Predicting Corporate Bond Returns: Merton Meets Machine Learning

Abstract

We investigate the return predictability of corporate bonds using big data and machine learning. We find that machine learning models substantially improve the out-of-sample performance of stock and bond characteristics in predicting future bond returns. We also find a significant improvement in the performance of machine learning models when imposing a theoretically motivated economic structure from the Merton model, compared to the reduced-form approach without restrictions. Overall, our work highlights the importance of explicitly imposing the dependence between expected bond and stock returns via machine learning and Merton model when investigating expected bond returns.

Keywords: machine learning, big data, corporate bond returns, cross-sectional return predictability

JEL Classification: G10, G11, C13