Predicting Individual Corporate Bond Returns

This paper documents substantial evidence of return predictability and investment gains for individual corporate bonds via machine learning. The forecast-implied long-short and market-timing strategies deliver significant risk-adjusted returns over transaction costs. Random Forest has the best performance as the ensemble of regression trees helps reduce overfitting. Using a long-span sample from 1976 to 2020, we can evaluate return predictability over business cycles and find aggregate predictors (e.g., corporate bond market return and TERM factor) that show higher forecasting power than bond characteristics. Finally, we find return predictability differs between bonds issued by private and public firms, with higher investment gains in private bonds