Measuring Client Risk Tolerance: Psychometrics Versus Revealed Preferences

Last month a war (of sorts) was declared, pitting various risk tolerance companies against each other with respect to their methodologies relating to portfolio risk assessment and analysis. The dispute appeared to be outside the realm of measuring a client’s appetite for risk, so to speak. However, there is a very real and clear line of demarcation in the world of risk-tolerance assessment “advisortech” with respect to the method employed in assessing the investor’s so-called “risk tolerance,” particularly the client’s psychological risk tolerance. In this respect, the point of distinction is (i) a traditional psychometric risk tolerance approach versus (ii) a revealed-preferences approach. Set forth below is my perspective on these two competing (and significantly different) approaches.

Psychometric Risk Tolerance Assessments

Psychometric approach to measuring risk tolerance from DataPoints

Psychometrics help to measure a client’s stable risk tolerance.

The assessments we have created here at DataPoints are based on the traditional psychometric approach. Psychometrics is the measurement of unseen human characteristics (personality, cognitive ability, etc.). There are various ways of measuring client personality, attitudes, and values, including everything from a structured interview guide to actually observing the client interacting with her bank account. From an efficiency and accuracy standpoint, however, assessments are often your best bet. They don’t involve human biases in measurement, particularly biases related to judgments during an interview or personal observation.

Make no mistake: measuring these unseen client characteristics is tough. Psychometric assessments are designed to measure the stable personality characteristics of your client. Still, they also have some errors in measurement (which is to say, they are NOT perfect). At DataPoints, we follow the methodologies of good test design to ensure we eliminate as much error as possible. One of the errors that we want to avoid is measuring something that has nothing to do with what we want to measure. In other words, we want to make sure we are accurately measuring the factor that we say we’re measuring. This is typically demonstrated through multiple validation studies– linking clients’ scores on a test (e.g., the Investor Profile) to some criterion (like investing decisions during a downturn).

The benefit for advisors (and, more importantly, their clients) from a psychometric approach is that the results can anticipate what the client might do in the future and guide the client to avoid less than ideal investing decisions. Additionally, a well-designed psychometric risk tolerance assessment can also be used with clients with little to no experience in investing.

Revealed Preferences: What Do I Want To Buy Today?

Contrast the psychometric approach with a revealed preferences approach to measuring a client’s risk tolerance. Revealed preferences are typically used in consumer science to reveal an individual’s “true” choice about certain items or services given certain scenarios and constraints. Think about this in the context of shopping for a car. Which car do you really want to buy from among a lot of cars that are the same price? If your income is held constant, and all the cars are the same price, your “true” preference would be the car you end up purchasing.

In the case of risk assessment, this same methodology has been applied to understanding a client’s current appetite for risk. I use the word “current” intentionally: revealed preference tests have the unfortunate side effect of being heavily influenced by what’s going on in the markets or an individual’s portfolio at that time. In other words, revealed preference assessments do not measure stable personality characteristics of the client.

Revealed preferences RTQs

A client’s score on a revealed preference RTQ may change depending on how the client feels about investing at the time. This makes the test unreliable.

It is not difficult to understand the appeal of a revealed preferences approach. The result more directly corresponds to portfolio allocation ranges because the revealed preference provides a numerical value that can correspond to portfolio volatility expectations. The problem that advisers have shared with us is simply that clients’ scores change. A client who scores a 97 today might later score a 75 after a market-climate change. We saw a significant change in preferences for risk from December 2019 to March/April 2020. Many advisors did as well.

Revealed preferences tests are not measures of stable personality characteristics. Instead, they are a way of understanding what a client wants at a specific point in time. Because the scores on revealed preferences tests are more fluid and less stable, there is less likelihood that they can accurately anticipate future behavior.

The Science of Measuring Client Risk Tolerance

There’s a debate brewing in financial services around how the psychological side of risk tolerance is best measured. It is becoming increasingly clear that fintech related to psychological risk tolerance assessment is dividing into two camps, (i) psychometric assessment versus (ii) revealed preference assessment. At DataPoints, our objective is to provide the most accurate insight possible to advisors and clients about their risk-related personality characteristics, including factors like volatility composure and confidence. We focus on measuring stable personality characteristics that have the greatest possibility of anticipating future behavior. We want you and your clients to understand their personalities better to assist you in (a) using your judgment to design a portfolio allocation that will maximize their potential for long-term success and (b) providing guidance to help clients become (or remain) effective investors. While the appeal of the match provided by the revealed preferences approach is enticing, we believe the psychometric approach is more likely to help your clients achieve long-term financial success.

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