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 propose a nonparametric method to test which characteristics provide independent 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...

We show that macroeconomic growth at the end of the year (fourth-quarter or December) strongly predicts the returns of the aggregate market, small- and large-cap stocks, portfolios sorted on book-to-market and dividend yields, bond returns, and international stock returns, whereas economic...

This paper uses factor analytic techniques for deriving factor realizations from a group of main economic indicators of both the German and the Turkish economy in order to test the effect of economic factors on asset returns in an APT framework. The factor structure of the German economy yields...

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 propose a theory that jointly accounts for an asset illiquidity and for the asset price potential over-reliance on public information. We argue that, when trading frequencies differ across traders, asset prices reflect investors' Higher Order Expectations (HOEs) about the two factors that...

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...

We examine the UK equity premium over more than a century using dividend growth to estimate expectations of capital gains employing the approach of Fama and French (2002). Over recent decades estimated equity premia implied by dividend growth have been much lower than that produced by average...