Forecasting Corporate Bond Returns: An Iterated Combination Approach

33 Pages Posted: 28 Oct 2013 Last revised: 23 Jun 2016

See all articles by Hai Lin

Hai Lin

Victoria University of Wellington - School of Economics & Finance

Chunchi Wu

State University of New York at Buffalo

Guofu Zhou

Washington University in St. Louis - John M. Olin Business School; China Academy of Financial Research (CAFR)

Date Written: June 21, 2016

Abstract

Using a comprehensive data set and an array of 27 macroeconomic, stock and bond predictors, we find that corporate bond returns are highly predictable based on an iterated combination model. The large set of predictors outperforms traditional predictors substantially, and predictability generated by the model is both statistically and economically significant. Stock market and macroeconomic variables play an important role in forming expected bond returns. Return forecasts are closely linked to the evolution of real economy. Corporate bond premia have strong predictive power for business cycle and the primary source of this predictive power is from the low-grade bond premium.

Keywords: Predictability; corporate bonds; out-of-sample forecasts; utility gains

JEL Classification: G12; G14

Suggested Citation

Lin, Hai and Wu, Chunchi and Zhou, Guofu, Forecasting Corporate Bond Returns: An Iterated Combination Approach (June 21, 2016). Available at SSRN: https://ssrn.com/abstract=2346299 or http://dx.doi.org/10.2139/ssrn.2346299

Hai Lin

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

P.O. Box 600
Wellington 6001
New Zealand

Chunchi Wu

State University of New York at Buffalo ( email )

L Street
Buffalo, NY, NY 14260

Guofu Zhou (Contact Author)

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/

China Academy of Financial Research (CAFR)

Shanghai Advanced Institute of Finance
Shanghai P.R.China, 200030
China

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