By Rob Bush, Deutsche Asset Management
One of the key differences between factor investing and other types of investing is that factors are not mutually exclusive. With equities, for example, a stock is either German, or French, or Japanese, but never all three. Within sector classifications, a stock can either be in the Information Technology sector, or in the Financial sector, but never in both. So, put simply, a stock’s geographic allocation, and its sector allocation, are completely distinct.
However, this is not true of factors. In the factor world, a stock can exhibit both value and momentum; or it can exhibit quality and low volatility and size. Just because it has one characteristic doesn’t necessarily preclude it from having another (though there are limitations – sometimes one factor is defined as the opposite of another – for example value and growth – and now of course, the ability to belong to both categories is lost).
But the fact remains that, with many factors, which define stocks across suitably different parameters, stocks can have multiple features. Investors can, if they so desire, both have their cake and eat it too (i.e. both have their value and their momentum – eating them is inadvisable).
Given this distinction therefore, here is a suggested three-step framework for determining the “right” number of factors (“right” is in quotation marks because it is ultimately a subjective decision):
- Identify those factors that you believe in, and to which you want exposure.
- Satisfy yourself that they are complementary (i.e. not philosophical or definitional opposites that might net off, like value and growth), and that pairs amongst them are not so highly correlated that including both would be unnecessary.
- Think hard about the methodology for combining them.
In past blogs we have discussed the first and last parts of this framework and will feature more on the subject in future blogs so, for now, let’s focus on the un-correlated nature of various factors.
Five well known, empirically-tested, factors with strong heritage are; value, size, momentum, quality and low volatility. To get a sense of how stocks can look across each pair of these factors (ten permutations), we did the following. First, we took the Z-score for each factor that FTSE Russell assigned to each stock in the Russell 1000. This is just the standardized raw score for each factor, so the higher the better. Then, we simply produced scatter plots of each pair-wise combination of these scores. Two of the resulting charts are shown below.
Pair-wise Scatter plots for Z-scores on value, size, momentum and quality for the Russell 1000 index stocks as of 6/27/16 (Source: FTSE Russell and Deutsche Asset Management). Past performance is not indicative of future results.
Two important features stand out. The first is that the relatively uncorrelated nature of most of these pairs of factors is apparent. Indeed, the only pair that has an absolute correlation of greater than 0.50 is momentum and low volatility (and this makes some intuitive sense, a stock that is trending higher is likely to have relatively low volatility). So, it’s probably fair to say that there isn’t really an example of any pair of factors within these five having a correlation so strong that an investor might conclude – if they have one of the factors, then they don’t need the other. We’d argue that each brings something unique to the mix.
The second point is that, generally speaking, there tend to be quite a large number of stocks in the upper right quadrant of each chart (indeed roughly the 25% you’d expect from a zero correlation). In other words, there are stocks available that exhibit both characteristics, confirming our earlier point about the non-mutually-exclusive nature of factors.
Another way of looking at the question of the right number of factors to include is to reflect that investors ultimately face a trade-off: too few, though simple, risks leaving strong incremental drivers of equity returns “on the table”; too many risks additional complexity for the sake of, probably diminishing, marginal excess return.
The Bottom Line
Pick factor exposures that you believe in, and be aware of the trade-off between having too few (leaving excess returns on the table), and having too many (risks needless additional complexity). Ensure that your final choices are relatively uncorrelated so that needless factors aren’t included.