| dc.description.abstract | We perform a comparative analysis of two machine learning methods to predict corporate
bond return in the US bond market. In contrast to previous studies, we find that the most
influential variables are associated with size risk and past return. However, credit and liquidity
risks are more prominent when negative externalities impact the market. Further, high
predictability at short horizons combined with the investment strategy employed translates
into highly significant alphas. We identify the best-performing method to be a decisiontree-
based model utilizing boosting. The out-of-sample performance for this method remains
statistically significant after accounting for transaction costs. | en_US |
