We consider parametric portfolio policies of any complexity using deep neural networks to optimize investor utility. Risk aversion acts as an economic regularization mechanism, with higher risk aversion constraining model complexity. Empirically, Deep Parametric Portfolio Policies (DPPP)...

We generalize the parametric portfolio policy framework to learning portfolio weights via deep neural networks. We find that network-based portfolio policies result in an increase of investor utility of between 30 and 100 percent over a comparable linear portfolio policy, depending on whether...

We provide data and code that successfully reproduces nearly all cross-sectional stock return predictors. Unlike most metastudies, we carefully examine the original papers to determine whether our predictability tests should produce t-stats above 1.96. For the 180 predictors that were clearly...

A central question in designing optimal policies concerns the assignment of individuals with different observable characteristics to different treatments. We study this question in the context of increasing workers' performance by using targeted incentives based on measurable worker...

A central question in designing optimal policies concerns the assignment of individuals with different observable characteristics to different treatments. We study this question in the context of increasing workers' performance by using targeted incentives based on measurable worker...

A number of recent studies regress a “narratively” identified measure of a macroeconomic shock directly on an outcome variable. In this note, we argue that this approach can be viewed as the reduced-form regression of an instrumental variable approach in which the narrative time series is...