A two-stage Bayesian network model for corporate bankruptcy prediction

Yi Cao, Xiaoquan Liu, Jia Zhai, Shan Hua

Research output: Journal PublicationArticlepeer-review

1 Citation (Scopus)
17 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)455-472
Number of pages18
JournalInternational Journal of Finance and Economics
Volume27
Issue number1
DOIs
Publication statusPublished - Jan 2022

Keywords

  • Bayesian network
  • LASSO
  • accounting ratios
  • interpretability analysis
  • sensitivity analysis

ASJC Scopus subject areas

  • Accounting
  • Finance
  • Economics and Econometrics

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