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 …

This paper studies whether the evident statistical predictability of bond risk premia translates
into economic gains for investors. We propose a novel estimation strategy for affine term …

We investigate the return predictability of corporate bonds using big data and machine
learning. We find that machine learning models substantially improve the out-of-sample …

Studies of bond return predictability find a puzzling disparity between strong statistical
evidence of return predictability and the failure to convert return forecasts into economic …

Common statistical measures of bond risk premia are volatile and countercyclical. This
paper uses survey data on interest rate forecasts to construct subjective bond risk premia …

Bond yields can be decomposed into expected short rates and term premiums. We directly
measure the former using all available US professional forecasts and obtain the latter as the …

We study risk premium in US Treasury bonds. We decompose Treasury yields into inflation
expectations and maturity-specific interest-rate cycles, which we define as variation in yields …

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 …

This article investigates the out-of-sample predictability of bond excess returns. We assess
the economic value of the forecasting ability of empirical models based on long-term forward …

A previous literature has documented that bond returns are predicted by macroeconomic
information not contained in yields contemporaneously. That literature has mostly relied on …