We came up with this method of stacking in 20 16, before we even heard of this debate. It's not like mixing and integrating approachs never entered my conscioucness. But we quickly decided stacking is superior. You can either gain or lose active weights based on agreement or disagreement. If two factors agree, you'll add active weight. If they disagree strongly, you'll lose active weight. Now for what its worth, if you allow your portfoid to short stocks, or if your active weights are extremely small, then the mixing and the stacking approaches differ only by multiplier on active weights. But if you're in a standard long only these approaches have very real differences.
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.