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A two‐stage Bayesian network model for corporate bankruptcy prediction

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  • Yi Cao
  • Xiaoquan Liu
  • Jia Zhai
  • Shan Hua

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.

Suggested Citation

  • Yi Cao & Xiaoquan Liu & Jia Zhai & Shan Hua, 2022. "A two‐stage Bayesian network model for corporate bankruptcy prediction," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(1), pages 455-472, January.
    • Handle: RePEc:wly:ijfiec:v:27:y:2022:i:1:p:455-472
    DOI: 10.1002/ijfe.2162
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    2. Martina Mokrišová & Jarmila Horváthová, 2023. "Domain Knowledge Features versus LASSO Features in Predicting Risk of Corporate Bankruptcy—DEA Approach," Risks, MDPI, vol. 11(11), pages 1-18, November.

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