Junbo Wang, Louisiana State University
Project Description/Abstract
We introduce a generalized statistical learning method, sparse orthogonal factor regression (SOFAR), in testing linear factor models with both large numbers of factors and testing assets. Our approach encompasses most of the existing methods in the literature and can be used in many other scenarios with large data sets. Applying SOFAR, we can select the latent factors from the whole swath of 219 candidate factors proposed by the literature simultaneously, identify test assets associated with the selected latent factors, and interpret them. We can also select the latent factors and correlated characteristics in the IPCA framework without bootstrapping. Without firm characteristics instrumenting, we find that four latent factors (market, investment, intangible, and frictions) are relevant to the covariance of asset returns and three types of factors (profitability, asset liquidity, and liquidity bets) price assets in cross-section. We also find that the out-of-sample prediction for the asset pricing model can be more precise with candidate factor selections. With characteristics as instruments, we only identify one factor, and the correlated characteristics are beta, size, momentum, and liquidity.
Co-authors
Christopher Jones, University of Southern California
Jinchi Lv, University of Southern California
Kuntara Pukthuanthong, University of Missouri