Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach

• 成果类型: Article
• 署名作者: Lin, Hai; Wu, Chunchi; Zhou, Guofu
• 署名单位: Victoria University Wellington; State University of New York (SUNY) System; University at Buffalo, SUNY; Washington University (WUSTL)
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2017.2734
• 发表日期: 2018
• 页码: 4218-4238
• 关键词: Predictability; Corporate bonds; iterated combination; Out-of-sample forecasts; utility gains

摘要

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.