Speaker: Jianqing Fan Princeton University
Time: 2023-07-25 10:15-2023-07-25 12:00
Venue: Tencent Meeting ID: 224-646-685
Motivated by dependence among high-dimensional predictors in big data, we introduce a Factor Augmented Sparse Throughput (FAST) model that utilizes both latent factors and sparse idiosyncratic components for nonparametric regression. The FAST model bridges factor models on one end and sparse nonparametric models on the other end and allows us to choose features for neural prediction. It encompasses structured nonparametric models and covers parametric models such as LASSO model and principal component regression. We introduced a special neural network architecture to train the model and showcase its applications in macroeconomic predictions, learning financial news sentiments, and asset pricing using firm's characteristics. The advantages of newly developed techniques are clearly demonstrated. For the last application, 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.
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 got elected to Academician from Academia Sinica in 2012.