Different Strokes: Return Predictability Across Stocks and Bonds with Machine Learning and Big Data

81 Pages Posted: 17 Sep 2020 Last revised: 19 Feb 2021

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 investigate the return predictability across stocks and 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 stock and bond returns. Although both stock and bond characteristics provide strong forecasting power for both stock and bond returns, stock (bond) characteristics do not offer significant incremental predictive power above and beyond bond (stock) characteristics in predicting bond (stock) returns. The results also indicate that stock (bond) characteristics are cash flow (discount rate) predictors and stock (bond) return predictability is driven by mispricing (risk) phenomenon.

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, Different Strokes: Return Predictability Across Stocks and Bonds with Machine Learning and Big Data (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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