Bond Risk Premiums with Machine Learning
研究发现机器学习方法（尤其是极端树和神经网络）能显著预测债券回报，结合宏观经济和收益率信息的预测比仅用收益率带来更大经济收益，且不同期限债券受不同经济因素影响。
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 channels.