Optimal Prediction of Asset Returns and Factors of the Stock Market

The presentation will consist of two components. The first focuses on optimal prediction of the S&P 500 in a framework that aggregates a large set of predictors into a single linear combination with strong oracle properties. The second examines how to exploit the novel feature of factors that switch between active and inactive states over time in asset pricing and investment decisions.

Guofu Zhou is the Bierman and Spears Professor of Finance at Olin Business School, Washington University in St. Louis. He received a B.S. in Mathematics from Chengdu College of Geology, China, and a Ph.D. in Economics from Duke University. His current research focuses on big data, machine learning and AI applications in finance. His recent work includes developing new factor models, proposing novel sentiment measures, advancing machine-learning methods for selecting firm characteristics and forecasting asset returns, estimating announcement risk premia, and studying the market impact of ESG and other economic risk exposures. He has published extensively across leading finance journals and has received numerous awards for both research and teaching.

Host: Xiaofeng Shao