Journal of Risk Model Validation

A gradient-boosting decision-tree approach for firm failure prediction: an empirical model evaluation of Chinese listed companies

Jiaming Liu and Chong Wu

  • Gradient boosting decision tree (GBDT) for firm failure prediction is proposed.
  • Sensitivity analysis and model interpretability of GBDT are analyzed and validated.
  • GDBT, bagging, Adaboost, Random Subspace and Random Forest are compared.
  • GBDT obtained the best results in terms of accuracy, precision, F-score, AUC.

Firm failure prediction is playing an increasingly important role in financial decision making. Ensemble methods have recently shown better classification performance than a single classifier, but the tree-based ensemble method for firm failure prediction has not been fully studied and remains to be further validated. Compared with other machine learning methods, it is more easily interpreted and requires little data preprocessing. In this paper, we employ a gradient-boosting decision-tree (GBDT) method to improve firm failure prediction and explain how to better analyze the relative importance of each financial variable. Because the GBDT deliberately adds new trees in order to correct errors made in previous steps, it has the potential to improve firm failure predictive performance. The influences of different parameters on model performance are analyzed in detail. Moreover, our proposed model is compared with four other popular ensemble methods. Our experimental results show that the GBDT outperforms these other methods in accuracy, precision, F -score and area under the curve. We therefore provide a full validation of GBDT, and believe that it is useful in controlling risk in financial risk management.

Sorry, our subscription options are not loading right now

Please try again later. Get in touch with our customer services team if this issue persists.

New to View our subscription options

If you already have an account, please sign in here.

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here