Bond Risk Premia with Machine Learning

Abstract

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 obtained 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, Empirical Asset Pricing

JEL Classification: C38, C45, C53, E43, G12, G17