The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning

64 Pages Posted: 17 Sep 2020 Last revised: 21 Dec 2020

See all articles by Turan G. Bali

Turan G. Bali

Georgetown University - Robert Emmett McDonough School of Business

Amit Goyal

University of Lausanne; Swiss Finance Institute

Dashan Huang

Singapore Management University - Lee Kong Chian School of Business

Fuwei Jiang

Central University of Finance and Economics (CUFE)

Quan Wen

Georgetown University - Department of Finance

Date Written: July 24, 2020

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.

Keywords: machine learning, big data, corporate bond returns, cross-sectional return predictability

JEL Classification: G10, G11, C13

Suggested Citation

Bali, Turan G. and Goyal, Amit and Huang, Dashan and Jiang, Fuwei and Wen, Quan, The Cross-Sectional Pricing of Corporate Bonds Using Big Data and Machine Learning (July 24, 2020). Georgetown McDonough School of Business Research Paper No. 3686164, Swiss Finance Institute Research Paper No. 20-110, Available at SSRN: https://ssrn.com/abstract=3686164 or http://dx.doi.org/10.2139/ssrn.3686164

Turan G. Bali

Georgetown University - Robert Emmett McDonough School of Business ( email )

3700 O Street, NW
Washington, DC 20057
United States
(202) 687-5388 (Phone)
(202) 687-4031 (Fax)

HOME PAGE: https://sites.google.com/a/georgetown.edu/turan-bali

Amit Goyal (Contact Author)

University of Lausanne ( email )

Batiment Extranef 226
Lausanne, Vaud CH-1015
Switzerland
+41 21 692 3676 (Phone)
+41 21 692 3435 (Fax)

HOME PAGE: http://www.hec.unil.ch/agoyal/

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Dashan Huang

Singapore Management University - Lee Kong Chian School of Business ( email )

50 Stamford Road
Singapore, 178899
Singapore

HOME PAGE: http://dashanhuang.weebly.com/

Fuwei Jiang

Central University of Finance and Economics (CUFE) ( email )

39 South College Road
Haidian District
Beijing, Beijing 100081
China

Quan Wen

Georgetown University - Department of Finance ( email )

37th and O Street, NW
Washington D.C., DC 20057
United States

HOME PAGE: http://faculty.georgetown.edu/qw50

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