This paper studies the relation between 36 firm-level characteristics and stock returns in 48 countries using instrumented principal components analysis. A non-U.S. country-neutral conditional factor model performs well in describing risk and returns and generates small and statistically insignificant anomaly intercepts when allowing for three or more latent factors. The non-U.S. model performs better in emerging than in developed markets, while showing substantial differences across countries. On average, only 10 characteristics significantly contribute to the models’ performance. Market beta, momentum, and firm size characteristics instrument for systemic exposure in U.S. and non-U.S. models, while investment and book-to-market do not. (JEL G11, G12, G14, G15)

中文翻译：

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公司特征和全球股票收益：有条件的资产定价模型

本文使用工具化主成分分析研究了 48 个国家的 36 个公司层面特征与股票收益之间的关系。非美国国家中性条件因子模型在描述风险和回报方面表现良好，并在允许三个或更多潜在因素时产生小的且统计上不显着的异常截距。非美国模式在新兴市场的表现优于在发达市场，但在不同国家/地区表现出显着差异。平均而言，只有 10 个特征对模型的性能有显着贡献。美国和非美国模型中系统性风险敞口的市场贝塔、动量和公司规模特征工具，而投资和账面市值比则不然。(JEL G11, G12, G14, G15)