Why Some Advisors
"Just Say No"
to Monte Carlo Simulations

by Julie Crawshaw

Monte Carlo simulation — the method of statistical analysis that determines the probability of certain events using a roulette-wheel like generation of random numbers — has become so popular that everyone except registered reps seems to use them to calculate probable investment outcomes. But if the SEC approves a NASD-proposed amendment, the prohibition against reps projecting investment results would be lifted, paving the way for reps to offer customers direct access to Monte Carlo tools.

On the surface, this is a good thing. Monte Carlo simulations are great teaching tools. A simulation, for example can show clients how particular spending patterns are likely to deplete their retirement nest egg.

However, this technique has some unfortunate failings as a financial planning tool. For starters, it doesn't recognize that portfolio performance depends at least as much on the sequence of future investment returns as it does on the average of those returns. Moreover, the thousands of iterations Monte Carlo simulators produce can lull clients into believing they've considered all the possible financial outcomes they could experience, when in fact the numbers generated may have little relevance to their particular financial situation. Further, Monte Carlo doesn't measure bear markets well. Finally, this kind of simulation is not capable of connecting projected investment returns with realistic cash flows.

No wonder some financial advisors are not thrilled about the prospect of customers running in to see their portfolio simulation. Broker/dealer Peter Bauer, president of Oakbrook Financial Group in Oak Brook, Ill., hired a firm to perform a Monte Carlo analysis at a client's request. “When I got it back, I thought it was junk,” Bauer says. “We did it to document the file, and the client was happy, but we'll never do it again unless a client insists.”

Bauer's not alone. According to James A. Shambo of Lifetime Planning Concepts in Colorado Springs, relying on Monte Carlo is dangerous because the system treats the current market not as a starting point but as merely one of all possible environments. “Monte Carlo can predict a 20 percent return because the simulation started at a 7 P/E and then doubled,” Shambo notes. “But how relevant is that if your real world starting point is a 30 P/E and your real client is a 70-year-old widow worried about how her portfolio will be affected over the next 10-to-20 years?”

A Monte Carlo simulation, Shambo notes, might predict 16 loss years out of 76 but is unlikely to put even two loss years in a row, let alone three or four, thus missing the present real world pattern. Nor do random distributions take into account clients' reactions to short-term volatilities. Yet investors “are most affected by the one-to-five year periods because that's where they live,” he says.

So what's the solution? Use the 76 years of real market data that's available, Shambo advises, and show clients what would have happened had they retired with their current income and projected draw-downs in a previous environment. “It's infinitely more useful for clients to see what would have happened in real economic environments using actual asset class cross-correlations, standard deviations, returns and starting P/E ratios instead of random ones,” he says.

To do just that, Shambo built 100 different portfolios from 1926 forward, prepared a one-page report for each and imported the data to his software. The resulting simulations left him stunned by how much more failure arose than was showed by the Monte Carlo distributions he'd been doing.

Another major problem Shambo sees is that Monte Carlo software makes it easy to raise the odds of success by increasing the common stock portion in your portfolio — a bad move to make when what the client needs is to save more, spend less or avoid withdrawing portfolio funds in down markets. “I don't want to increase my liability because I made a mathematical model that failed most of the tests of human nature,” Shambo says. “Monte Carlo doesn't take human emotions into account. It assumes investors will ride out tough times without backing away from poorly performing investments, something few clients do.”

In fact, Monte Carlo makes it impossible to analyze proposed financial strategies accurately, says Larry Fowler, co-founder of the financial planning software company financeXpert.com, because it treats clients' financial resources as separate from their financial obligations. Fowler notes that Monte Carlo unrealistically assumes investors will never unexpectedly withdraw funds from their portfolios.

Fowler adds that Monte Carlo oversimplifies complex financial issues by not tracking income tax bases in portfolio rebalancing and by treating cash flow as a constant value, which disregards the devastating effects of large variable expenditures when investment returns are negative. Frustration with Monte Carlo's failure to connect volatility in investment returns and variable cash flows led Fowler to create financeXpert.com, which is now bringing out transaction-based financial planning software that Fowler says addresses the financial planning problems Monte Carlo doesn't see.

Another critic of Monte Carlo analysis is Harold Evensky, chairman of Evensky, Brown & Katz Wealth Management in Coral Gables, Fla. Evensky uses the Moneyguidepro.com Web site (which he helped design) to educate clients about the huge range of possible investment results. But he stresses that the real threat to clients' well being is not point-estimation, but the assumptions used to plan investments. “We need to recognize that in planning our clients' future, we're dealing with ambiguity, not risk,” Evensky says.

The bottom line for investors today, Evensky concludes, is being less concerned with the probability of success and more concerned with the consequences of failure. And the bottom line for brokers, reps and other financial advisors is finding ways to model portfolios realistically.