Predicting Corporate Bond Illiquidity via Machine Learning
测试了机器学习方法预测美国公司债券非流动性的效果，发现其优于传统历史流动性基准，其中梯度提升回归树表现最佳，且捕捉预测变量间的非线性效应和交互作用能进一步提升预测性能。
This paper tests the predictive performance of machine learning methods in estimating the illiquidity of US corporate bonds. Machine learning techniques outperform the historical illiquidity-based approach, the most commonly applied benchmark in practice, from both a statistical and an economic perspective. Gradient-boosted regression trees perform particularly well. Historical illiquidity is the most important single predictor variable, but several fundamental and return- as well as risk-based covariates also possess predictive power. Capturing nonlinear effects and interactions among these predictors further enhances forecasting performance. For practitioners, the choice of the appropriate machine learning model depends on the specific application.