Revealing Preferences

An interesting new risk tolerance instrument comes with strong scientific credentials.  It also helps identify, in advance, which clients will freak out in a downturn.

According to our recent software survey, 43.5% of advisory firms use some form of risk tolerance assessment tool with their clients.  But I suspect that the real number is close to 100% if you add in all the ad hoc questionnaires floating around, plus all the self-created assessment processes and in-person interviews where clients are shown the horrors of past bear markets and asked how they would react if they were to experience another one, fully or partially invested in risk assets.

In the past, the dominant program in the U.S. market was FinaMetrica.  Its value proposition has always been compliance-related: don’t you want your file to contain a psychometrically-proven assessment of a client’s risk tolerance if/when somebody’s lawyer asks you whether this client’s portfolio was appropriate?  More recently, we’ve seen Riskalyze take over as market share leader with a more marketing-oriented proposition.  In presentations touting the program, advisors are told to compare a prospect’s existing portfolio’s risk score (provided by Riskalyze) with the prospect’s Riskalyze risk preference score.  If there’s a discrepancy (and there usually is), then it presents you with an opportunity to throw shade on the prospect’s current advisor’s judgment and win over the client relationship.

But what if you can have both, and also a more nuanced understanding of the how the client thinks about investments?  I’m asking myself this question as I take a brand new risk tolerance instrument called Risk Essentials, formally introduced into the U.S. market on February 5 by a company called Capital Assessments, whose product line is called TrueProfile (www.trueprofile.com).

Risk Essentials appears to be built on impeccable scientific credentials; it was created by University of California, Berkeley economics professor, and former chair of the department, Shachar Kariv, who has been studying the way consumers make decisions for the past 15 years.  Among his projects are some widely-used phone and computer apps that use game theory to tease out peoples’ decision preferences, now having been completed by tens of thousands of people in the U.S., the Netherlands and Australia.  Along the way, Kariv has used the data and some decision-making theory to help the World Bank assess the stability of its microfinance loan processes.

How can Risk Essentials improve on the risk tolerance instruments we already have?

“If you told me that you took a risk questionnaire, and your score was a 75,” says Kariv, “the first thing any scientist would ask you is: with what probability?  What is the probability that you misclassified me, and I’m really a 60?  What they’re really asking for,” Kariv continues, “is the mathematical model behind the instrument, what we call the statistical confidence intervals.  But in the questionnaires you see on the market, there is no statistical theory to provide this information.”

Another problem is the nature of the questions themselves.  You ask whether a client would prefer this particular possible return with this particular possible downside, and that gives you one single data point.  Kariv finds that far too limiting in a world where clients would not be willing to answer more than, say, 25 questions.  “To get more information more quickly, you want the client to make choices from many many possible alternatives, rather than a ‘yes’ or a ‘no’ on only one,” he says.  “So you never ask questions.  You give decision scenarios, and ask people to take action in these decision scenarios—which are not perfectly hypothetical, because they reflect what happens in portfolios.  It is what we call a continuous choice set, where a client or participant will pick one position out of many, many alternatives.”

Kariv argues that the amount of information you can collect from six decision scenarios is much greater than you could get from 100 or 200 binary or multiple choice questions.

Third, Kariv believes that advisors would want to know, not only a client’s tolerance for volatility, but also how sensitive the client is to market loss.  This gets to something industry commentator Michael Kitces has been talking about for years: the ability to tolerate volatility can be very stable over different market cycles, but risk perception—that is, what the client expects or fears from the markets—can change dramatically depending on whether we’re experiencing a euphoric bull or a panicky bear run.  It can also change if the client has a newborn baby or other life event where losing the nest egg carries more consequences than it did before.

Finally, the risk tolerance instruments on the market today don’t assess how consistent the client or prospect’s answers are—which is another dimension to unraveling what the client is actually thinking. 

This gets back to what Kariv was talking about earlier with his statistical confidence intervals.  He says that a small subset of your clients will closely resemble what economists call homo economicus, the robot-like pseudo-human who always makes perfectly consistent decisions that line up perfectly with his preferences, who always “maximizes the utility” of every decision.  You know those clients; they understand what they want, they don’t panic in market cycles, they save reliably and follow your advice to the extent that it aligns with their goals.

“You want to measure, mathematically, how close or how far a person is from being homo economicus,” says Kariv.  “When you have a client who doesn’t have what we call ‘stable preferences,’ it means you would want to treat that client very differently from your most rational clients,” he adds.  “You know that you should communicate with that client much more often when there’s a market downturn than you would clients who are closer to homo economicus.”

In fact, you might slip into your CRM different codes for different clients based on their scores; ‘gold’ clients are unlikely to call you no matter what the markets do, while ‘silver’ clients, who have less stable preferences and more loss aversion, might need an occasional message to let them know you’re monitoring the situation. 

‘Bronze’ clients, who don’t have stable preferences and have demonstrated high loss aversion, can be expected to suddenly fall apart during the bear market, even though your traditional risk profile instrument confidently matched them with a moderately aggressive portfolio mix.  Now you know why.

