The next evolution of Monte Carlo analysis

Financial advisers frequently use Monte Carlo analys`is to demonstrate the resiliency of a financial plan. These simulations produce a score that is useful in securing a client’s confidence in their plan. Such analyses do, however, have some inherent limitations.

There are potential solutions to these limitations with the next generation of Monte Carlo analyses. With advances in technology and the deployment of Monte Carlo in financial planning, these solutions can support advisers creating stronger, more personalized financial plans.


Traditional Monte Carlo scores are narrowly focused on a single year, which puts advisers in a challenging position. Typically, an adviser chooses a conservative life expectancy to mitigate longevity risk. But this life expectancy estimate can lead to artificially low Monte Carlo scores, in turn leading to overly conservative financial advice based on that score.

Next-generation Monte Carlo analyses should be able to show a Monte Carlo score at every age across a client’s lifetime. This helps incorporate all other equally justifiable life expectancy assumptions from which to judge the strength of a financial plan, effectively managing longevity risk with a more holistic Monte Carlo analysis.

While the score for any single year is important, it’s far more valuable to see the long-term trend in scores, and understand when probabilities of success start dropping, when they reach a critical threshold, and how quickly they drop, among other trends.


Monte Carlo is binary in nature — each trial within a Monte Carlo analysis is labeled either a pass or a fail. If 600 trials out of 1,000 pass in a simulation, you have a 60% probability of success. There is no nuance associated with this insight. Regardless of whether a trial falls short of funding its goals by $1,000 or $1,000,000, it’s counted equally as a failure. To a financial planner, of course, the difference between these two scenarios is obvious. This is a human understanding of context and nuance, which traditional Monte Carlo lacks.

New methods of Monte Carlo analyses should give advisers more transparency into the range and grouping of trials, especially trials that could be categorized as borderline successes or borderline failures. This gives an adviser more insight into the strength of a plan, allowing them to better tailor a plan to the client’s needs. It’s those instances where a plan is on the cusp of succeeding or failing where an adviser can have the most impact and value for a client.

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Not only should next-generation Monte Carlo simulations match a human understanding of nuance, they should humanize the results as well.

Monte Carlo results can be misleading, and they’re often confusing for clients. Even most advisers struggle to understand what a good Monte Carlo score is and frequently disagree about an acceptable confidence threshold.

Traditional Monte Carlo typically outputs a single percentile score. Most people’s experience with such a score is from school, where 100% is the goal and anything in the 90s is strong. In financial planning, a Monte Carlo score of 100% should not be the goal, and even a score in the 90s could be the result of overly conservative planning, in which a client is needlessly making lifestyle sacrifices to solve for potential problems that are unlikely to ever happen.

A Monte Carlo score in the 70s could be a rather strong score from which to build a plan, but many people perceive 70s as a terrible score based on their memory of school grades. This also conveys the message that the plan has a 30% chance of failing, which can be a shock to a client, when in reality it’s more accurate to say there’s a 30% chance they may have to make some lifestyle adjustments.

The next generation of Monte Carlo analyses could have different scoring methods, for example, displaying the age at which a Monte Carlo score first dips below a threshold of plan confidence.

Clients may more easily understand the value of watching an age-based Monte Carlo score increase from age 80 to 90 over time and appreciate what that means for their lifestyle and legacy more so than trying to interpret how a higher percentile relates to all their financial goals throughout their lifetime.

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While a singular probability of success is a useful metric, advisers can be looking to do more for their clients.

The next evolution of Monte Carlo analyses should allow advisers a more nuanced insight into the performance of a plan and offer results in a more consumable format. This way, less time is spent on explaining how the results are achieved, and more time is spent on personalized, impactful plans that help clients live their preferred lifestyle while achieving their most important financial goals.

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Matt Rogers is director of financial planning at eMoney Advisor.

The post The next evolution of Monte Carlo analysis appeared first on InvestmentNews.

Andrew is half-human, half-gamer. He’s also a science fiction author writing for BleeBot.

Andrew Vincent
Andrew is half-human, half-gamer. He's also a science fiction author writing for BleeBot.
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