We explore the dimensionality of stock returns in North America, Europe, Japan, Pacific, and Emerging Markets on the basis of 240 cross-sectional predictors. Our approach allows us to identify those predictors that are most consistently related to nonmicro-cap stock returns (i.e., independent of...

We demonstrate that firm-specific momentum profits are predictable across a wide range of international equity markets when combining information given in a multitude of stock characteristics. This predictor is comparatively simple to compute and can yield significant positive out-of-sample...

We examine the out-of-sample performance of 240 stock market anomalies enhanced by 49 machine learning algorithms and over 260 individually trained models across an international data sample of nearly 1.9 billion stock-month-anomaly observations from 1980 to 2019. We demonstrate significant...

We examine the value of analyst recommendations across 45 countries and 3.8 million firm-month observations from 1994 to 2019. Recommendation-based portfolio strategies lead to highly significant (insignificant) abnormal returns in international markets (in the U.S.). In line with...

Addressing recent calls by European regulatory and supervisory authorities, we develop a new bottom-up climate risk assessment method to examine the resilience of the European banking industry regarding transitory climate risks. We illustrate our approach by estimating the impact of a 50-100 EUR...

We study whether growth in the capital share (KS) of aggregate income (GDP) can explain equity portfolio returns in international stock markets as proposed by Lettau et al. (2019) for the U.S. market. We find that growth in local capital share has positive explanatory power for equity portfolio...

Applying large language models (GPT-3, Luminous, T5-XXL) to business descriptions of more than 79,000 firms, we construct business networks (BNs) of economically linked firms across the globe. We run multiple evaluation tasks and find that our networks are better suited to identify relevant...