International corporate bond returns: Uncovering predictability using machine learning
使用国际数据集和机器学习方法，研究发现美国和全球公司债券收益存在强可预测性，且预测因素因市场而异，为债券定价和全球分散投资提供参考。
We examine the cross-sectional predictability of corporate bond returns using a novel international dataset and a set of machine learning techniques. We find strong predictability in both U.S. and non-U.S. markets, with differing predictive factors. Bonds in developed markets show greater integration with the U.S. market and stronger ties to equity markets. Predictive performance of machine learning models varies over time and is greater before the onset of the COVID-19 pandemic and during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. The results offer insights into bond pricing and global diversification opportunities.