Monte Carlo projections Featured

7:34am EDT September 29, 2005
In 1999, George and Judy Brown had accumulated a respectable nest egg after working hard for 35 years. At the relatively young age of 57, they both were ready to retire so they could do the things they had always dreamed of: traveling across the country in a motor home, visiting their children and grandchildren and playing golf. They were confident that their assets — Individual Retirement Accounts, 401(k)s, joint savings and investments — would sustain their less-than-lavish lifestyle for the next 30 to 40 years. Just to affirm their beliefs, they employed a reputable financial planner to perform cash-flow analysis on their nest egg.

The financial planner was comprehensive, assessing all the Browns’ assets and current and future income sources, determining reasonable annual living expenses, and planning for expected and unexpected expenses. He was even conservative in assigning investment assumptions, plugging in a long-term rate of return of 8.5 percent on the Browns’ investments. “In my opinion,” stated the planner, “your nest egg will comfortably support your lifestyle, even if you live in to your nineties.”

As a result, the Browns retired that year, purchased a motor home, traveled around the country, visiting their children’s families and enjoying golf three days a week. However, in just two short years, the Browns awoke to the horrible realization that their nest egg was decreasing rapidly, to the point where they had to sell their motor home and start working again.

Although this scenario is fictitious, thousands of couples actually were confronted with similar nightmares following the horrendous bear market of the early 2000’s. So where did the Browns and their planner go wrong? Why were the cash flow projections so far off? The problem was with relying on average returns.

Traditional financial planning models, as used in this example, use rate of return assumptions that do not vary with time. These assumptions often reflect average historical rates of return earned by particular asset classes or combinations thereof over an extended period. Returns have historically fluctuated from year to year in an unpredictable fashion, and future rates of return cannot be guaranteed or even predicted with certainty. In essence, traditional financial planning models failed to account for one of the most important elements — uncertainty or risk.

Now a more sophisticated alternative is working its way into financial planning. Using computer software or a Web-based program, you can calculate the probability of achieving your goals through a Monte Carlo simulation. Monte Carlo is a mathematical model for computing the odds or probability of an outcome, such as the value of your nest egg at retirement, by testing thousands upon thousands of possible results or trials.

Monte Carlo simulations have been around for more than 50 years, but only with advances in low-cost computing power have they expanded beyond the scientific community. Of course, probability is a centuries-old computational technique. The mathematics behind Monte Carlo came out of the Manhattan Project to build the atomic bomb during World War II. The work is largely credited to Stanislaw Ulam, an Austrian-born mathematician, along with computer pioneer John von Neumann. Ulam named the method Monte Carlo after a relative fond of sneaking off to Monaco’s casinos.

Monte Carlo analysis projects cash flow and net asset values multiple times — each under a different set of conditions — to yield a range of possible outcomes or trials. Monte Carlo analysis is, therefore, able to incorporate uncertainty or risk into the planning process by demonstrating how different assumptions about the future can impact the likelihood of your meeting or exceeding clearly defined financial planning goals.

Monte Carlo represents an improvement over traditional methods of financial planning. Nevertheless, as with any financial model, the results are sensitive to underlying assumptions. In George and Judy Brown’s case, Monte Carlo analysis could have very well produced results that would have motivated them to work a few more years, which would have made all the difference in the world.

Gary Storie is a wealth adviser with NexTier Wealth Management. Reach him at (724) 935-3461 or gstorie@thebank.com