Clustering and Risk Profiles (w/ Raoul Pal & Trevor Mottl)
TREVOR MOTTL: It's pretty interesting when you look at how stocks cluster through time. And in some periods that we view as very favorable to picking stocks, you see relatively numerous distinct clusters with stocks behaving in a relatively idiosyncratic pattern. But through periods of stress and turmoil, what you frequently see is aggregation of clusters, and you start to see pictorially see that risk-on, risk-off feature in those clusters. And in periods where you see clear winners and clear losers, you also see that. So if you go back to the Trump election, there were some clear clustering occurrences that you'd see between stocks that had paid high taxes versus stocks that paid low taxes, small caps versus large caps. So the features of those clusters became very distinct, and you really saw a bifurcation of the market in that period of time. RAOUL PAL: So when you see something like that, that sets up an opportunity to say, can we capture some alpha out of noticing these clusters or the changing clusters that maybe the market hasn't picked up yet? Well obviously, the market has in price but not cognitively. TREVOR MOTTL: So we use those cluster definitions as features in our model. So rather than responding in a human sense to it, we build that feature into the model so that the model understands, during period of where clustering looks like this, X, this is what the risk profile is. It could be. This is what the risk profile has been in the past. And when clustering is in a Y state, then the expected risk profile is likely different. So it's a feature that we use to train the operative.