A two‐stage Bayesian network model for corporate bankruptcy prediction

Yi Cao,Shan Hua,Xiaoquan Liu,Jia Zhai

doi:10.1002/ijfe.2162

Abstract

AbstractWe develop a Bayesian network (LASSO‐BN) model for firm bankruptcy prediction. We select financial ratios via the Least Absolute Shrinkage Selection Operator (LASSO), establish the BN topology, and estimate model parameters. Our empirical results, based on 32,344 US firms from 1961–2018, show that the LASSO‐BN model outperforms most alternative methods except the deep neural network. Crucially, the model provides a clear interpretation of its internal functionality by describing the logic of how conditional default probabilities are obtained from selected variables. Thus our model represents a major step towards interpretable machine learning models with strong performance and is relevant to investors and policymakers.

Highlights

Corporate bankruptcy is a serious issue in the financial market due to its damaging economic and social consequences
Empirical evidence suggests that default forecasting performance can be improved by selecting the most relevant variables via the least absolute shrinkage and selection operator (LASSO) (Tian et al, 2015); or including new heterogeneous features such as textual information (Mai et al, 2019); or employing complicated deep neural network models (Cerchiello et al, 2017), which consist of a number of layers, each armed with numerous hidden neurons, and exhibit strong capability in capturing the relationship between input variables and output bankruptcy forecasts
To implement the statistical structure learning algorithm, our first step is structure learning, that is, we identify the interactive relation between variables, specify the topology of the framework in order to construct a Bayesian network

Summary

| INTRODUCTION

Corporate bankruptcy is a serious issue in the financial market due to its damaging economic and social consequences. The Bayesian network is able to address what-if questions of an ad-hoc scenario, such as what could a firm do differently to achieve a better health status This allows us to construct bankruptcy probability surface by changing input variables in company financial statements. Given the clear internal functionality of the Bayesian network model, we are able to draw probability surfaces of variables of interest and perform sensitivity and scenario analyses to address what-if questions such as how bankruptcy probabilities change with regard to a particular input variable. We believe that this is the first ad-hoc scenario analysis in bankruptcy prediction.

| LITERATURE REVIEW

Findings

| CONCLUSION