We use deep neural networks to estimate an asset pricing model for individual stock returns
that takes advantage of the vast amount of conditioning information, keeps a fully flexible …

We propose a nonparametric method to study which characteristics provide incremental
information for the cross-section of expected returns. We use the adaptive group LASSO to …

Monetary policy shocks have a large impact on stock returns in narrow windows around
press releases by the Federal Reserve. We use spatial autoregressions to decompose the …

Realistic heterogeneity in price rigidity interacts with heterogeneity in sectoral size and input-
output linkages in the transmission of monetary policy shocks. Quantitatively, heterogeneity …

We propose a novel framework for analyzing linear asset pricing models: simple, robust, and
applicable to high‐dimensional problems. For a (potentially misspecified) stand‐alone …

This paper compares various machine learning models to predict the cross-section of
emerging market stock returns. We document that allowing for non-linearities and …

We study markup cyclicality in a granular macroeconomic model with oligopolistic
competition. We characterize the comovement of firm, sectoral, and economy-wide markups …

We show that labor market frictions are first-order for understanding credit markets. Wage
growth and labor share forecast aggregate credit spreads and debt growth as well as or …

We develop a model in which customer capital depends on key talents' contribution and
pure brand recognition. Customer capital guarantees stable demand but is fragile to …

We find that manufacturing firms adopt more conservative capital structures in response to
the Nitrogen Oxides (NOx) Budget Trading Program (NBP) of 2004, a regional cap-and …