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In times of financial uncertainty, Monte Carlo simulations provide insight into portfolio performance. Whether it’s high inflation or greater market volatility, Monte Carlo analysis will reveal how those uncertainties impact a client and provide peace of mind about their plans.
Understand capital market assumptions in Monte Carlo simulations
Calibrating your capital market assumptions (CMAs) is the first step to understanding how your Monte Carlo results compare to the real-world behavior of a financial plan during a period of uncertainty. Most Monte Carlo analyses use CMA data based on either the past performance of asset classes or via a projected performance based on analysis from an investment committee. Both approaches are valid, with each one offering the advisor something to consider when using Monte Carlo analysis for retirement planning.
For example, CMAs based on projected performance often use 10-year market outlooks, even though financial plans often span 25-40-year horizons. Is the advisor comfortable using market assumptions deemed reasonable for the next 10-15 years for a client with a 40-year life expectancy? Many investment committees are conservatively expecting 10-year returns to be lower than historical averages. Are these lower return assumptions still useful for 40-year projections where economic cycles should even out, resulting in market returns closer to historical norms?
On the other hand, a system using historical performance assumes asset classes have a higher return than an advisor is comfortable with. For example, the near-30-year bull market in bonds starting in the 1980s can make historical fixed income returns more optimistic than many advisors care to assume.
In either case, a system where the advisor can edit their CMAs is helpful to run Monte Carlo analysis with the assumptions they feel most comfortable with. Understanding how CMA data is created is an important step in interpreting and trusting your Monte Carlo results.
Adjust CMAs and planning scenarios to see the impact of uncertainties
Getting a Monte Carlo result using your CMA data is an appropriate starting point to understanding uncertainties. You can then change your CMAs to better represent the market environment and get an alternate Monte Carlo score.
You could increase the assumed inflation rate in your CMAs or decrease stock returns and increase volatility. Tweak your CMAs, and compare the adjusted results to the first set of results using standard CMA data to see how sensitive your client is to such market uncertainty. If the two results are significantly different, you can proactively take the appropriate action to protect a client’s plan. Or, if the two results aren’t that different, you can help clients feel confident that their plan is still on track.
Compare Monte Carlo scores and the range of wealth presented in the results. This will give you further insight into the impact of economic events. For example, you could simulate incorporating an annuity to protect against poor market performance in your analysis. You may find that this doesn’t improve Monte Carlo scores, but it results in a more stable range of wealth, which could be an encouraging step to take during times of great uncertainty.
Monte Carlo scores based on revised CMAs are an effective way to test your client’s financial resiliency if the macroeconomic environment changes. You can see the impact of changes like higher inflation or increased volatility while testing different recommendations. When Monte Carlo scores don’t differ significantly, you can assure clients that their financial plans are resilient, and that market uncertainty doesn’t translate to drastic changes in their plan.
Use new Monte Carlo metrics
Most Monte Carlo analyses rely on a common metric, the probability of success, to portray the strength of a client’s plan, with a 100% score being the highest. The issue here is that many advisors and clients do not know what a “strong” score is. Is 80% strong enough? What about 85% or 90%?
The probability of success does not answer fundamental questions. Does a 75% score mean there is a 25% chance they run out of money or a 25% chance they need to make lifestyle adjustments? And when would that happen?
Some new, alternative metrics put a Monte Carlo analysis and score into more human terms. Confidence age, for example, shows a client when their Monte Carlo is likely to drop below a pre-determined threshold.
Being able to tell a client their plan is strong until age 88, for example, gives a human perspective to their plan that a 75% Monte Carlo score fails to offer. Clients understand an age metric more easily than a percentage metric, which facilitates easier conversations about their plan and the ways in which uncertainty may impact it.
Monte Carlo analyses for retirement planning offers advisors important insights during times of financial uncertainty, helping their clients gain confidence in their plan. Newer metrics help clients understand the value and resiliency of their plan, giving them peace of mind at times when financial anxiety may be high.
Matt Rogers, CFP® is director of financial planning at Fidelity’s eMoney Advisor.
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Advisor Perspectives welcomes guest contributions. The views presented here do not necessarily represent those of Advisor Perspectives.
