As more and more countries have employed stay-at-home policy to halt the spread of COVID-19, the effectiveness of this policy has become an important question to both researchers and policymakers. To answer this question, our paper empirically measures the effect of stay-at-home policy on the...

This paper proposes a statistical framework in which artificial intelligence can assist human decision making. Using observational data we benchmark the performance of each decision maker against the machine predictions, and replace decision makers whose information process quality is dominated...

We predict asset returns and measure risk premia using a prominent technique from artificial intelligence -- deep sequence modeling. Because asset returns often exhibit sequential dependence that may not be effectively captured by conventional time series models, sequence modeling offers a...

We use proprietary futures brokerage data with intraday time stamps to analyze how embedded derivatives leverage affects trading and performance in the cross-section of investors. The effect is heterogeneous: Skilled investors benefit from leverage, while leverage has the opposite impact on the...

Blood type is one of the most fundamental phenotypes in biological, medical, and psychological studies. Using a unique dataset of one million Chinese pregnancies, we find strong evidence from a group of statistical tests for assortative mating on blood type. After controlling for anthropometric...

We directly optimize the objectives of portfolio management via reinforcement learning---an alternative to conventional supervised-learning-based paradigms that entail first-step estimations of return distributions, pricing kernels, or risk premia. Building upon breakthroughs in AI, we develop...