Applications of machine learning for corporate bond yield spread forecasting |
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| Affiliation: | 1. Graduate Program in Economics, Federal University of Santa Catarina, 88049-970 Florianopolis S.C., Brazil;2. Department of Economics, Federal University of Santa Catarina, 88049-970 Florianopolis S.C., Brazil;3. Department of Statistics, University of Brasilia, 70910-900 Brasilia, D.F., Brazil;4. Graduate Program in Business Administration, University of Brasilia, 70910-900 Brasilia D.F., Brazil;5. Graduate Program in Economics, Federal University of Espirito Santo, 29075-910 Vitoria E.S., Brazil;1. Shenzhen Central Sub-branch, The People’s Bank of China, Shenzhen, China;2. School of Finance, Nanjing University of Finance and Economics, Nanjing, China;3. Institute of Agricultural Economics and Development, Chinese Academy of Agricultural Sciences, Beijing, China |
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| Abstract: | 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. |
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| Keywords: | Equity volatility Forecasting Machine learning Yield spread |
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