Applications of machine learning for corporate bond yield spread forecasting

Jong Min Kim, Dong H. Kim, Hojin Jung

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


This article considers nine different predictive techniques, including state-of-the-art machine learning methods for forecasting corporate bond yield spreads with other input variables. We examine each method's out-of-sample forecasting performance using two different forecast horizons: (1) the in-sample dataset over 2003–2007 is used for one-year-ahead and two-year-ahead forecasts of non-callable corporate bond yield spreads; and (2) the in-sample dataset over 2003–2008 is considered to forecast the yield spreads in 2009. Evaluations of forecasting accuracy have shown that neural network forecasts are superior to the other methods considered here in both the short and longer horizon. Furthermore, we visualize the determinants of yield spreads and find that a firm's equity volatility is a critical factor in yield spreads.

Original languageEnglish (US)
Article number101540
JournalNorth American Journal of Economics and Finance
StatePublished - Nov 1 2021

Bibliographical note

Publisher Copyright:
© 2021 Elsevier Inc.


  • Equity volatility
  • Forecasting
  • Machine learning
  • Yield spread


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