We are in the middle of a major risk management revolution that is calling into question our traditional definitions and processes for measuring and managing risk. One key area of concern has been the risk management questionnaire that most advisors use to determine the amount of investment risk to include in their clients’ portfolios and that have been built into multi-manager asset allocation programs and investment methodologies like Modern Portfolio Theory. There has been an increased amount of scrutiny regarding the accuracy of these questionnaires and their efficacy as a long-term investment tool. In hindsight, it seems that risk questionnaires were originally created as part of a simplified, paint-by-the-numbers investment sales process. However, as risk questionnaires have been evolving over the past 5 years, so has their value as an investment tool.
To better understand the evolution and improvements of these risk management tools, we talked to Larry Shumbres, CEO of Totum Risk – a FinTech company that provides a unique risk tolerance tool for financial advisors that calculates how much risk an investor can take given their current life situation (risk capacity) instead of the traditional model that only looks at how much risk an investor wants to take (risk preference). We asked Larry to take us through the evolution of the risk questionnaire tool and to address today’s more innovative approaches and potential future developments.
Bill Hortz: What fundamentally has been wrong with the traditional industry risk questionnaires?
Larry Shumbres: This is such an important question and the foundation of Totum’s inception. We looked at what was wrong with traditional risk questionnaires and set out to create a product that addressed all of these concerns.
The most obvious flaw with traditional risk questionnaires is that they solely focus on psychometric preferences. The problem with this model is that preferences change all the time. To accurately calculate an investor’s risk tolerance, emphasis needs to be given to how much risk they can take given their current life situation. And since the average household has one major life event each year, it’s important to give the questionnaire annually to make sure their portfolio aligns with their life changes. Unfortunately, most advisors only give traditional questionnaires once, further reducing the efficacy of their risk assessment.
Another major issue is the questionnaire itself which is often too lengthy and includes questions that are not relatable to the client or difficult to understand. Additionally, what some companies claim to be algorithms are actually just standard deviation or weak methodologies that would not hold up in arbitration.
Hortz: How did Totum define the problem differently when you started the process to better refine and build a more effective risk analysis solution for the industry?
Shumbres: At Totum, we knew our risk tolerance questionnaire had to relate to the investor and had to have deep academic models and methodologies to help advisors and investors better understand their true risk tolerance/aversion. In order to accomplish this, our product calculates 3 different risk scores. The first two are the investor’s risk capacity score and their risk preference score. When presented on an easy-to-read line graph, the space between these two numbers illustrates the “sweet spot” for their investment risk. We refer to this space as the Risk Band. Simply put, the Risk Band clearly identifies where the investor wants to be and where they should be.
The third score is the risk score of the investor’s current portfolio, auto-populated by Totum and presented on the same line graph. Together, these 3 scores set the foundation for a healthy conversation between the advisor and their client regarding the right amount of investment risk for their portfolio.
As I mentioned before, ease and accuracy are just as important to the investor taking the questionnaire. To address this component, Totum shortened the questionnaire and incorporated machine learning as well as over 60 pages of PhD designed algorithms on the back end to provide results that are accurate enough to hold up in arbitration.