Monte Carlo Simulation: Smart Bet for Baby Boomers’ Retirement Plans

By Evan M. Levine

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SEPTEMBER 2005 - The baby boom generation, those 77 million Americans born during the postwar period of 1946 to 1964, has witnessed the Dow Jones Industrial Average increase by a factor of 40 and has also enjoyed approximately 500% growth in home equity during their adult lives. But somehow, 25 million of the 77 million boomers currently have a net worth of less than $1,000 (excluding the value of their home equity). Boomers’ financial situation, combined with rising life expectancies, means they will require high-quality retirement planning advice. Many of them will want that resource to be their tax advisor or a referral from their tax advisor.

A CPA firm that decides to provide this service can provide it in-house by hiring a financial planner or a registered investment advisor, or it can form a strategic alliance with another firm of professionals. In either case, what is needed is not just someone who executes transactions. The right person is someone capable of providing accurate and forward-thinking advice. Ideally, this will be an advisor who understands the application of Monte Carlo simulations in retirement planning.

The Monte Carlo Method

Various industries have used the Monte Carlo method for decades. Its principles were first used in the 1940s by scientists at Los Alamos, N.M., working on the atomic bomb. It has more recently been applied to help urban planners predict traffic patterns and to institutional investment portfolios to forecast probable outcomes. Today, retirement planners in retail financial services use it to adjust retirement income planning for annual variations in projected “average” returns. Consider the period 1968–1998, when the average return for the S&P 500 was 11.7%. If a planner was advising a client at the beginning of that period and actually had the foresight to use 11.7% as an assumed average return, it would appear that an individual could safely withdraw 8.5% of the portfolio’s initial value, then increase withdrawals by 3% annually for inflation (see Exhibit 1). In reality, however, it wouldn’t have worked out that way, because while the S&P 500 indeed averaged 11.7% per year, it did not deliver 11.7% each and every year. Some years were much worse than the overall average of 11.7% and other years were much better. Consequently, had the individual followed the advice in this example, she would have actually depleted all of her assets by 1981 because stocks performed very poorly in the first half of this period (see Exhibit 2).

A Monte Carlo simulation can help because, rather than rendering advice using a flawed assumed average return for each and every year of an analysis, an advisor can simulate the plan using randomly ordered returns based on a set of reasonable parameters. Computer software can simulate retirement cash flows 500 or 1,000 times, thus reflecting a range of possible outcomes (see Exhibit 3).

One can then observe the results of a financial plan during the peak of a bull market or during the trough of a bear market. From this, a planner can arrive at a probability of success: that is, how many times out of the 500 or 1,000 simulations the plan actually held up (i.e., the portfolio assets were not depleted before the end of the time period). Many planners look for somewhere between 75% to 90% success to have sufficient confidence in a plan.

Because this approach has its limitations, these models are only as good as their assumptions, and using one certainly doesn’t completely eliminate uncertainty. By recognizing uncertainty, however, it is an improved, more sophisticated form of advice as compared to traditional plans that use obviously erroneous averages.

Useful websites for learning more about Monte Carlo simulations include www.financeware.com and www.analycorp.com.


Evan M. Levine, ChFC, is a financial advisor based in Garden City, N.Y., specializing in retirement income planning for baby boomers. He can be reached at 516-240-6161 or elevine@finsvcs.com.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 



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