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 outline a framework in which accounting “valuation anchors" could be connected to expected stock returns. Under two general conditions, expected log returns is a log- linear function of a valuation (market value-to-accounting) multiple and the expected growth in the valuation anchor. We...

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 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 select characteristics and to estimate how they affect expected returns nonparametrically. Our method can handle a large...

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...

Cross-predictability denotes the fact that some assets can predict other assets' returns. I propose a novel performance-based measure that disentangles the economic value of cross-predictability into two components: the predictive power of one asset's signal for other assets' returns...

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...

A great proportion of stock dynamics can be explained using publicly available information. The relationship between dynamics and public information may be of nonlinear character. In this paper we offer an approach to stock picking by employing so-called decision trees and applying them to XETRA...

We show empirically that survey-based measures of expected inflation are significant and strong predictors of future aggregate stock returns in several industrialized countries both in-sample and out-of-sample. By empirically discriminating between competing sources of this return predictability...