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

Author

Listed:
  • Hai Lin

    (Victoria University of Wellington, Kelburn, Wellington 6012, New Zealand)

  • 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)

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.

Suggested Citation

  • Hai Lin & Chunchi Wu & Guofu Zhou, 2018. "Forecasting Corporate Bond Returns with a Large Set of Predictors: An Iterated Combination Approach," Management Science, INFORMS, vol. 64(9), pages 4218-4238, September.
    • Handle: RePEc:inm:ormnsc:v:64:y:2018:i:9:p:4218-4238
    DOI: 10.1287/mnsc.2017.2734
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    References listed on IDEAS

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