Before revision: We estimate a general non-linear asset pricing model  with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information.
After revision: We propose a novel approach to estimate asset pricing models for individual stock returns that takes advantage of the vast amount of conditioning information, while keeping a fully flexible form and accounting for time-variation. Our general non-linear asset pricing model with deep neural networks applied to all U.S. equity data combined with a substantial set of macroeconomic and firm-specific information.