The paper investigates the determinants of the idiosyncratic volatility puzzle by allowing linkages across asset returns. The first contribution of the paper is to show that portfolios sorted by increasing indegree computed on the network based on Granger causality test have lower expected...

We provide the first large-scale study of the performance of expected-return proxies (ERPs) internationally. Analyst-forecast-based ICCs are sparsely populated and not robustly associated with future returns. Earnings-model-forecast-based ICCs are well-populated, but are unreliable outside the...

We examine the connection between tail risk — as measured in Kelly and Jiang (2014) — and the cross-section of expected returns. In conditional predictive regression systems and vector-autoregressions of the market portfolio and the long- and shoresides of the Fama-French factor portfolios,...

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

We theoretically characterize the behavior of machine learning asset pricing models. We prove that expected out-of-sample model performance—in terms of SDF Sharpe ratio and average pricing errors—is improving in model parameterization (or “complexity”). Our results predict that the best...