A0529
Title: Structural deep learning in conditional asset pricing
Authors: Yuan Liao - Rutgers University (United States) [presenting]
Andreas Neuhierl - Washington University in St. Louis (United States)
Jianqing Fan - Princeton University (United States)
Tracy Ke - Harvard University (United States)
Abstract: New structural nonparametric methods are developed 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. The 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.
