Globally recognised expert in applied decision science, behavioural finance, and financial wellbeing, as well as a specialist in both the theory and practice of risk profiling. He started the banking world’s first behavioural finance team as Head of Behavioural-Quant Finance at Barclays, which he built and led for a decade from 2006.
This is the fourth post in a series giving our response to the FCA’s Call for Input on how to apply behavioural finance to help people make engaged investment choices more comfortably and confidently, and what role regulations can play in helping that to happen.
The series highlights four broad areas where better application of behavioural-finance insights can lead to better investor outcomes.
This post covers more elements of a suitability process that are currently overlooked, including pension transfers, ESG, and adviser noise.
The full series (additional links to be added later) is:
The last post looked at how suitability processes overlook investors’ emotional ability to take risk and so end up being designed not for humans, but zombies. Here we look at the implications of this in relation to some specific areas of advice: pension transfers; ESG investing; and adviser noise and inconsistency.
When considering pensions transfers, advice is generally driven by quantitative assessments of the costs, benefits, and risks of an annuity stream versus a lump-sum that can be invested and drawn down.
However, all this modelling rests on two vast behavioural assumptions:
Whether they do is heavily dependent on each investors’ Composure and Impulsivity (readily measurable using robust psychometric assessments of the sort we provide at Oxford Risk) among other behavioural dimensions.
An investor who is low Composure and high Impulsivity should absolutely have a different threshold for whether a pension transfer is suitable or not, and a different answer to this question than a high Composure, low Impulsivity individual.
The behavioural considerations should frequently entirely outweigh the quantitative assessment of financial costs and benefits, and yet are often barely considered, if at all.
Consideration of non-financial outcomes and client needs are particularly pertinent when advising on sustainable-investing solutions.
Delivering client needs should consider not only narrow financial needs, but also their values and preferences for non-financial outcomes. This is something that is becoming ever more important to investors, so it is perhaps notable that responsible/sustainable/ESG investing in particular doesn’t seem to be under consideration at all in this Call for Input.
In late 2020, Oxford Risk’s behavioural suitability tools were expanded to include the option for measuring investor attitudes to responsible investing: highlighting investor preferences for using their wealth to achieve positive social and environmental outcomes, and making recommendations for how to use this information to better align their wealth with their values.
The FCA also asked: Are there other underlying issues which have an impact on the consumer experience in this market that you think we should consider? What are they and how do you think they affect consumers?
A huge and largely under-appreciated issue is less the problem of behavioural bias on the part of either advisers or consumers, but that of adviser noise.
It is entirely possible for advisers to be, on average, unbiased, but nonetheless to give very inconsistent answers to different clients, or to the same client at different times.
This is an issue that Daniel Kahneman has been drawing attention to in recent years, particularly in his HBR article of 2016 (Noise: how to overcome the high, hidden cost of inconsistent decision making) and indeed is the subject of his May 2021 book (with Olivier Sibony and Cass Sunstein): Noise.
It is an area of research we believe is ripe for investigation. Our first formal ‘Noise Audit’ – a statistical look at the scale and subtleties of the problem of inconsistent advice – was released in April 2021, in partnership with Momentum Investments in South Africa.
In financial advice, noise stems from most suitability processes being very human heavy, and thus prone to the errors, biases and inconsistencies of human subjectivity.
In short, the advice given to any particular investor may differ wildly from adviser to adviser (even within the same firm with the same processes), or within a single adviser depending on the surrounding context and timing.
Even if the advice of any one adviser is on average unbiased, this is cold comfort if each individual client gets advice on the day that is substantially inconsistent with the most suitable advice.
The right investment solution should differ based on the investor circumstances it’s recommended for, not the adviser that’s recommending it.
Consistency of advice is a crucial concern for any firm. If what is deemed suitable for a client can differ depending not only on which adviser within a firm they speak to, but also on the prevailing mood of a particular adviser, then that firm has a problem. Especially when we remember that advice isn’t a single event, but an ongoing relationship, and that the regulations care not whether you get it right on average, but whether you get it right for each individual.
The two main sources of inconsistency in advisory processes are an overreliance on humans, and the heavily front-loaded nature of suitability assessments.
Upfront assessments are necessary but insufficient, and often overplayed. Suitability reflects circumstances. Circumstances change. Constantly. Sometimes changes are imperceptible. Sometimes pandemics happen. Because this is inherently complex, we are drawn towards the sanctuary of the status quo. Overemphasising initial assessments makes investment solutions over-fitted to the investor’s circumstances at that single point in time, and unresponsive to subsequent changes. They drift away from what is suitable over time. And in times of crisis this drift can become a dash.
While Risk Tolerance is relatively stable, Risk Capacity has many moving parts. Studies on multi-attribute decision-making show that even when people think they’re assimilating evidence from all sources, they’re really just filtering down to the few that stand out. And that few isn’t consistent over time, let alone over different decision makers.
The aim is not to turn advisers into algorithms. Humans are wonderful at many things. But they are inefficient and unreliable decision makers, especially where many moving parts are involved – as in Risk Capacity. Humans are prone to noisy errors – unduly influenced by irrelevant factors, such as their current mood, the time since their last meal, and the weather.
As shown in the diagram at the top of this post, noise isn’t bias. Bias is systematic: it errs the same way every time. Like a mapping model that puts every client into too risky a portfolio. Noise errs in more mysterious – and therefore less easily manageable – ways.
At present, it is fair to say that the advice industry has little notion of the existence of this problem, and certainly little notion of the extent to which this is costly to consumers.
However, two recent academic studies offer considerable cause for concern. They showed that specific case studies detailing all the client financial circumstances and attitudes required to make reasonable suitability assessments were given hugely different risk levels and investment solutions when evaluated by pools of advisers (see Hubble and Grable 2019, and Baekstrom, Marsh and Silvester 2018). At Oxford Risk, we’ve also conducted a similar study for a client, with similar results.
Establishing frameworks to drive consistency in diagnosing situations doesn't mean giving every client the same answer. It means those answers need to be within boundaries defined by a clear diagnosis of the problem. There are multiple paths towards remedying any situation, depending on client personality, circumstances, and engagement.
Identifying noise isn’t about eradicating inconsistencies. It’s about eradicating unjustifiable ones and evidencing justifiable ones