What Do Large Factor Models Learn? Self-Induced Regularization, Cost of Overfitting, and Self-Adaptivity

What Do Large Factor Models Learn? Self-Induced Regularization, Cost of Overfitting, and Self-Adaptivity Xiong, Xin This paper studies the out-of-sample performance of large, overparameterized linear factor models for stochastic discount factor (SDF) estimation. Motivated by recent advances in finance and machine learning, we analyze the all-inclusive ridge estimator that incorporates all candidate factors without ex-ante screening or dimension reduction. Our new non-asymptotic pricing error bou