First you need an expected return model. And assuming your predicting cross sectional equity returns, that model should utilize some sub set of things that fall under machinery. If you aren't trading in individual stocks, then what you do is dependent on the amount of data that you have. In cross sectional equities, you generally have lots o your models can be far more sophisticated. You want to count for structural sources of coverants, like industry, country, size and assume the remaining variance as residual. It's hard to build conditional expector returns in that space that are better than unconditional expected returns.
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.