Computing and Countering Financial Complexity

January 28, 2021
Greg

Greg

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.

Key points

  • Form must follow function. Capturing clicks is no use without first capturing valuable, usable, client insights.
  • There must be solid scientific lines between the measurement of different components of a suitable risk level.
  • In investing, it is not the number of inputs, but the number of moving parts that constitutes the most relevant complexity for individual investors.

Navigating complexity means navigating the movements of humans

Fewer clicks. Fewer options. More automation. Less thinking. As personal finances become both more personalised and more personally managed, ways of making that managing simpler are everywhere. The wild hedge of financial complexity has been attacked by Occam's Razor and turned into a garden of neatly manicured apps.

This is both sensible and admirable. Investors are paralysed enough already – resistant to getting started with investing, and fonder of finding an answer than understanding why it's right for them. There's no need to amplify it with an unnecessary paradox of choice. Make things easier to understand, and easier to implement and you make better investors, or at least better investment decisions.

However, this can go too far. Form must follow function. Capturing clicks is no use without first capturing valuable, usable, client insights. In suitability, this can manifest in several ways:

  • Muddling distinct components of the risk profile – While they all play a part in determining a suitable risk level for an investor to take, there's a crucial difference between a long-term willingness to take risk, a financial ability to take risk, and a short-term emotional ability to take risk. While they must ultimately be combined, there must be solid scientific lines between the measurement of each, however easy a corner it is to cut to blur those lines or overlook infecting correlations.
  • Jumping from a 'risk number' to a solution – Model-portfolio solutions can be a sensible means of reducing complexity; blindly linking them to a risk assessment is not.
  • Prioritising client reassurance over client outcomes – The point of suitability is to ensure an investor ends up with the best investments for their personality and circumstances, not to make sure they feel comforted at any cost. Forgetting what you're trying to do and why in favour of how you're doing it isn't suitable.
  • Skipping over the necessary science – Regulatory risks rise as the seriousness of testing investor attitudes to risk falls. On the surface, it can be difficult to tell a good psychometric assessment from a bad one. Even the same statement can be fit for purpose in one group of statements and unfit in another. The tests must be rigorously tested for reliability and validity.

Each of these errors is a result of trying to save an investor from seeing the complexity of the decision process matching investors to investments – especially the inputs into it. This is misplaced effort. For what is the complexity we should be concerned about?

In investing, it is not the number of inputs, but the number of moving parts that constitutes the most relevant complexity for individual investors.

Because investment markets move around more than an investor's relatively stable risk tolerance, traditionally most attention is paid to profiling at the outset. However, over the course of an investment journey, it is the moving human parts – a panoply of behavioural reactions – that are more worthy of attention.

A focus on the inputs is understandable, but a reliable conclusion from forecasting studies is that while consideration of more variables increases confidence, it doesn't increase accuracy, and it dilutes relevant factors in a homeopathic ocean of irrelevant ones.

Suitability is dynamic; it suffers when it's seen as a snapshot. Because profiling is about the human that owns an investment, it needs to first profile personality more comprehensively, and remember that investors continue to be human throughout their ownership of an investment.

Investor understanding is crucial. But helping investors navigate complexity is better than pretending it can be cost-effectively avoided. The real returns from an understanding of the investor are preferable to an artificial understanding by the investor.

Originally published in New Model Adviser on 20/2/2020.

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