What if we developed a totally different type of risk model? I don’t claim to fully understand the process but it seems like we are looking for things like correlation and beta between basic factor models and our own custom portfolio. And then we figure out a way to lower the correlation of the two with custom weighting of holdings within our portfolio.
I struggle with this approach on a basic level. This is the technical analysis approach which says all we need to know is found in the price action. But is this the best approach?
Suppose you want to lower your risk to momentum stocks (either high or low). You can do this in many ways. The technical approach could be as simple as mixing 50/50 high momentum and low momentum stocks. The equity curve will likely not look like high momentum or low momentum because you mixed them. But that is exactly what you have inside. You are 100% exposed to the momentum factor. Of course, you could also run the correlation matrix on individual holdings as well.
The fundamental approach would be to rank stocks based on momentum and remove individual stocks with an extreme ranking either high or low.
It seems to me that we are adding in too many steps. First, Fama and French create simple factor portfolios to discover market-wide factor premiums. These portfolios spit out a time series equity curve. We compare our equity curve to the Fama French one and compute correlation and beta and so forth. Then we mix and match assets to lower our exposure to that factor premium. Why not measure that factor directly? Why create a some massive average and then compare ourselves to that average when we can measure the factor directly?
I don’t know that defining a stock as being ‘value’ or ‘growth’ or ‘momentum’ simply by comparing its price action to some broad composite is the best way to define it. If someone really wanted lower exposure to value, why not measure this directly using a value rank? This is one way it could work.
We create a portfolio in P123 in the Russell 1000 index. P123 spits out aggregate portfolio ranks based on value, growth, low volatility and so forth. If there is a score we don’t like, P123 can suggest some changes. It can filter out some of the highest factor ranks. It can suggest adding stocks with lower factor ranks. If we want to neutralize the factor premium, then our average factor ranks should be close to 50 out of 100.
I would be in favor of a more direct approach to limiting certain factor exposures if this is what is desired. But just because a value stock has low correlation to a broad portfolio of value stocks does not mean it you don’t have ‘value stock risk’. It just means it is trading differently than a broad portfolio of value stocks.