Model complexity and the performance of global versus regional models
利用线性与机器学习算法，基于24个发达市场数据，发现全球模型在算法复杂时显著优于区域模型，而线性方法下两者差异不显著。
We assess the predictive performance of global versus regional models in cross-sectional asset pricing using linear and machine learning algorithms. Based on data from 24 developed market countries, we find that the relative performance of globally versus locally trained models depends on algorithmic complexity. Regional models yield marginally higher long-short portfolio returns for linear methods, but spanning tests show no statistically significant alpha. In contrast, global models significantly outperform their local counterparts for more complex algorithms. These findings challenge earlier studies that rely on ex-post comparisons and linear methods, highlighting that global models outperform regional approaches when model complexity increases.