Predicting Individual Corporate Bond Returns

Xindi He, Guanhao Feng, Junbo Wang, Chunchi Wu
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引用次数: 5

Abstract

This paper finds positive evidence of return predictability and investment gains for individual corporate bonds for an extended period from 1973 to 2017. Our sample consists of both public and private company bond observations. We have implemented multiple machine learning methods and designed a Fama-Macbeth-type predictive performance evaluation. In addition to robust predictability evidence, there are four main findings. First of all, we find the lagged corporate bond market return as the most important predictor, suggesting a short-term market reversal story. Second, this paper concludes that equity information is conditionally redundant for similar public and private company bond performance. Third, a model-forecast-implied long-short strategy delivers 1.48% monthly returns and 1.4% alpha during the last two decades, which substantially drops if we do not consider private company bonds. Finally, the return predictability is mainly due to the cash flow component instead of the discount rate component.
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预测个别公司债券回报
本文发现了1973年至2017年期间个别公司债券的回报可预测性和投资收益的正证据。我们的样本包括公共和私人公司债券观察。我们实现了多种机器学习方法,并设计了fama - macbeth型预测性能评估。除了强有力的可预测性证据外,还有四个主要发现。首先,我们发现滞后的公司债券市场回报是最重要的预测因素,表明短期市场反转的故事。其次,本文得出股权信息对于类似的上市公司和非上市公司债券绩效具有条件冗余性的结论。第三,在过去20年里,模型预测隐含的多空策略带来了1.48%的月回报率和1.4%的阿尔法,如果不考虑私人公司债券,这个数字会大幅下降。最后,收益的可预测性主要取决于现金流量部分,而不是折现率部分。
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