The information ratios nearly doubled from the factor approach and the machine learning approach was walk forward out of sample, while the factor approach had unavoidable in sample bis. When you choose the factor approach over the optimized machine learning approach, you're basically giving up high risk, agested returns for ease of explanation - which is sometimes a mutually beneficial trait. Highacter predicts higher stock returns are related to the underlying characteristic, not the auto correlation. It matters that the highest re-weigh has an effect on expected returns. A feature should drive more expected returns if it is dispersed as it is in linearage. And i should say, there is still an advantage to smart bata, which is easy transparency
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.