26 June 2024 Financial Analysts Journal Volume 80, Issue 3

Predicting Corporate Bond Illiquidity via Machine Learning

  1. Axel Cabrol, CFA
  2. Wolfgang Drobetz
  3. Tizian Otto
  4. Tatjana Puhan
Testing the predictive performance of machine-learning methods in estimating the illiquidity of US corporate bonds, this study finds that such techniques outperform most non-machine-learning-based models.
Read the Complete Article in the Financial Analysts Journal CFA Institute Member Content


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.

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