The financial crisis has laid bare some of the folly of long-term financial planning. The notion that you can use past data on assets to create forward-looking plans that stretch out ten, 20 or 30 years is naïve.

When advisors make predictions about the returns on stocks, for instance, they are often looking back at data on returns, standard deviations and correlation coefficients going back to 1926. The conventional wisdom holds that the farther back in years the data goes, the better. But what the 2008 crisis taught us is that this method of considering possible bad outcomes is deficient.

Monte Carlo simulation in financial planning programs, for instance, uses historical returns of asset classes and their standard deviations to project possible future outcomes. In most planning apps, a lognormal or fat-tail distribution is used to better reflect the unlikely chance of a really big gain or loss in any given year. But it turns out that Monte Carlo simulations randomize returns and risk data too much to model the real world accurately. The math doesn't work. It fails to see that some economic conditions linger-that portfolios can perform very poorly or very well several years in a row.

Actual economic scenarios, in other words, instead of randomness, should thus become the basis of investment projections. Real-world historical data should form the building blocks of simulations.

This is not a new concept. And yet scenario planning has not caught on among advisors. The financial crisis, though, may have shown we must have it in our financial planning applications.

To make the case, I enlisted the help of two smart advisors, Tom Connelly and Kristen Jankowski, who helped me create an illustration that shows why planners need software simulating different economic scenarios.

Connelly is the founder and CEO of Versant Capital Management in Phoenix and chairman of the investment committee of the Arizona State Retirement System. Jankowski owns Financial Planning Outsource Services in Florham Park, N.J., and writes about 100 financial plans a year for advisors.

The three of us designed a crude, economically sensitive financial plan for a fictional couple. Connelly provided assumptions about the most-likely-to-prevail economic scenarios over the next five years: a Goldilocks economy (disinflation with moderate growth), deflation (negative growth), stagflation (low growth with high inflation), and a "Go-Go" situation (high inflation with high growth). To make five-year forecasts about returns, standard deviations, correlation coefficients and inflation, Connelly drew from past data.

For the Goldilocks scenario, Connelly used data from January 1981 through December 31, 2000. The high-inflation/low-growth scenario used data from the start of 1968 through the end of 1981. We used the Great Depression to derive inputs about a deflationary environment, using return, risk, and inflation data from 1929 to 1940. Finally, the high-growth/high inflation scenario was modeled on recent financial and economic statistics generated by China and Brazil.

To simulate the impact each scenario might have on a financial plan, Jankowski, a CFP and MBA, keyed the return, risk and inflation data into MoneyGuide Pro's Monte Carlo simulation engine. To keep the model simple, we used the application's default correlation coefficients for asset classes. To its credit, MGP allows an advisor to input his own assumptions when making a Monte Carlo simulation (not all planning programs give you the freedom). Most planners use the default Monte Carlo setting in planning apps. But these assumptions are not foolproof, as you are about to see, especially when you are trying to anticipate something like the 100-year flood the economy has just been through.

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