It is a massive improvement that these tools are now commonplace and available for use by advisers to assess suitability. However, it is likely advisers have also heard some criticisms of this method, some of which are valid.
One main drawback is that assumptions of correlations and returns can be inaccurate, especially over shorter time periods, typically anything less than five years.
Also, funds that are not traditional long-only funds cannot have a forecast volatility calculated.
For example, some absolute return funds, and funds using a large amount of derivatives, will not be able to have their underlying investments attributed to any particular asset class.
Another criticism is that while a portfolio may appear to be diversified on the surface, in actual fact the total level of risk will come predominantly from the riskiest asset classes.
For example, in the case of multi-asset funds, if we take a typical balanced fund split equally into bonds and equities, then in practice the equity volatility will drive most of the volatility of the total fund. And even if we diversify across other asset classes, such as commodities or property, it’s not uncommon during periods of market panic and stress for all of the asset classes to become increasingly correlated, thereby making the fund appear much more risky and volatile than originally anticipated.
Fund providers historically structured their analyst research and funds along asset class lines, but is this really the most accurate approach, or is this just convenient?
Risk on a diet
Using investment factors to measure risk is a more recent framework that has been gaining traction within fund management.
Investment factor models can be a more detailed way to analyse the types of risk inside a portfolio.
Instead of just considering asset class behaviour, investment factors go a step further and consider specific sources of risk, for example, inflation risk, credit risk, interest rate risk or equity risk. Factor-based models don’t conveniently attribute risk to existing asset class categorisations.
A useful analogy to think of factor-based fund exposures is similar to the nutrition you gain from eating food.
If you’re someone with a healthy lifestyle, you could be concerned about the level of fat in your diet. For example, if you’re monitoring your daily fat intake you would know that having a wedge of cheese contains more fat than drinking a glass of milk. So, you could drink a lot more of the milk and obtain the same amount of fat.
In addition, you would know that by consuming more milk instead of the cheese, you would obtain more calcium and other minerals.
