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Structural Deep Learning in Financial Asset Pricing
November 8, 2022 @ 4:00 pm - 5:00 pm
Jianqing Fan (Princeton University)
Abstract: We develop new financial economics theory guided structural nonparametric methods for estimating conditional asset pricing models using deep neural networks, by employing time-varying conditional information on alphas and betas carried by firm-specific characteristics. Contrary to many applications of neural networks in economics, we can open the “black box” of machine learning predictions by incorporating financial economics theory into the learning, and provide an economic interpretation of the successful predictions obtained from neural networks, by decomposing the neural predictors as risk-related and mispricing components. Our estimation method starts with period-by-period cross-sectional deep learning, followed by local PCAs to capture time-varying features such as latent factors of the model. We formally establish the asymptotic theory of the structural deep-learning estimators, which apply to both in-sample fit and out-of-sample predictions. We also illustrate the “double-descent-risk” phenomena associated with over-parametrized predictions, which justifies the use of over-fitting machine learning methods. (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) and Guy Medal in Silver (2014). He was elected to Academician from Academia Sinica in 2012.
Fan is interested in statistical theory and methods in data science, statistical machine learning , finance, economics, computational biology, biostatistics with particular skills on high-dimensional statistics, nonparametric modeling, longitudinal and functional data analysis, nonlinear, survival analysis, time series, wavelets , among others.