There are too many significant anomalies for the result to be driven purely by luck. Almost every signal is a quality signal, and quality signals tend to be negatively correlated with size and value. A c r show this with respect to size, in their size matters. The idea holds for a variety of quality factors, even ones that come from alternative data sources. If your asa prising anomaly generates more significant alphas than there were significant factor returns in the first place, that is not a successful mor because there is now more significant factors than before. But if you want interactions and non linearties, you need grading boosting and random force. You don't get to ignore those guys.
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.