Financial advisors have it harder these days. Not only has market volatility whipsawed client equity. What's worse, investment pros also have to fight — really fight — to keep client assets. Many individual investors are aiming to go it alone to manage their own portfolios.

A number of online resources are aiding and abetting self-directed investors, including so-called “black box” asset allocators. These applications range from the spartan, four-question engine on the CNN/Money web site to more robust calculators like the one found on PortfolioMonkey.com.

Helping Them Help Themselves

Most of these sites leave users with an allocation to decipher and employ on their own. One newcomer to the asset allocation scene, however, wants to close the loop by sending investors back to their advisors with a portfolio.

Now in public beta testing, Riskalyze (at www.riskalyze.com) generates portfolios of individual stocks, mutual funds or exchange-traded notes and funds. Users can access an e-mail interface to ship the allocations directly to their brokers or advisors, presumably as conversation starters.

Certainly, the portfolios modeled by Riskalyze are bound to stir discussion. For one thing, they're generated with just three parameters. “We view every individual's portfolio equation as having three ingredients,” says Riskalyse CEO Aaron Klein. “A list of stocks, ETFs or mutual funds they want the algorithm to consider; an economic prediction for the next six months; and the investor's unique and personal ‘Risk Fingerprint.’”

Naive Forecasting

Market outlooks are user-selected from a spectrum ranging between “optimistic” and “pessimistic.” That alone is enough to spark discourse among some advisors. Rick Ferri, founder of Portfolio Solutions LLC, a Michigan-based manager of nearly $1 billion, thinks an investor's short-term hunch isn't a firm foundation for a portfolio. “I don't get the idea of asking investors how they ‘feel’ about the markets over the next six months,” says Ferri.

As it turns out, investors see the past as a default for the future. “Most users will stick with the historical returns model which averages the returns of the last 40 years or so,” says Riskalyze's Klein. “Others may believe the future will be similar but not quite as good. And still more will want to invest with a very specific prediction in mind.”

In mid-January 2012, for example, the Riskalyze engine would have spit out a 15-stock portfolio in response to a query from a modestly risk-seeking investor (the Risk Fingerprint) expecting a historic return of 5.4 percent over the ensuing six months.

Riskalyze benchmarks its portfolios to the S&P 500, making tracking error a concern; a large error could impede the investor even if his or her forecast is realized. That risk isn't small. Ronald Surz and Mitchell Price, then of Roxbury Capital, found that a typical 15-stock basket strayed as much as 8.1 percentage points a year from the market's return. Thus, a 10 percent uptick in the S&P 500 could translate to anything from a handsome 18.1 percent gain to a paltry 1.9 percent uptick.

Further diversifying the portfolio with additional stocks reduces the tracking error, but even with as many as 60 well-chosen securities, Surz and Price found the typical tracking error was still 3.5 percentage points a year.

Riskalyze, like other asset allocation algorithms relying upon Modern Portfolio Theory (MPT), uses historical average returns and standard deviations to generate an efficient frontier from which the optimal portfolio is derived. The frontier is a curve representing the intersections of expected returns and anticipated risk. Efficient portfolios plotted along the curve effectively minimize risk for any given level of expected return.

It's important to note that MPT only defines the distribution of outcomes, not the paths that are followed to arrive at them. An advisor may well wonder, and wish to ask, what matters most to an investor — the path or the objective. After all, two investors can have the same goal, but the paths they're willing to follow to reach it could be markedly different.

A trader, for instance, would be more likely to alter his or her allocation on a daily or weekly basis to achieve a given return. That has implications, too, for the model's inputs. The appropriate risk and return metrics for optimization of the trader's portfolio would need to be daily or weekly data points. In contrast, the MPT data for an investor with a longer holding period in mind, say a year or two, should reflect that time frame. The inputs into the asset allocation decision framework need to be consistent with the client's willingness and ability to adjust his or her holdings. Here, black boxes like Riskalyze are typically opaque.

