Predicting Corporate Bond Illiquidity via Machine Learning
61 Pages Posted: 26 Jun 2023 Last revised: 11 Dec 2023
Date Written: December 7, 2023
This paper tests the predictive performance of machine learning methods in estimating the illiquid-ity of U.S. 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. Tree-based models and neural networks outperform linear regressions, which incorporate the same set of covariates. Gradient boosted regression trees perform particularly well. Historical illiquidity is the most important single predictor variable, but several fundametal and return- as well as risk-based covariates also possess predictive power. Capturing interactions and nonlinear effects among these predictors further enhances predictive performance.
Keywords: Illiquidity estimation, machine learning, corporate bonds
JEL Classification: G11, G12, C58, G17
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