Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach
使用27个宏观经济、股票和债券预测变量，发现迭代组合模型能显著提高公司债券收益的可预测性，且低等级债券溢价对经济周期有强预测力。
Using a comprehensive return data set and an array of 27 macroeconomic, stock, and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability generated by the iterated combination is both statistically and economically significant. Stock market and macroeconomic variables play an important role in forming expected bond returns. Return forecasts are closely linked to the evolution of real economy. Corporate bond premia have strong predictive power for business cycle, and the primary source of this predictive power is from the low-grade bond premium. The Internet appendix is available at https://doi.org/10.1287/mnsc.2017.2734 . This paper was accepted by Lauren Cohen, finance.