Reliance upon historical data necessarily assumes that each period's return — whether daily or yearly — is independent of that of every other and is equally likely to repeat. In other words, the past is always assumed to be a prologue to the future. That could be a dangerous assumption, especially when outsized volatility is embedded in the data record.

Klein boasts of the Riskalyze technology that identifies the time slices close to a user's prediction to build a prediction model for each investment. There's little user input for the model. Aside from the outlook for the short-term investment and desired security universe, risk tolerance is the only user variable employed in the portfolio. “We don't use any subjectivity in our allocation assumptions,” says Klein. “It's pure math.”

Klein acknowledges that his system's opacity is intentional, but it's also fleeting. “We've done our best to mask the complexity of what's going on under the hood to build a product that individual investors can understand,” he says. New features will soon be rolled out which should help users better see the algorithm's moving parts.

No matter how transparent a system like Riskalyze becomes, some advisors will never be comfortable with a top-down approach to portfolio construction. Count Larry Swedroe, principal of St. Louis-based Buckingham Asset Management LLC, among them.

The Problem with the Efficient Frontier

“There are lots more issues related to the right asset allocation beyond one's risk tolerance,” says Swedroe. “Thinking that is the only thing is an awful mistake. I wrote a whole book on the ‘Right Financial Plan’ and how to determine the right allocation. Thinking you can do it with a few questions is foolhardy at best and dangerous at worst. It's like giving a high-powered NASCAR machine to a drunk teenager.”

The essential problem advisors like Swedroe see with efficient-frontier-based models lies with its data inputs. “Very small changes in data can lead to massive changes in recommendations,” Swedroe opines. “You get crazy answers.”

Take for example, the equity risk premium for the S&P 500. It was 6.7 percent for the 1926-1990 period; when the bull market of the 1990s is included, it jumps to more than 8 percent. The difference makes for considerable lopsiding in a portfolio's equity/fixed income mix.

The same holds true for asset class returns and correlations. Changing the time period sampled or the data point frequency can greatly impact the appearance of the optimal portfolio. Whole asset classes may, in fact, be entirely excluded. To combat this, practitioners often impose constraints on efficient frontier models, such as holding an asset class to no more than 20 percent or no less than 5 percent of a portfolio.

Imposing constraints produces, in Swedroe's thinking, the same outcome as a simple common sense approach — a relatively balanced globally diversified portfolio with exposure to all the necessary asset classes. And, for this reason, he views modeling as a waste of time.

Time is an important factor in the common sense approach. Swedroe normally takes two hours in a face-to-face meeting with prospective clients to gather the necessary intelligence for portfolio development. “We have a bank of over 100 questions we can pose,” he states. “We may ask as many as 50 in a discovery session.”

Swedroe goes beyond an appreciation of an investor's risk tolerance to include other factors. An appreciation of a client's spending patterns is critical in the determination of income needs. Living expenses are likely to be more modest for a client in rural Arkansas as opposed to New York City, making for variances in bond allocations.

The client's labor capital is also considered. A construction worker, because of his higher income variability, may require a lower compensatory beta. More importantly, Swedroe wants to know how much money the client wants to leave as a legacy. For all these reasons, Swedroe prefers to build portfolios from the bottom up, though all his clients may not require every allocation.

It's not likely that advisors like Larry Swedroe or Rick Ferri would welcome Riskalyze-generated portfolios in their e-mail inboxes, but that's not dissuading Aaron Klein. He, in fact, says some advisors have voiced an interest in using the Riskalyze technology themselves. “We've been hearing from a number of advisors who are really excited about the potential impact of applying our technology to their business,” says Klein. “They see how it will help them build client satisfaction, and increase their protection against frivolous lawsuits over ‘know your customer’ rules.”

In the meantime, Klein expects the Riskalyze site to remain free for individual investors. “At this point,” he says, “we don't even have plans for paid premium features. We think there are plenty of revenue opportunities to build a marketplace around investing ideas, access to great advisors, and licensing the technology for advisors and brokers to use with their clients.”

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*With apologies to the Beastie Boys' hit song, “(You Gotta) Fight for Your Right (to Party!)”