Financial planners and investment advisors these days usually pooh-pooh the idea of using an optimizer to construct a portfolio or even to see how a portfolio might behave. But many smart institutional money managers still rely heavily on this sophisticated tool. I’m not smart enough to tell you whether you should be using an optimizer in your practice. People with CFA, Ph.D., and other letters after their names are arguing about it. But I can report to you about the debate over optimizers, and then you’ll judge whether a new optimization and portfolio analysis tool from AdvisoryWorld is right for you.
Portfolio optimization technology was born from the groundbreaking Modern Portfolio Theory developed in the early 1950s by Nobel laureate Harry Markowitz, and it became wildly popular with advisors 35 or 40 years later, around the time Markowitz won the Nobel prize in 1990.
Optimizers are essentially investment planning sausage machines. You input the mean return, correlation coefficient, and standard deviation of each security or asset class in your portfolio, and an optimizer grinds out the optimal mix of each holding in the portfolio. The software shows you the least amount of risk you could take to get the most return out of a given portfolio, or, conversely, the highest return you could get while taking a given amount of risk.
What Your Peers Are Reading
Trouble is, optimizers are slaves to mathematics. If you’re building a portfolio with one security or asset class having far better returns than several other securities or asset classes, then the optimizer will tell you to put all or most of your portfolio in the winning asset class. Since most optimizers use return and standard deviation inputs based on long-term or near-term history, it’s like determining what’s ahead of you by looking through a rear-view mirror.
As a result, most advisors who use optimizers constrain their output. They tell the optimizer not to build portfolios with weightings too high in any one security or asset class, for instance, or constrain the portfolio suggested by the optimizer in other ways to tame its slavish devotion to statistics.
The meddling has caused a backlash against optimizers. If you have to put in all these constraints to make the optimizer make sense, many advisors now say, why use it at all? If you’re just going to constrain the optimizer until it gives you a portfolio that makes sense to you, why not just forget about using it and let common sense, experience, and judgment guide your asset allocation decisions.
Some rocket-scientist institutional money managers have devised statistical methods for constraining optimization, and with some research, an independent advisor could combine these statistical formulas with his or her view of the investment markets in order to use an optimizer more scientifically. In other words, if you do some research to gain an informed opinion, you could change an optimizer’s inputs so that they are based on statistics and common sense, and then, according to institutional money managers who like optimizers, you are likely to find an optimizer more useful. (In fact, Ibbotson Associates Portfolio Strategist software uses an optimization algorithm like this, and it makes judgments about future returns and the economy.) But doing this is time-consuming work, so instead most advisors conclude that optimizers are not worth the trouble.
Skepticism in the Ranks
Thus, it is no surprise that after getting eight advisors to participate in a demo of AdvisoryWorld’s ICE (Integrated Capital Engine), I found myself repeatedly having the same discussion about the merits of using optimizers. Advisors are skeptical about using this complicated tool, and that skepticism was probably the main reason why only one of the eight advisors who tried ICE said they were likely to buy it.
“Optimizers are too dependent on a single time period and you can make them say anything you want,” says Kathleen Day, a CFA and president of a life-planning firm in Miami called The Enrichment Group. Day, who used to show advisors at industry conferences how to use an optimizer, says ICE’s optimizer does what it is supposed to do. “But after running about 10,000 optimizations,” she says, “I just don’t see the value in it when common sense and judgment will get you to the same portfolio decisions.”
While the optimizer is the central engine of AdvisoryWorld’s ICE application, Day says several parts of this Web-based application are “interesting.” Day says she likes a retirement planning tool in ICE that lets you run Monte Carlo simulations with multiple cash outflows instead of just one single outflow, and sees this is as an improvement over most other Monte Carlo calculators. And ICE’s investment policy statement and risk tolerance questionnaire tools are also good, she says. However, Day stops short of saying that she’d pay the $1,900-per-year price tag to get all of the databases, tools, and reports.
The Appeal to B/Ds
Still, ICE may find a niche with broker/dealers looking for portfolio tools for their reps, and it does have the well-known name of AdvisoryWorld’s Phil Wilson behind it. Wilson, who entered the investment management business in 1964 and then spent more than 20 years in investment banking, institutional money management, and consulting, founded his own investment software company in 1987. Wilson Associates made software for portfolio construction, including RAMCAP, an optimizer that competes with Allocation Master, and a more powerful optimizer appropriately called Power Optimizer, which competes with products like Ibbotson Portfolio Strategist. Wilson’s two optimizers each have about 1,000 users to this day, he says. Wilson changed the name of his company about two years ago to AdvisoryWorld, and ICE is a centerpiece of his effort to create Web-based portfolio tools for advisors.
Wilson says some big independent broker/dealers have rolled out ICE for their reps, including LPL, National Financial Partners, and others. He says that about 5,000 reps are now using ICE and predicts that 30,000 will be using ICE by the end of the year.