Putting the psycho back into metrics like risk tolerance

14 February 2023 Paul Nixon Head of Behavioural Finance at Momentum Investments
Paul Nixon Head of Behavioural Finance at Momentum Investments

Paul Nixon Head of Behavioural Finance at Momentum Investments

Often ignored in the client’s risk profile is the important dimension of behavioural risk capacity. Said differently, how composed will the client be in the face of market turbulence? Other challenges are often conflated constructs like risk tolerance and risk perception in the client’s risk profile

Risk tolerance is a long term and stable attitude to risk and should give the same result if measured in a market crunch or market surge. Just like your personality doesn’t change your attitudes to risk don’t either (in general). If you get variable results, it means you’re measuring this incorrectly and are capturing risk perception instead – how much risk the client is feeling in markets at the time of measurement. Ultimately this creates noise or random variability in the advice provided.

This is different from bias, which is more systematic and therefore predictable. According to Klement (2015), less than 15% of the variation in risky assets between investors stems from their risk profile. If constituted correctly this should be far closer to 100%

The reasons are likely twofold:

Firstly, it is probable that investment advisers do not see the value in this process or indeed have different views in respect of what constitutes a risk profile and what the primary driver thereof should be. The variation in this case stems from the adviser.

Some advisers focus on required return (based on a cash flow analysis) while others simply reverse engineer the process to arrive at a predetermined investment solution. Foerster et al., (2017) studied approximately 180 000 Canadian brokerage accounts and found that risk tolerance, time horizon, financial knowledge and income only explain 13.1% of the variation in risky assets. When considering the influence of the adviser, however, 31.6% of the variation is explained. Said differently, the adviser is more influential in the riskiness of the portfolio than the client circumstance.

In a landmark study by Momentum Investments and Oxford Risk in the UK, it was found that only 28% of the variation in advice came from changing client circumstances – again this should be much closer to 100%.

Secondly, a proliferation of instruments that are poorly designed leads to inconsistent outcomes. The variation in this case resides in the instrument itself. Rice (2005) demonstrates from questionnaires collected that even when they are answered in the most conservative way possible, the resulting equity recommendations range from 0% to 70%. Furthermore, instruments often confuse perceived and apparent risk tolerance as mentioned earlier.

We should be wary of hypothetical win/loss scenarios in respect of their likely framing effects. Both money and risk mean different things at different times to different people. The outcome is often that the results are not particularly scientific; the results are neither reliable (consistent) nor valid (accurate). This predictably leads to adviser disengagement.

So how should these tools be designed? The answer is relatively straightforward: They should be designed psychometrically. This is as much about the process as the outcome. Classical test theory (CTT) in psychology is based on the notion that the score an individual obtains from a test consists of two distinct. parts. The first is the true score and the second is measurement error. The true score (such as measuring an attitude) can never be observed (only approximated) and the observed score will begin to resemble the true score as measurement error decreases (Grable, 2017). To do this the instrument should provide an outcome that is both reliable and valid. These are critical in the psychometric process.

Validity: This is the extent to which the tool measures what it was designed to measure (Grable, 2017). The purpose of the instrument is therefore an important starting point in its evaluation. Construct validity is important here. For example, are the items or questions related to the construct? If the instrument is testing risk tolerance or risk attitudes for example and is asking questions about the investor’s time horizon or cash flow needs, the construct validity is low because these considerations (while important) will reveal virtually nothing about the investor’s risk tolerance or attitudes. Convergent and divergent construct validity are important checkpoints here. We would expect items that are testing the same construct to converge (be strongly correlated)

Reliability: This refers to how much (or little) measurement error we are prepared to tolerate. The primary sources of measurement error stem from the test questions themselves (the list discussed in the previous section). Measures such as the Cronbach Alpha provide a reliability coefficient that can be used to determine whether measurement error is within acceptable parameters. An important part of this process is test-retest reliability where some time is allowed to pass and the same subjects tested again. The correlation between the first and second results give an indication of reliability.

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