International corporate bond returns: Uncovering predictability using machine learning

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. • Corporate bond returns exhibit strong predictability across both U.S. and in non-U.S. markets. • The predictive performance of machine learning models declines after the onset of COVID-19. • Return predictability is notably stronger during periods of deteriorating business conditions, reduced market liquidity, elevated investor sentiment, and heightened risk aversion. • Downside risk and illiquidity play a more significant role in predicting returns in non-U.S. markets. • Developed economies demonstrate stronger cross-country bond market integration and greater bond–stock integration.