Bond Risk Premia with Machine Learning
84 Pages Posted: 26 Aug 2018 Last revised: 10 Feb 2020
Date Written: February 7, 2020
We show that machine learning methods, in particular extreme trees and neural networks (NNs), provide strong statistical evidence in favor of bond return predictability. NN forecasts based on macroeconomic and yield information translate into economic gains that are larger than those ob- tained using yields alone. Interestingly, the nature of unspanned factors changes along the yield curve: stock and labor market related variables are more relevant for short-term maturities, whereas output and income variables matter more for longer maturities. Finally, NN forecasts correlate with proxies for time-varying risk aversion and uncertainty, lending support to models featuring both of these channels.
Keywords: Machine Learning, Ensembled Networks, Forecasting, Bond Return Predictability, Em- pirical Asset Pricing
JEL Classification: C38, C45, C53, E43, G12, G17
Suggested Citation: Suggested Citation