Abstract

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 machine learning methods substantially improve the out-of-sample predictive power for bond returns, compared to the traditional linear regression models. While equity characteristics produce significant explanatory power for bond returns, their incremental predictive power relative to bond characteristics is economically and statistically insignificant. Bond characteristics provide as strong forecasting power for future equity returns as using equity characteristics alone. However, bond characteristics do not offer additional predictive power above and beyond equity characteristics when we combine both sets of predictors.

嘉宾介绍

姜富伟教授现任中央财经大学金融工程系主任，教授、博导、龙马青年学者，研究领域包括资产定价、行为金融、金融科技、国际金融等，在金融学国际国内顶级期刊Journal of Financial Economics、Review of Financial Studies、《金融研究》、《管理科学学报》等发表论文30余篇，主持北京市和国家自然科学基金项目4项。研究成果被ESI评为经济管理类全球前1%最高被引用论文，被《哈佛商业评论》、《清华金融评论》等转载，荣获亚洲金融协会最佳论文奖、国际财务管理协会最佳论文奖等众多国际学术奖励荣誉。担任国家自然科学基金通讯评审、教育部学位中心评审专家、英文SSCI来源期刊Annals of Economics and Finance编委和30多本中英文学术期刊评审。