Abstract:
We propose a period-by-period machine learning (ML) framework to estimate time-varying risk premia and asset pricing functions in factor pricing models. We develop a rigorous asymptotic theory to interpret the output of the ML procedures used in asset pricing. Our approach enables an economic interpretation by decomposing return predictions into risk-related components and mispricing while allowing for flexible, nonlinear, and time-varying models. One of our empirical findings reveals a time-varying correlation between the equity risk premium and macroeconomic variables, particularly real consumption growth. This dynamic relationship sheds light on the long-standing equity premium puzzle. These results show that our method enhances predictive performance and provides critical insights into the dynamic relationship between firm characteristics, risk exposures, and asset returns. (Joint with Tracy Ke, Yuan Liao, and Andreas Neuhierl).
About the Speaker:
Jianqing Fan is a statistician, financial econometrician, and data scientist. He is Frederick L. Moore '18 Professor of Finance, Professor of Statistics, and Professor of Operations Research and Financial Engineering at the Princeton University, where he chaired the department from 2012 to 2015. He is the winner of The 2000 COPSS Presidents' Award, Morningside Gold Medal for Applied Mathematics (2007), Guggenheim Fellow (2009), Pao-Lu Hsu Prize (2013), Guy Medal in Silver (2014), Noether Distinguished Scholar (2018), Le Cam Award and Lectures (2021), Wald Memorial Award and Lectures (2025). He got elected to Academician from Academia Sinica (中央研究院院士) in 2012 and Royal Flemish Academy of Belgium (比利时皇家科学院院士） in 2023.
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