What drives stock returns across countries? Insights from machine learning models
用机器学习分析多个市场特征，发现长期反转、动量、盈利收益率和市场规模等少数变量能解释大部分跨国股票回报差异，而国家风险影响较小。
We employ machine learning techniques to examine cross-sectional variation in country equity returns by aggregating information across multiple market characteristics. Our models reveal significant return predictability, which translates into discernible patterns in portfolio performance. In addition, variable importance analysis uncovers a sparse factor structure that varies across forecast horizons. A handful of critical predictors—such as long-term reversal, momentum, earnings yield, and market size—capture most of the return differences, while country risk measures play a minor role. Consistent with the partial segmentation perspective, return predictability persists in small, illiquid, and unintegrated markets and weakens over time as the constraints on capital mobility diminish. As a result, attempts to forge them into profitable strategies can be challenging at best. • Machine learning uncovers significant predictability in country equity returns • Critical predictors include long-term reversal, momentum, earnings yield, and market size. • Country risk measures have a much smaller impact on return predictability. • Predictability persists in small, illiquid, and unintegrated markets. • Challenges exist in translating predictability into profitable strategies.