can use the latest iterations.
Following the webinar, they received a ton of questions about Monte Carlo, specifically the latest Monte Carlo-enabled planning methods and how they can be deployed today, some of which I will address below.
What’s a Good Target Percentage Score in Monte Carlo?
An acceptable Monte Carlo score will vary from planner to planner and client to client. There is no single universal score that would be considered acceptable. Financial professionals may consider segmenting Monte Carlo scores into low, medium, and high thresholds to help interpret the results of a Monte Carlo simulation for all financial plans.
Financial planners shouldn’t be striving to achieve a 100 percent probability of success, however. In this case, the planner would likely be solving for problems that are very unlikely to ever occur, needlessly impacting the client’s lifestyle enjoyment by telling them to save too much and spend too little.
The key to finding an acceptable Monte Carlo score is to strike a balance between what’s a comfortable risk at a high probability of success. The right score is one that offers a high enough confidence level for the planner while also allowing the client to enjoy the fruits of their labor.
What’s a Good Life Expectancy Estimate for Monte Carlo?
Again, this will vary for every financial professional and client, based on the client’s personal health circumstances. Using a longer life expectancy is a conservative approach and one that many planners employ. One of the biggest fears among clients is outliving their money so using a longer life expectancy when planning reduces that risk. However, it will result in a lower Monte Carlo score. In turn, that lower score could drive the planner to make recommendations that maybe would not apply if a shorter, but equally justifiable life expectancy were used. So, financial professionals must once again strike a balance between using a sufficiently conservative life expectancy and avoiding advice based on artificially low Monte Carlo scores.
The new Monte Carlo methods described above, where success rates are generated at every age and not just the life expectancy age, help planners with this balancing act. This kind of Monte Carlo visualization provides the advisor with a much more holistic view of a client’s situation. Trends in Monte Carlo scores over time offer information that a single score never could.
Does Monte Carlo’s Accuracy Change Over Time?
Monte Carlo, like any projection, will be less reliable the longer the timeframe. All plans are based on assumptions that need to be updated over time. This is not unique to Monte Carlo—because Monte Carlo is so investment-oriented, it is important to make sure investment assumptions are realistic to have the most accurate Monte Carlo score over an extended time period. For example, over a long time horizon, make sure your projection incorporates changes to the asset allocation as the projected age increases just as the client will do in real life.
Do Monte Carlo Simulations Incorporate Events Like the Great Recession?
Every vendor is different in how they randomize rates. At eMoney, we do not recreate past periods like the 2008-2009 crisis. We use randomized rates where the rates for each asset class are randomized based on the assumed return and standard deviation the user entered. So, with a larger standard deviation for asset classes like equities, the Monte Carlo process will create years with assumed investment losses. Sometimes those losses are significant—such as 30 percent or more. Users of Monte Carlo get those bad years and really bad years automatically baked into their scores at appropriate frequencies.
What is the Difference Between Binary and Nuanced Monte Carlo Interpretations?
Monte Carlo analyses, in today’s form, are inherently binary. Every trial within a Monte Carlo analysis is either a pass or a fail. A trial that falls $1,000 short is labeled a failure just the same as the trial that falls $2,000,000 short, even though a financial professional’s advice would be very different in those two scenarios. A human understands the nuance behind why the two outcomes are vastly different, even if a traditional Monte Carlo analysis would not report on them differently. A smarter visualization would recognize that not all failures or successes are equal and provide insight into the likelihood of a “close call.” After all, in the real world, it is these close-call situations where planners show their worth and provide the most value.
Creating reports or visualizations that move away from the traditional binary pass/fail approach and instead provide better insight into the nuanced range of outcomes from a Monte Carlo analysis would create a serious leap in the evolution of Monte Carlo and financial planning.
What is Borderline Failure and Borderline Success in Monte Carlo?
There is no formal definition of borderline failure or borderline success for Monte Carlo simulations and yes, it is the same as a “close-call”. These are merely concepts used to demonstrate the idea of binary versus nuanced Monte Carlo interpretations. Every Monte Carlo score is based on a vast number of simulations. The end result for each simulation is either a pass or a fail. In reality, as I explained in the previous question, two failed simulations could be very different in their degree of failure and thus require a very different planning recommendation. Financial professionals would find value in defining what would constitute a borderline failure or success so they can adjust plans accordingly to maintain high levels of confidence and personalization in the plan.
How Do You Explain Monte Carlo Results to Clients?
One issue financial professionals often find with Monte Carlo scores is that they are not “humanized.” A percentile score, for many clients, is not the most intuitive way to present findings and one that often creates questions and confusion. For example, when a client sees their Monte Carlo score at a pre-determined life expectancy age is 75 percent, their first question is likely, “75 percent of what?” Then the planner explains the idea of thousands of plan trials and randomized rates. Even if the client readily understands this concept, they’re likely to then ask, “Is 75 percent a good score?” A 75 percent on a math test would not be good, but a 75 percent score for a financial plan, depending on the client, could potentially be a strong foundation from which to work. What a client thinks is a bad score may be actually be fairly strong and what a client thinks is a great score such as 95 percent may be a sign of over-planning and solving problems unlikely to ever occur.
Presenting Monte Carlo scores as an age instead of a percentile score can be more easily consumed by clients. At eMoney, we call this Confidence Age. Confidence Age is determined by the age at which the Monte Carlo score drops below a pre-determined level chosen by the financial professional. When a client sees their Confidence Age and asks what it means, the explanation is simple: “I am confident you can meet your financial goals until about this age, after which my confidence wanes and you might need to adjust your goals.”
If a client is given a Confidence Age of 81, for example, it means the Monte Carlo score is at or above a score that makes the financial professional confident in the plan until that age. By using age as the client-facing metric, all the background math becomes less relevant and the client will more naturally grasp what a good or bad score is. The client will likely easily know that they don’t want to start losing confidence in their goal funding at age 81—they can intuitively understand that that is too soon, and also understand the importance of making some sacrifices to bump up their Confidence Age.
The Evolution of Monte Carlo
Monte Carlo analyses are a powerful tool in financial planning. The way they’re used, and the way they’re explained to clients, are undergoing change. If you haven’t watched the Monte Carlo webinar embedded above, I’d recommend watching it to take a deeper dive into these topics and how Monte Carlo will continue to evolve in the industry.
You can also continue learning by checking out a recent post on how to make plans more personal with Monte Carlo-enabled longevity risk analysis.
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