Going Beyond Risk Scores to Reinvent Retail Investing

Aimvest originates from the idea that best practice is never good enough. In this article, we highlight the flaws of risk scores and explain how the industry can do better for investors.

Risk questionnaires are a norm when it comes to investment advice. They may vary in content and length, but their common purpose is to distill an investor’s complex profile into a single measure of how much risk their portfolio should assume. Inputs include age, wealth, income, investment horizon and investor behavior. The resulting measure, either qualitative or quantitative, is meant to be reductive enough so as to be interpreted by the person or the algorithm building the portfolio. It ultimately determines the target volatility constraint inputted in a portfolio optimizer function. There is no denying that it is hard to design the perfect survey: from the impact of recent market performance to question order, many factors can negatively influence the quality of the final metric. But is this delicate practice helping investors achieve their goals at all? Can a single score summarize complex situations and allow for effective capital allocation? Should you rely on it as an advisor? We answer those questions by the negative and suggest a better approach.

1. Risk surveys focus on the wrong thing

Risk surveys are a limited – or even counterproductive – tool because their output is used to optimize for the wrong objective function. They focus on appetite for market risk, trying to qualify (“conservative”, “aggressive”) or quantify it (“your risk score is 1”, “your risk score is 10”). 

However, investors (people!) only care about achieving their goals. I know this is going to sound weird if you are a finance geek like ourselves, but people usually do not understand, and do not want to learn about, investment risk. Yet, the way their money is invested today often depends on their answers to questions on their investment philosophy.

Take a look at some sample survey excerpts.

Isn’t it unreasonable to assume that investors can easily answer these questions in a precise manner, without being influenced by their daily mood?

Instead, wouldn’t it be much easier for advisors to strive to find out what their clients seek to achieve and let them express it in meaningful terms, and then use that for a better tailored investment advice?

A precise financial goal defined by a maturity date, a floor, a target, and contribution amounts and frequency is our way of doing so. These parameters can be updated at any time to reflect a change in the investor’s goals (e.g., “I got a raise and I want to contribute more” or “My future has become more uncertain and I need to increase my floor”). Only this type of approach can allow the portfolio builder to decide on a risk loading optimal to each goal at each point in time. 

2. Risk scores imply sub-optimal deterministic risk loading

Under the current paradigm, investment services providers cannot successfully optimize portfolios for financial goals. Indeed, age, wealth, income, behavioral patterns and other components inputted in the portfolio optimizer at the beginning of the investment period produce an output – an allocation strategy – which remains unchanged until the end of the investment horizon, unless the client retakes a lengthy risk survey or their advisor chooses to reallocate assets based on a rule of thumb. 

Such practice fails to maximize probability of success. The particular case of glide paths is worrisome: future risk levels are determined at the start of the project based on the initial risk score, implying that portfolio value evolution is not considered a relevant variable.  

Optimal risk loading through time ought not depend on time only!

Risk taking should instead become dynamic once the goal is properly recognized and expressed in terms of targeted levels of wealth. We've designed the Goal-Hedging Portfolio (*) with that in mind. As is illustrated below, the future value of the portfolio with respect to targeted amounts occupies a key role in defining the optimal risk level. As a result, the latter is path dependent.

3. Risk surveys blur the value of advisors

Throwing investors into risk buckets is a poor though common strategy which prevents advisors from creating as much value as they could. In addition, the advent of robo-advisors has been obviously challenging their added value to the retail investing value chain. Robos seamlessly integrate risk aversion surveys and diligently construct policy portfolios, mimicking the services offered by traditional advisors.

But advisors can foster the new revolution. By using risk surveys for compliance purposes only and by shifting the paradigm away from risk scores, they will be able to help clients define their goals in meaningful terms and target them more accurately. Advisors will be at the center of a feedback loop where every input directly influences outcome distribution, and where they will leverage their most valuable asset, their relationship, to set their clients on the right track to achieve their goals. 

"Because of its sole focus on market risks (risks embedded within asset classes benchmarks and associated investment managers), the traditional approach fails to account for what is the only relevant risk for individual investors, namely the risk of not achieving their meaningful goals" Lionel Martellini, Director of EDHEC-Risk Institute.

Aimvest started with a philosophy – that providing investment services is about exposing clients to a distribution of outcomes closely matching their goals. The current paradigm, arbitrarily mapping clients to a limited set of generic portfolios based on reductive risk scores, was far from being able to support our vision. We created our own model: risk surveys are replaced by collaborative efforts from both the client and the advisor. The mandate is defined in intuitive terms and capital is managed dynamically to maximize the chances of achieving the client’s goal. Client’s expectations are wisely managed, and their trust is earned through future validation.

(*) The GHP is our proprietary algorithmic portfolio management strategy. It is the mathematically-optimal portfolio to address goals defined by a floor and a target in the presence of variable and asynchronous contributions from investors. It allows investors to reach their target with the highest probability.