The typical arguments against machine learning are, it's a black box. It's going to lead to overfit models when you combine a hundred and forty characteristics. So linearage, grady boosting and randam forest aren't blackbocks. They're just not olus. But we do have our own attribution system, and there's nothing really proprietary about it so i'm happy to explain it.
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.