Deep Learning for Bond Yield Forecasting: The LSTM ‐ LagLasso
提出一种可解释的深度学习模型LSTM-LagLasso，用于预测债券收益率，通过特征选择和LSTM内部门控信号分析，揭示不同时间区间下预测的动态机制，实证表明其统计精度优于多层感知机。
ABSTRACT We present long short‐term memory (LSTM)‐LagLasso, a novel explainable deep learning approach applied to bond yield forecasting. Our method involves feature selection from a large universe of potential features and forecasts bond yields using dynamic LSTM networks. It examines the internal gating signals of a trained LSTM and explains their dynamics through exogenous variables that may influence bond price formation. By considering these variables at various lags and using the Lasso technique for feature selection, we demonstrate how different hidden units within the LSTM dynamically adjust to make predictions across different temporal regimes and how their evolution is shaped by various external factors. In an empirical study on government bond yield forecasting, we demonstrate the statistical accuracy of LSTM‐LagLasso compared to a multilayer perceptron (MLP) and highlight its explainability.