Revealing sliders

Can you do all this with a simple questionnaire?  I was skeptical as I went through the ultra-simple onboarding process, picking my financial goals from a list, inserting how much I’m looking to invest ($1 million) and the earliest when I plan to withdraw the money (10 years).

On to the questions!  Of course, as Kariv noted, these are not questions at all: I encounter a screen that shows a sideways triangle, which represents risk/return space.  I can move a slider up and to the right to put more of my TrueProfile-Imagemoney at risk.  (See graphic)  At every point up and down the slider I can see the potential upside and potential downside of this portfolio mix, based on the risk/return characteristics of what I later discover is the “moderate opportunity” portfolio. 

I like the odds, and make a choice that is pretty close to the maximum.

The second screen looks very similar, only now I’m invited to invest $250,000 of my portfolio somewhere on the spectrum, and the odds of return vs. risk look even better.  (This, I learn later, is the “highest opportunity” portfolio.)

I make similar selections on the third screen and on a fourth screen, which I later learn is the “lowest opportunity” set, I become a little more cautious.  Finally, I’m presented with what is later labeled a “high opportunity” portfolio, and I again swing for the fences.

The bottom line is that after looking at just a handful of screens, I have chosen from probably several billion possible total combinations of choices, and the ensuing report tells me that I have been fairly consistent in my answers.  My risk tolerance score is 98 out of 100, which is toward the top of the high zone.  Interestingly, I have a medium loss aversion of 20 out of 100, toward the lower end of the zone.

The software then maps these results to different model portfolios that my advisory firm uses with its clients, and shows me the best fit.

Best fit

From the advisor’s perspective, you can go to the TrueProfile dashboard and upload existing client contact information from a spreadsheet.  From there, you can invite clients to take the Risk Essentials test with an email or—as most early-adopter advisors seem to be doing it, you can invite clients to your office and the two of you can go through the instrument together in person. 

In the setup phase, you also would identify the broad asset classes in each of your model portfolios and upload them into the system: 25% large cap growth, 15% large cap value, 20% international stocks, etc.  A future version will allow advisors to use one of the account aggregation engines to upload model portfolios directly, and have a more granular allocation.  For advisors without model portfolios, you would upload asset mixes along the efficient frontier, and customize a portfolio for clients based on the general outlines of their revealed preferences.

If you’re working with a prospect, you might also want to input the client’s current portfolio, mapping the specific funds or ETFs to these broader asset classes, so that your lineup might include, say, ‘conservative,’ ‘balanced,’ ‘moderately aggressive,’ ‘aggressive’ and ‘Current Portfolio.’

After the prospect takes the test, the report shows a risk tolerance score, a loss aversion score and the stability of the prospect’s preferences.  If the stability is low, you might pause a second before deciding to take on the prospect as a client.

The second page shows the portfolio that would be the best fit, and the composition of that portfolio.  An interesting graph at the bottom shows how well the other model portfolios fit the client’s profile.

Why would this matter?  Kariv posited the not-so-unusual scenario where a client’s risk tolerance and need-to-take-risk have diverged; the client’s goals won’t be achieved with, say, a conservative asset allocation, even though that’s where the client would feel most comfortable.  So you can look at more aggressive portfolios and see how well they might fit—and for some clients, other portfolios may not be dangerously far from an exact fit.  You talk this over with the client, and if you mutually agree to go with something that is not an exact fit, this is noted in your CRM for future discussion whenever the markets are going down.

This process would also let you compare the prospect’s existing portfolio with his or her risk tolerance and loss aversion score—and as you know, the match will probably be imperfect.  The prospect can see how much closer one of the other portfolios would fit with his/her preferences.

Risk Essentials is the first of a bunch of different client assessment tools that Kariv has on his drawing board, all with a similar gamification interface.  Next up will be an instrument that looks at clients’ propensity to save vs. spend under different circumstances and return environments.  After that: a game that would allow clients to put their goals in a hierarchy.

Cost?  There’s an introductory price through April 30 of $99/month per advisor; the cost will go up to $129 per advisor per month thereafter.  The company offers a free 14 day trial, so you can test it on clients and prospects before you make the purchase decision.

The bottom line:  There seems to be no question that Risk Essentials gathers more information about clients, more quickly, than the other instruments in the marketplace. 

Yes,  I really do wish there were a more precise asset mapping process for the model portfolios, and I suspect that the instrument will be easier to use once there’s a Quovo integration that lets you upload a prospect’s existing portfolio data and map it to asset classes automatically.  But having the consistency measure and adding a measure of the sometimes-unstable loss aversion aspect of a client’s psychology might be tremendously valuable when the markets take their inevitable downturn.

Risk Essentials covers the bases of a scientifically-defensible client assessment tool, and it has the potential to be a marketing instrument.  Most importantly, it helps you better identify in advance those clients who are likely to freak out unexpectedly when you thought you had them in the perfect portfolio for their psychology and needs. 

I look forward to including the instrument in the next software survey, where I expect it to raise the overall market share level for risk tolerance instruments among advisors who have been hesitant about replacing their ad hoc assessment procedures with a professional tool.