Investigating bankruptcy prediction models in the presence of extreme class imbalance and multiple stages of economy

TL;DR

This paper evaluates various bankruptcy prediction models on a new public dataset, addressing class imbalance with resampling, and analyzes their performance across different economic stages, including crises.

Contribution

It compares eight well-known BPMs using a new dataset with resampling and examines their robustness during economic fluctuations.

Findings

Models perform differently during economic crises.

Resampling improves minority class prediction.

Tree-based ensemble techniques show promising results.

Abstract

In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.