There is a “leap” from a consumer “risk profile” to the actual portfolio. Although the default assumption is that an extremely high-risk tolerance might translate to 100% equities and extremely conservative might be all cash – why is this?
In a research paper by Shawn Brayman, Nicki Potts, Kira Brayman and Yegor Komissarov presented at the Academy of Financial Services in Chicago in October they explored several methodologies that “map” from the client’s profile to portfolio.
- It compared three primary methodologies from the academic literature or regulators – Grable (2008), Davey (2015), MIFiD-II a European regulatory outline, against alternatives and each other.
- There was a strong correlation between Davey and MIFiD-II on mapping profiles to product based on the Value at Risk (VaR) of the products. Grable was similar but with a more linear scaling of the level of risk by profile.
- There were three methodologies used in mapping – behavioural expectation by consumers; exposure to equity holdings or VaR. Behaviourial expectation and exposure to equities is a valid heuristic but insufficient to scale to the wide variety of portfolios and products in the marketplace.
- Although the VaR calculation is recognized in multiple sources as the preferred methodology to align client concerns of drop in the value of their portfolio to the actual products, we found a fundamental flaw in the alignment of VaR (or volatility) and largest drops. It appears as if we misrepresent the likelihood of drops to clients. Clients live through events that are worse than what we describe as occurring once in 100 years as much or more than 9% of the time.
The purpose of the mapping is to align investments that will be suitable for the client, that they will understand the risk associated with, and if that risk should be realized the client will not take inappropriate actions (selling and crystalizing loses). A key challenge is how to explain the risks to clients in a manner they can understand?
This research found there seems to be a “loose consensus” on the mapping but there is a disconnect on the usage of volatility or standard deviation as the metric to explain this as it does not seem to relate to client understanding of “how far can it drop”. The methodology outlined by Davey (2015) of greatest declines or falls appears to have the strongest relationship to actual understanding and expectation by clients.