Predicting Corporate Bond Returns: Merton Meets Machine Learning

70 Pages Posted: 17 Sep 2020 Last revised: 25 Aug 2022

See all articles by Turan G. Bali

Turan G. Bali

Georgetown University - 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

McDonough School of Business, Georgetown University

Date Written: July 24, 2020

Abstract

We investigate the return predictability of corporate bonds using big data and machine learning. We find that machine learning models substantially improve the out-of-sample performance of stock and bond characteristics in predicting future bond returns. We also find a significant improvement in the performance of machine learning models when imposing a theoretically motivated economic structure from the Merton model, compared to the reduced-form approach without restrictions. Overall, our work highlights the importance of explicitly imposing the dependence between expected bond and stock returns via machine learning and Merton model when investigating expected bond returns.

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, Predicting Corporate Bond Returns: Merton Meets 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 - 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

McDonough School of Business, Georgetown University ( email )

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

HOME PAGE: http://quan-wen.facultysite.georgetown.edu/home

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