We provide a comprehensive study on the cross-sectional predictability of corporate bond returns using big data and machine learning. We examine whether a large set of equity and bond characteristics drive the expected returns on corporate bonds. Using either set of characteristics, we find that...

We investigate the cross-sectional determinants of corporate bond returns and find that downside risk is the strongest predictor of future bond returns. We also introduce common risk factors based on the prevalent risk characteristics of corporate bonds -- downside risk, credit risk, and...

I find that aggregate asset growth constructed from bottom-up data negatively predicts future market returns both in and out-of-sample and this result is robust across G7 countries. I further show that aggregate asset growth contains information about future market returns not captured by...

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the corporate bond market. We find a significantly positive intertemporal relation between expected return and risk in the bond market and the time-series predictability is driven by aggregate...

We provide time-series and cross-sectional evidence on the significance of a risk-return tradeoff in the bond and equity markets. We find a significantly positive intertemporal relation between expected return and risk in the bond market. We also propose novel measures of systematic and...