Close Close
ThinkAdvisor
headshot of David Blanchett of PGIM

Retirement Planning > Retirement Investing > Income Investing

Monte Carlo Failures Aren’t Plane Crashes

By David Blanchett

X
Your article was successfully shared with the contacts you provided.

What You Need to Know

  • Unlike a plane trip, retirement is not a binary outcome of complete success or failure.
  • Advisors sometimes use the plane-crash analogy to sell products like annuities, but guaranteed income products create uncertainties of their own.
  • Advisors who use Monte Carlo projections should give their clients broader context instead of focusing on the success rate.

Retirement, like life, is fundamentally uncertain. Not only do we not know how long we’re going to live, we also don’t know things like future market returns, future inflation, even when we are going to retire.

These uncertainties make retirement planning difficult; however, one way to provide context regarding these uncertainties is to run what is commonly known as a Monte Carlo projection.

The uncertainty associated with Monte Carlo is often used by financial advisors (and educators) to sell strategies that have less uncertainty (e.g., some type of insurance product with a guarantee).

For example, I recently came across someone suggesting withdrawal strategies were analogous to a plane crashing, which is an incredibly misleading analogy. In reality, people will adjust their spending in retirement as situations warrant. Additionally, no financial plan, strategy or product can guarantee a successful outcome for an investor, given the myriad of risks and uncertainties in real life.

While there are notable imperfections with outcome metrics commonly employed in Monte Carlo simulations (the probability of success) and some of the underlying assumptions (e.g., static withdrawal rates), I still believe Monte Carlo models can be useful for financial advisors and investors, but context is important, especially when some folks in the industry attempt to sell against the approach using misleading analogies.

Decision-Making Under Uncertainty

Monte Carlo modeling was developed by John von Neumann and Stanislaw Ulam during World War II to improve decision-making under uncertain conditions (related to the development of nuclear weapons) and named after the Monte Carlo Casino in Monaco. Over the last few decades, Monte Carlo projections have become an incredibly common way for financial advisors to demonstrate the uncertainty associated with financial planning projections and accomplishing various financial goals (e.g., retirement).

The key differentiator with a Monte Carlo projection versus other projection methodologies, like time value of money calculations, is the element of chance (i.e., randomness). Typically, forecasted returns are the only random (or stochastic) variable in financial planning programs that employ Monte Carlo; however other variables, like age of death or age of retirement, could be randomized as well.

The return assumptions in Monte Carlo projection can be historical, forward looking, or some combination of the two. There is no requirement that returns be normally distributed in a Monte Carlo projection; however, there may be limitations given the specific program used by an advisor (i.e., constraints are program-specific as there really aren’t any limitations with Monte Carlo).

The probability of success is the most common outcome metric for a Monte Carlo projection. The success rate is simply the number of trials (or runs) the respective goal is accomplished (e.g., retirement income) divided by the total number of trials.

Some financial advisors, most commonly those who are selling a product with some type of explicit guarantee (e.g., an annuity), may use an analogy to imply that any chance of failure is simply unacceptable. Some examples include:

  • Would you board an airplane that has a 10% chance of failure?
  • A brain surgeon with a 95% success rate means 5% of his patients die.
  • Closing windows on a house to ensure no birds or unwanted intruders can enter.

The analogies all imply that an unsuccessful trial is somehow a cataclysmic failure. In real life, the impact of “failure” is likely to be significantly less severe. For example, a significant flaw in success rate metrics is that they ignore the magnitude of failure. For example, falling $1 short in the 35th year of a projection with a $100,000 would be treated as failure, despite the fact the person would have accomplished 99.999%+ of their goal.

In reality, people are unlikely to “fail” (as conveyed by a success rate). They will likely have to make some kind of adjustment to their plan during retirement instead. This adjustment, often in the form of a cutback in spending in later years, can be relatively minor or potentially more significant, but what’s important is that it is effectively impossible to somehow remove all the uncertainties with a single product or solution.

For example, even products that provide guaranteed (or protected) lifetime income (e.g., an annuity, of which I am typically a fan) do not ensure a successful outcome across all future possible states. Annuities can introduce inflation risk to a strategy (since they are not typically explicitly linked to inflation) as well as liquidity risk (e.g., those that require an irrevocable election). So while some products have the potential to reduce or mitigate certain risks, it’s not possible to eliminate them all.

While beyond the scope of this piece, I do think our industry should move away from success rates as an outcome metric. I recently published some research walking through a more realistic, implementable retirement income planning model.

For those financial advisors who are stuck using existing tools, focusing on the income generated in retirement for certain percentiles (e.g., the worst 1 in 5 trials) at various ages (e.g., age 95) is a way to provide more useful context than simply suggesting an individual has a 75.234% success rate.

Conclusions

Unlike a plane trip, retirement is not a binary outcome, where there is complete failure or success. In reality, retirees have the ability to adjust over time and are going to do so as situations warrant. Understanding this nuance is important when conveying the pros and cons of Monte Carlo projections and ensuring investors make the best planning decisions possible!

David Blanchett

NOT FOR REPRINT

© 2024 ALM Global, LLC, All Rights Reserved. Request academic re-use from www.copyright.com. All other uses, submit a request to [email protected]. For more information visit Asset & Logo Licensing.