We all know that life is uncertain, and that uncertainty especially applies to personal finances. That’s why the financial planning community is increasingly employing a technique that illustrates to clients the “odds” that a given financial strategy will prove successful.
Take investing for retirement. When calculating how much you might need to save monthly in order to achieve a certain lifestyle upon retirement, you might assume a certain dollar size for your nest egg, an average annual return on your investments, and the number of years you have to save. For example, you might assume a $500,000 nest egg and an eight percent return for 20 years. From that, it’s simple to calculate how much you should save each month to build your nest egg.
The problem is, the odds are it won’t happen. It’s an illusion of stability. That’s because nothing happens every year exactly as you calculate. Investment returns, for example, might average 8 percent over those 20 years. However, during that time returns will vary one year to the next: perhaps 20 percent one year, 6 percent another, a 7 percent loss another. Similarly, the temperature for a given day probably won’t be exactly the average temperature for that day. Unlike the daily weather, however, the sequence in which investment returns occur can make a huge difference in how large your nest egg really turns out to be. If returns are higher than average early on in the accumulation phase, the nest egg will probably be larger than you projected; lower-than-average returns early on means it will likely be smaller than you planned.
The same approach applies to withdrawal rates from your nest egg. How much you can safely withdraw each year and not run out of money before your death will depend on, among other factors, investment returns, the inflation rate, and whether you live longer or shorter than your life expectancy.
To get a better feel of what your chances really are for achieving a particular financial goal, planners are turning to a computer modeling technique called Monte Carlo simulation. Say you want to know how much you can realistically withdraw from your retirement portfolio each year and not run out of money (three percent? Five? Eight?). The planner plugs in the desired withdrawal rate, the “expected” return of the investments, the portfolio’s standard deviation (how much the returns might vary each year), an expected inflation rate, life expectancy and so on. The program then generates hundreds, even thousands of variations of these numbers, each generation slightly altering a particular variable such as the sequence of investment returns, the average rate of return or a different life expectancy, while keeping the target withdrawal rate the same.
The result shows you the probability, given the assumptions used, that a particular withdrawal rate will achieve the results you want. For example, you might find that you have a 90 percent chance of not running out of money by withdrawing no more than 4 percent from your portfolio each year in retirement—but only a 75 percent chance if you consistently withdraw 5 percent. This would be similar to the weather reporter saying the likelihood of rain today is 75 percent, which might encourage you to take an umbrella. The decision whether to take a particular level of financial risk, of course, is ultimately yours. Keep in mind that Monte Carlo calculations are very useful in assessing risk, the calculations are based upon historical relationships which may or may not hold true in the future.
While Monte Carlo simulation is being applied most prominently to retirement planning, it can be used for any type of financial decision that involves uncertainty. For example, you might use it to determine the odds that a particular funding rate for a vanishing premium life insurance policy is sufficient or how much you should invest each year to build a college fund. Monte Carlo is not foolproof. The assumptions one plugs into the program need to be realistic, and there are inevitably variables left out of the calculations. Variables are usually generated from long-run historical norms. But what if the future is vastly different? What happens if you experience a major financial crisis, such as an illness or loss of a job? This could change your financial picture dramatically.
Ultimately, Monte Carlo is a useful tool that provides a more accurate picture of a financial strategy. But it doesn’t guarantee results. Nothing substitutes for common sense, years of experience in working with retirees, and a realistic overall financial plan that prepares for the uncertainties along the way.
What You Should Know About Retirement
- Retirement - Planning for an Unplanned Retirement
- Investment Risk: A Short Course
- Tax Tips for Early Retirement
- Long Term Care: A discrepancy between fact and fiction
- The Changing Face of Retirement
- Insurance Information for Retirees
- Comfortable Retirement
- Long Term Care Insurance- Smart Ways to Save
- Questions for Your Retirement
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