Investors often focus their attention on recent information only, underestimating the rele-vance of information from the distant past. In consequence, the ordering of historical re-turns robustly predicts future stock performance in the cross-section. Using data from 49 countries, we...

We examine the cross-section of international equity risk premia with machine learning methods. We identify, classify, and calculate 88 market characteristics and use them to forecast country returns with various machine learning techniques. While all algorithms produce substantial economic...

Does factor momentum drive the stock price momentum? Inspired by the recent findings from the United States, we revisit this relationship across 51 markets. The factor momentum effect remains strong—both within and across countries—regardless of typical drivers of return predictability....

We study factor return seasonalities in international markets. Using up to 143 characteristic-sorted portfolios from 39 countries, we document a pervasive cross-sectional pattern: anomalies with a high average same-calendar month return outperform those with low average returns. The effect...

We conjecture that partially segmented stock indexes that are characterized by low correlation with the world market are mainly priced by local factors and should produce abnormal returns relative to a global asset-pricing model. This implies a negative relation between correlation and future...

We investigate the impacts of new COVID-19 infections on stock returns within China's unique zero-COVID policy framework. We document a remarkable negative pattern: a COVID-19 outbreak within a city adversely affects the performance of local firms in a nonlinear fashion. This effect intensifies...

We investigate the impacts of new COVID-19 infections on stock returns within China’s unique zero-COVID policy framework. We document a remarkable negative pattern: a COVID-19 outbreak within a city adversely affects the performance of local firms in a nonlinear fashion. This effect...