We develop an approach to determine whether a particular predictor represents a proxy for fundamental risk. We build on the assumption that risk-based predictors should be linked to new information about economic conditions. We show that most predictors forecast returns on either days with macroeconomic announcements or the remaining days, indicating that sources of return predictability differ across predictors: few are driven by fundamental risk; most have other origins. We show that Shiller’s excess volatility is confined to non-announcement days, suggesting that the ability to forecast stock market’s noise component underlies much of the predictability documented in the literature.

We demonstrate that a common global model predicts stock returns more effectively than local models estimated individually for each country, even when the global model excludes local data. We introduce a ``generalized elastic net" (GENet) to estimate a combined global-and-local model, showing theoretically and empirically that it efficiently (i) transfers information from global data to local countries and (ii) detects unique local components. The resulting model is 94% global---nearly the same function predicts stock returns across countries and over the past century. These findings have broad implications for asset pricing, highlighting the stability of the stochastic discount factor as a function of characteristics.

We employ a novel, high-dimensional global news corpus (GDELT) to examine the effects of news-based sentiment on international equity market returns. Leveraging over 520 million articles from 14 developed countries - classified by theme, source, and target country - we construct granular sentiment linkages that capture how one country’s media reports on another. Using machine learning, we test the predictive power of local and cross-country sentiment measures for equity market returns and their potential to drive profitable trading strategies. Our analysis reveals that cross-country sentiment linkages are pivotal for almost all included countries, while the influence of local sentiment is primarily confined to North America and Europe. Overall, these findings demonstrate how global sentiment patterns reflect partial market integration and play an important role in asset pricing.