Financial-Planning Tool Fails to Gauge Extreme Events
If one had asked a financial adviser 18 months ago for retirement-planning guidance, there is a good chance he would have run a "Monte Carlo" simulation. This calculation method, as it is commonly used in financial planning, estimates the odds of reaching retirement financial goals.
But there is little chance your Monte Carlo simulation, named for the gambling mecca, would have highlighted a scenario like the market slide just seen. Though these tools typically run a portfolio through hundreds or thousands of potential market scenarios, they often assign minuscule odds to extreme market events. Yet these extreme events seem to be happening more often.
Some industry participants and academics are pushing to improve the Monte Carlo tools' ability to highlight the risk of major market slides.
There is no standard Monte Carlo approach, but the method is nothing new. It was used during World War II to help develop the atomic bomb. By the late 1990s some financial-services firms, like T. Rowe Price Group Inc., had introduced Monte Carlo tools aimed at individuals.
Monte Carlo simulation has wide appeal, and is used in online tools offered by firms like Fidelity Investments and by independent retirement planners. The financial-services industry provides retirement planning, in part, because it attracts clients and boosts fee income.
Here is how a typical Monte Carlo retirement-planning tool might work: The user enters information about his age, earnings, assets, retirement-plan contributions, investment mix and other details. The calculator crunches the numbers on hundreds or thousands of potential market scenarios, guided by assumptions about inflation, volatility and other parameters.
It then spits out a "success rate," which shows the percentage of market scenarios in which the investor had money remaining at the end of his estimated life span. In many cases, the consequences of failure -- say, running out of money at age 80 -- aren't laid out.
Many providers of the tools argue that it is a significant improvement over the traditional retirement-planning approach, which typically involves assuming some set market return, say 8% for U.S. stocks, year after year, an assumption considered unrealistic by academics and financial pros.
The questions about Monte Carlo tools reflect broader concerns about mathematical models for gauging portfolio risks.
These models were supposed to help quantify and manage the risks of mortgage-backed securities, credit-default swaps and other complex instruments. But given the events of the past couple of years, it appears that the models often gave big institutions, as well as small investors, a false sense of security.
Now, some investors have decided that if risk can't be accurately measured, they will just have to play it safe. Jeff McComas, a chemical engineer in Woodbury, Minn., has used six or seven Monte Carlo calculators and found that none highlighted the possibility of a scenario like the recent market downturn. The lesson: "The future is so unknown that your prudent choice is to save as much as you can now and live below your means," said Mr. McComas, 39 years old.
Some financial advisers are equally skeptical. "I take whatever probability of failure that comes out of your Monte Carlo simulation and add 20 percentage points," said William J. Bernstein, author of "The Four Pillars of Investing."
Critics emphasize that the problem isn't Monte Carlo itself, but the assumptions that go into it. Since no standard approach exists, one user might plug in a range of assumptions on interest rates, inflation or volatility that is different from another user.
Also controversial is that many Monte Carlo simulations assume that market returns fall along a bell-curve-shaped distribution. That means a high probability may be assigned to, say, a stock-market return of 5%, which would fall toward the middle of the bell, and negligible odds assigned to a 54% decline, which would fall near the extreme edge, or "tail."
"In a bell-shaped curve the probability of getting one of these extreme outcomes we're seeing is basically zero," said Paul Kaplan, vice president of quantitative research at Morningstar Inc.
While a bell-curve model indicates there is almost no chance of a greater than 13% monthly decline in the Standard & Poor's 500-stock index, such declines have happened at least 10 times since 1926, according to a report by Mr. Kaplan.
Some Monte Carlo models, like the one used by Financial Engines, assign higher odds to extreme market events than the bell-curve distributions. Even so, "I would not claim we have the magical ability to accurately predict very infrequent events," said Christopher Jones, the firm's chief investment officer.
Some firms are considering revising Monte Carlo models to reflect a world where big market swings happen more often. Morningstar last year tweaked its asset-allocation software offered to institutional investors, allowing users to choose a bell-curve-shaped distribution or a "fat-tailed" distribution, which assigns higher probabilities to extreme market events. The company is exploring using this model in more products, Mr. Kaplan said.
Laurence Kotlikoff, a Boston University economics professor who developed the ESPlanner financial-planning software, and Richard Fullmer, senior portfolio strategist at Russell Investments, said they also are considering offering clients Monte Carlo scenarios that incorporate fatter-tailed distributions.
The choice could make a difference in an investor's retirement plans. While a bell-curve model shows a negligible risk of a greater than 50% decline in the S&P 500 over extended time periods, a fatter-tailed model assigns it a probability of 4% or 5%, odds high enough to grab the attention of risk-adverse investors, according to Mr. Kaplan's report.
Some industry participants and academics are pushing for Monte Carlo tools to more clearly illustrate the scarier scenarios. In a recent paper, Moshe Milevsky, associate finance professor at York University's Schulich School of Business in Toronto, proposed a calculation that Monte Carlo tools could use to show a retirement plan's vulnerability to extreme market events.
Some industry participants also are trying to set standards that could help Monte Carlo tools more accurately capture extreme market events. The Retirement Income Industry Association in 2007 issued a set of principles noting that the calculators should run a large number of scenarios.
The ideal models run tens of thousands or hundreds of thousands of scenarios, which help gauge extreme events at the tail end of the distribution, observers said. Yet some tools run only 1,000 scenarios or just several hundred.—Neal Templin contributed to this article.
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