Extracting Information from the Corporate Yield Curve: A Machine Learning Approach

58 Pages Posted: 21 Nov 2016 Last revised: 30 Mar 2020

See all articles by Xu Guo

Xu Guo

State University of New York at Buffalo

Hai Lin

Victoria University of Wellington - School of Economics & Finance

Chunchi Wu

SUNY at Buffalo - School of Management

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School

Date Written: March 30, 2020

Abstract

We document strong evidence on the cross-sectional predictability of corporate bond returns based on 48 yield predictors that capture the information in the yield curve one to 48 months ahead. In addition to standard regression forecasts, we generate forecasts based on machine learning, which improves the forecast performance especially for junk bonds, and find that short- and long-term predictors are most informative. Return predictability is economically and statistically significant, and is robust to various controls. The pronounced bond anomaly uncovered in this paper joins a host of equity anomalies that challenge rational pricing models.

Keywords: Yield signals; moving averages; cross-sectional predictability; machine learning; corporate bond returns

JEL Classification: G12; G14

Suggested Citation

Guo, Xu and Lin, Hai and Wu, Chunchi and Zhou, Guofu, Extracting Information from the Corporate Yield Curve: A Machine Learning Approach (March 30, 2020). Available at SSRN: https://ssrn.com/abstract=2872382 or http://dx.doi.org/10.2139/ssrn.2872382

Xu Guo

State University of New York at Buffalo ( email )

Amherst, NY 14260
United States

Hai Lin (Contact Author)

Victoria University of Wellington - School of Economics & Finance ( email )

P.O. Box 600
Wellington 6001
New Zealand

Chunchi Wu

SUNY at Buffalo - School of Management ( email )

Jacobs Management Center
Buffalo, NY 14222
United States

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School ( email )

Washington University
Campus Box 1133
St. Louis, MO 63130-4899
United States
314-935-6384 (Phone)
314-658-6359 (Fax)

HOME PAGE: http://apps.olin.wustl.edu/faculty/zhou/

Here is the Coronavirus
related research on SSRN

Paper statistics

Downloads
622
Abstract Views
2,462
rank
46,934
PlumX Metrics