Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach

Hai Lin, Chunchi Wu () and Guofu Zhou ()
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Chunchi Wu: State University of New York at Buffalo, Buffalo, New York 14228
Guofu Zhou: Olin School of Business, Washington University in St. Louis, St. Louis, Missouri 63130

Management Science, 2018, vol. 64, issue 9, 4218-4238

Abstract: Using a comprehensive return 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 iterated combination 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; iterated combination; out-of-sample forecasts; utility gains (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (44)

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