Work

A two‐stage Bayesian network model for corporate bankruptcy prediction

Year: 2020

Type: article

Abstract: We 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.

Source: International Journal of Finance & Economics

DOI: 10.1002/ijfe.2162

Authors: Yi Cao, Xiaoquan Liu, Jia Zhai, Shan Hua

Institutions: University of Edinburgh, University of Nottingham Ningbo China, University of Nottingham, Xi’an Jiaotong-Liverpool University, University of Surrey

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $2,500

Funders: Henan University, National Office for Philosophy and Social Sciences