Predicting Individual Corporate Bond Returns

51 Pages Posted: 30 Jun 2021 Last revised: 6 Feb 2023

See all articles by Xin He

Xin He

Hunan University - College of Finance and Statistics; City University of Hong Kong (CityU)

Guanhao Feng

City University of Hong Kong (CityU)

Junbo Wang

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

Chunchi Wu

SUNY at Buffalo - School of Management

Date Written: January 31, 2023

Abstract

We use machine learning methods to find substantial evidence of return predictability and investment gains for public and private individual corporate bonds from 1976 to 2020. The return forecast-implied long-short and market-timing strategies deliver significant risk-adjusted returns net of the transaction cost. We find random forest outperforms all other methods because the ensemble of nonlinear trees helps reduce overfitting. Moreover, given the long history of our bond sample, we can evaluate macro predictors and find they contain more useful information than bond characteristics for the out-of-sample prediction. Finally, predictability differs between private and publicly-listed companies, with investment gains larger for private company bonds, which the literature has overlooked.

Keywords: Bond Characteristics; Machine Learning; Macro Predictors; Return Predictability; Private Bonds

JEL Classification: C55, C58, G0, G1, G17.

Suggested Citation

He, Xin and Feng, Guanhao and Wang, Junbo and Wu, Chunchi, Predicting Individual Corporate Bond Returns (January 31, 2023). Available at SSRN: https://ssrn.com/abstract=3870306 or http://dx.doi.org/10.2139/ssrn.3870306

Xin He

Hunan University - College of Finance and Statistics ( email )

109th Shijiachong Road, Yuelu District
Changsha, Hunan 410006
China

HOME PAGE: http://www.xinhesean.com

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
Kowloon
Hong Kong

Guanhao Feng (Contact Author)

City University of Hong Kong (CityU) ( email )

83 Tat Chee Avenue
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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