Predicting Individual Corporate Bond Returns

35 Pages Posted: 30 Jun 2021

See all articles by Xin He

Xin He

City University of Hong Kong (CityUHK) - College of Business

Guanhao Feng

City University of Hong Kong (CityUHK)

Junbo Wang

Dept. of Economics and Finance, City Univ. of HK

Chunchi Wu

SUNY at Buffalo - School of Management

Date Written: June 19, 2021

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.

Keywords: Bond Characteristics, Individual Corporate Bonds, Machine Learning, Private Company Bonds, Return Decomposition, Return Predictability

JEL Classification: C1, G1

Suggested Citation

He, Xin and Feng, Guanhao and Wang, Junbo and Wu, Chunchi, Predicting Individual Corporate Bond Returns (June 19, 2021). Available at SSRN: https://ssrn.com/abstract=3870306 or http://dx.doi.org/10.2139/ssrn.3870306

Xin He

City University of Hong Kong (CityUHK) - College of Business ( email )

83 Tat Chee Avenue
Academic Building (LAU)
Kowloon Tong, 12200
Hong Kong

Guanhao Feng (Contact Author)

City University of Hong Kong (CityUHK) ( email )

83 Tat Chee Avenue
Kowloon Tong
Hong Kong

Junbo Wang

Dept. of Economics and Finance, City Univ. of HK ( email )

83 Tat Chee Ave., Kowloon Tong
Kowloon Town
Kowloon, 220
Hong Kong
34429492 (Phone)
852-2788-8806 (Fax)

Chunchi Wu

SUNY at Buffalo - School of Management ( email )

Jacobs Management Center
Buffalo, NY 14222
United States

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