The last type of factor timing that i've seen is regime switzing models. I personally have not seen this work in contests of timi they can build amazing back tests, but seem to turn into random number generators ex post. Most regime switching models i see are 90 % confident there in one regime or the other. And then you infer conditional expect o returns based on those regime states. They're way to over confident, and thus they can result in bad decision make.
Vivek Viswanathan is the Head of Research at Rayliant Global, a quantitative asset manager focused on generating alpha from investing in China and other inefficient emerging markets.
Our conversation circles around three primary topics. The first is the features that make China a particularly attractive market for quantitative investing and some of the challenges that accompany it. The second is Vish’s transition from a factor-based perspective to an unconstrained, characteristic-driven one. Finally, the critical role that machine learning plays in managing a characteristic-driven portfolio.
And at the end of the conversation we are left with a full picture of what it takes to be a successful, quantitative investor in China.
I hope you enjoy my conversation with Vivek Viswanathan.