The future of financial planning is in Monte Carlo. No, not the city. The technique. Already, many leading advisory firms have integrated Monte Carlo into their planning process. The vast majority of advisors, however, are just beginning to explore probabilistic forecasting, and there are some serious misconceptions about how it works.
I’ve spent four months running Monte Carlo simulations, speaking with vendors who offer tools for making Monte Carlo calculations, and chatting with planners who are using it. I’m not professing to be an expert in Monte Carlo simulation. But I have asked planners and vendors to run simulations to test different scenarios that I came up with, and I have spent hours on conference calls with advisors taking tours of leading Monte Carlo applications. Here’s what I’ve learned.
Discussions at conferences and media coverage of Monte Carlo simulation often focus on minutiae about statistical techniques. But two rocket scientists who develop these applications tell me that when advisors get into heavy discussions about these statistical issues they sound a bit foolish, or, as one put it, like Professor Irwin Corey, the 1970s comic famed for his gibberish.
Most advisors know that Monte Carlo is a way to model the randomness of investment returns. What many don’t understand is that even though Monte Carlo simulation lets you model the randomness of the stock market and other assets, you still have to make assumptions about the future of the investment markets that you want to model.
Not long ago, an advisor grilled me about an article I had written explaining the logic of Monte Carlo simulation. The article explained the case of a 65-year-old couple in excellent health and with a $1 million nest egg from which they wanted to draw $60,000 a year.
I explained why the couple stood a very likely chance of eating Friskies Buffet in their old age, assuming they would live as long as the actuarial tables used by life insurance companies suggest. The simulation was based on an assumption that equities would return 8% annually, intermediate term bonds would stay at their current yield, and inflation would remain at 3%. Based on these assumptions, if the couple invested 60% in stocks, 30% in bonds, and 10% in 30-day Treasuries, the forecast showed they stood a 68% chance of running out of money in 26 years–and there’s a 50-50 chance one of them will live that long.
The financial advisor questioning me about the simulation told me that something must be wrong with my story. It should not be assuming an 8% return on stocks and making these other assumptions about expected returns, he told me. The whole purpose of Monte Carlo, he said, is to simulate the randomness in returns the couple would likely face. This is a widely held misconception about Monte Carlo. “Your expected return assumptions in Monte Carlo simulations matter as much as they do if you were using a deterministic model,” says Christopher Jones, executive vice president at Financial Engines, a Palo Alto, California-based advice company founded by Nobel laureate William Sharpe.
When you use Monte Carlo analysis, you still have to make assumptions about the future. Most of the professional Monte Carlo software tools come with assumptions built in, and they simulate the randomness of an asset class based on history, a view of the future, and risk.
Assumptions Will Differ
For example, Financial Engines Professional Advisor Service, one of the leading firms selling a Monte Carlo advice platform to advisors, assumes a 7.5% real return on large-cap stocks and an unconditional 3.5% inflation rate. But Morningstar Advisor Workstation assumes a 7% real return on large caps. The scientists who create these programs examine the historic risk–variance and standard deviation–for each asset class and make an assumption about the future based on their world view. When you run a simulation, the randomness it simulates is around its forecasted return for an asset class, and its risk and its other assumptions about the capital markets.
“What’s important about Monte Carlo simulation is that it focuses attention on scenarios that are something other than the expected return,” explains Jones. “The point that Monte Carlo makes, as opposed to other techniques, is that the probability of achieving the expected return can be in some cases a small number. Monte Carlo shows the range of possibilities and that, in fact, you are unlikely to achieve the exactly expected return.”
Based on your starting assumptions, the simulation randomly spits out returns that a client will get over his or her lifetime. In the case of my 65-year-old couple with $1 million, for instance, in year one of their retirement portfolio, the simulation may assume that they will get a 12% return, but the following year could result in an 8% loss. Based on capital markets assumptions programmed by the makers of the Monte Carlo software tool you’re using, a simulator randomly assumes returns for every year the couple is expected to live, thus creating a simulation for their lifetime with randomly generated inputs. But it doesn’t stop there.
A Monte Carlo simulator will make a client live more than one lifetime. The 65-year-old couple’s lifetimes could be simulated thousands of times. Based on capital market assumptions about the assets in the portfolio, the couple’s portfolio could thus be tested to see how the randomness of the markets would impact them if they lived 500, 2,000 or even, 10,000 times. What you and your client come away with is a chance of achieving a given financial goal. You can tell clients that, say, in 30% of the simulations, they have enough to retire, pay for college, or achieve other goals.
