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Retirement Planning > Spending in Retirement > Income Planning

Income Planners Wager That Monte Carlo Testing Is A Safer Bet

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Are income planners who do not use Monte Carlo testing taking a gamble that their clients will not have enough money in retirement?

While a gambling person might put their chips on “no,” a growing number of planners are saying “yes” to the use of stochastic modeling, according to those interviewed.

Their reason: Monte Carlo testing increases the likelihood that their clients will realize their goals, and it provides a way to check to see if their income plan is working.

With Monte Carlo simulations, also known as stochastic modeling, scenarios or iterations are generated using computer software. Financial advisors say the number of iterations they use range from 500 to 10,000. The approach differs from static testing where assumptions are established and do not change. The range of likelihood (in outcome) that the planners view as acceptable spans from 80% to 95%.

“How sure do you want to be?” asks accountant and planner Bernard Kiely, of Kiely Capital Management Inc., Morristown, N.J. Kiely says he does not like using averages that he says are found in more traditional modeling because they can be misleading. “By using averages, Bill Gates and I would be the wealthiest person in the world.”

Among factors Kiely does include in simulations are inflation and a portfolio’s rate of return. When he tries to estimate a return for a portfolio, he says, he starts by using an asset allocation program; then he uses a stochastic modeling system to determine the likelihood that the portfolio will achieve the returns predicted.

For example, if a modeling test shows the likelihood is 67% that a client’s goal will be reached, the test is helping the planner to realize that more work needs to be done in order to increase the likelihood of attaining the desired goal, Kiely says.

He cites an instance in which clients, a retired teacher and his wife, were trying to decide whether the wife should work 4 more years or should retire. By her working the additional 4 years, according to the Monte Carlo simulation, the chance of there being enough money in retirement increased to 87% from 83%, providing more clarity for that couple, he says.

That clarity can include understanding the changes that working part time in retirement, selling a house and downsizing, or moving out of an expensive part of the country such as New Jersey to a less expensive part of the country can make, Kiely adds. So, the benefit is not that it gives a planner absolute certainty, he says, but rather that it allows planners to take proactive measures before a plan fails to perform as expected.

“The fundamental question is ‘Are we on track?’” says Tom Davison, a certified financial planner and partner in Summit Financial Strategies, Columbus, Ohio. Stochastic modeling helps answer that question, he says. It also allows for flexibility and enables an income planner to avoid “rules of thumb,” which he describes as “dangerous.”

That flexibility, Davison says, is demonstrated in how stochastic modeling better reflects the sequence of returns rather than assuming a steady rate of return each year over a period of years. Sequence of returns can make a “huge difference,” he says.

That flexibility also can be used to include just about any input a client needs, including the cost of long term care and how it impacts other expenses, the cost of taking care of an adult dependent child, and the cost of health issues that may arise for clients, Davison says. “You can build in just about anything that you can think of.” (See chart.)

Planners are currently not really trained about how to perform a stochastic analysis and how to use math modeling to create a long-term plan, he says. But they should be, Davison says, noting that a sizeable number of planners now are gravitating to this approach.

Scott Farber, a planner, accountant and attorney who is vice president-wealth management with Woodstock Corp., Boston, says he is a “huge fan” of stochastic modeling. It is “much more accurate” than traditional models, he says, even if “it is a little tougher to explain.”

It is more accurate, he says, because it takes into account variations in stock market performance. So, for instance, rather than assuming 8% growth each year, the model might assume returns of 8%, 12% and 20% over a three-year period, he says. The difference can be “unbelievably dramatic.” It reflects life, according to Farber. “You could win the lottery, lose a job or have a kid.”

When clients see how likely it is that retirement income will be reached, it can move them to take action, Farber continues.

While stochastic modeling cannot completely guarantee an outcome, it does offer a comfort level, he says.

When a Monte Carlo simulation is being done, he says, each variable important to an income plan has to be examined independently so that there is no doubt that the outcome is a result of that variable. Using multiple variables at one time makes it difficult to tell whether or not the outcome was the result of one or several variables, he points out.

By using Monte Carlo simulations, it is possible to show a client the relationship between risk and return, says John D. Smith, a certified financial planner in wealth management with the firm of Balasa, Dinverno & Foltz, LLC, Itasca, Ill.

For instance, if a portfolio is 50/50 stocks and bonds and produces a 90% chance that a client’s income goal will be reached, Smith says, then is it worth creating a portfolio that is 80/20 stocks and bonds that produces a 95% likelihood that a client’s income planning goal will be reached? If the client does want to take on that additional risk, when “it is probably a lock,” then that “roots out more issues,” he adds.

If the likelihood of success turns out to be a figure such as 67%, then there needs to be a discussion of steps that can be taken to increase that likelihood, he adds. “Monte Carlo is not the focal point. It is a confirmation that static projections are accurate.”

And, Smith continues, it works best with retirees or those within 5 years of retirement. The reason, he explains, is that Monte Carlo analysis is sensitive to changes and longer projects are less certain.

Smith says that while there is talk of a lot of planners using Monte Carlo analysis, he would estimate that 25%-33% of planners are using it. But, he notes, at least planners are starting to think about it.

Monte Carlo analysis has proven helpful in getting clients to understand that they cannot take 10%-12% distributions annually and still hope to meet their income planning objectives, says Steven Markel, a senior financial advisor with Investor Solutions, Coconut Grove, Fla. Particularly before the tech bubble bust, Markel says that roughly one out of three clients was taking a distribution that would cast doubt on future income planning distributions being realized. A 6% distribution is more in line with appropriate distributions, but many clients have looked at a linear return of 11% on the S&P 500′s historic returns since 1926, so they believe that they can take a 10% distribution and still have enough to keep a long-term income plan intact, he says.

What a stochastic model will illustrate, Markel says, is that the double-digit returns they have seen in recent years are not something they may continue to see.

It also has the flexibility to incorporate a cost-of-living adjustment in calculating projections for an income plan, he says. That flexibility could allow an early retiree to lower the amount needed to reflect integration of Social Security when the client reaches age 65, Markel says.

When a client starts taking income, Merkel says that he performs a Monte Carlo analysis to assess how much income can be distributed. On average, he says, he performs 3 to 5 Monte Carlo analyses a month to prepare clients for the income distribution phase of their lives. Once an analysis is done, he says it is a good idea to check it annually as well as when there is a life changing event.

This article originally appeared in the May 2005 issue of Income Planning, an online publication of National Underwriter Life & Health. You can subscribe to this e-newsletter for free by going to

The approach differs from static testing where assumptions are established and do not change

Monte Carlo analysis has proven helpful in getting clients to understand that they cannot take 10%-12% distributions annually and still hope to meet their income planning objectives

Nine Years of Retirement
(actual, and reverse)
Order Of Return Matters

Source: Tom Davison, Summit Financial Strategies, Inc. Columbus, Ohio



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