This kind of statistical analysis is better than traditional planning forecasts. It more realistically reflects the way the markets behave, illustrating the probability and magnitude of possible outcomes. The deterministic models still used by planners and investment managers do not reflect the probability of achieving a goal.
Until Monte Carlo came along with the advent of faster computers and better software tools, many advisors relied upon history as a guide to assemble a projection of returns and behavior of the markets and create a forecast. In its simplest form, the equation is easy to understand: Large-cap stocks since 1926 have averaged about a 10.5% return annually. So our 65-year-old couple can plan to get that kind of return over their lifetime. But returns don’t come in 10.5% increments every year. Just ask a 65-year-old who rolled over her nest egg from a qualified plan into a self-directed IRA in the stock market in 2000. Because she may have lost 45% or 50% of her capital in the early years of her retirement, she may never recover, of course, and her spending plans may no longer make sense. My point is to explain the pitfalls of Monte Carlo to advisors before adoption of the technology becomes widespread.
I first reviewed a Monte Carlo planning tool three years ago, when Financeware (FW) became the first Web-based application targeting independent advisors. (Financeware products are offered through Investment Advisor’s Web site). FW’s introduction of a Monte Carlo simulator was followed by other professional forecasting tools from startups, while other Monte Carlo simulators, such as Financial Engines (FE) and m-Power, targeted retail consumers directly and through 401(k) plans. In recent months, a spate of new professional products for advisors has been launched, and I’ve spent the last few months trying to understand how these tools would work for advisors.
With the help of a panel of six advisors, some of whom were already using Monte Carlo analysis to serve their clients, I reviewed Financial Engines Professional Advisor Service in the December 2001 issue of Investment Advisor. I followed up with a review by many of the same advisors of Morningstar Advisor Workstation in February 2002. In March, I spent time testing the Monte Carlo simulator created by NETirement.com. I’m about to tell you about two more Monte Carlo simulators that have emerged in recent months, Efficient Portfolios and Ibbotson Portfolio Strategist software.
Adoption of these tools by independent advisors has been slow, although the granddaddy of them all, Financeware, does claim a good bit of success. In addition to sales directly to institutions, nearly 3,000 advisors have independently purchased FW from wirehouses, regional B/Ds, and independents, according to David Loeper, FW’s chairman and CEO. Last June, Thomson Financial invested in Financeware and licensed parts of the program in an advisor technology platform being offered to brokerages and independent B/Ds. But Financeware, which has received $16 million of venture capital in two rounds of financing, has not lived up to my initial enthusiasm. Despite all the creativity and buzz and money at the company, Financeware is not widely used by independent advisors I know. This is not to slam FW; it’s more about advisor resistance to new technology.
An ASP With No Sting
I believe that the main reason Financeware has not had greater penetration is that it is a Web-based application service provider. With an ASP, you run your application over the Internet. The application itself is stored on a Web server. Sometimes client data is, too. That’s different from installing an application locally on your computer. ASPs have a very important benefit. When a new version of the software comes out, you don’t have to install it on all the machines in your office. The ASP installs the upgrade once on its server and you get the benefits immediately without the hassles of installing updates on your PCs’ hard drives. Despite this benefit, most advisors remain uncomfortable with ASP technology for advice tools. RIAs seem to have the greatest aversion.
Most entrepreneurs running their own RIA are control freaks. That’s why they chose to start advisory firms. They don’t like the idea of an application running remotely and being dependent on their Internet connection to conduct business. They are squeamish about giving data to third-party software providers. They are concerned about privacy issues for their clients. So the ASP model is likely to gain greater acceptance with registered reps. If you are a registered rep affiliated with a B/D, compliance is a hassle and the ASP can simplify the supervision tasks.
While Financeware became the best-known Monte Carlo application sold to RIAs and reps that was delivered over the Web, others have followed suit. Two of the big players in Monte Carlo–Morningstar and Financial Engines–are in fact actively trying to sell their applications to B/Ds. Each has seen some success. Components of Morningstar’s Advisor Workstation are being deployed by Merrill Lynch and Nationwide, although no brokerages or custodians have yet signed on for its Monte Carlo tool. Thomson, which uses Financeware’s Monte Carlo technology, recently signed a deal with Janney Montgomery Scott. Financial Engines has inked an agreement with TD Waterhouse. Despite these successes, it’s not clear that the ASP model will win the battle for independent advisor desktops.
The reason why this architecture is not used by the big firms making Monte Carlo tools is that it is not as scalable as an ASP. Most of the MC tool developers have long-term strategic business plans that project adoption by tens of thousands of advisors and hundreds of thousands or millions of clients. NETirement’s current architecture would not support that scale of numbers but can work just fine for an advisory firm with hundreds of clients.