A two‐stage Bayesian network model for corporate bankruptcy prediction

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Cao, Yi, Liu, Xiaoquan, Zhai, Jia and Hua, Shan (2020) A two‐stage Bayesian network model for corporate bankruptcy prediction. International Journal of Finance & Economics . ISSN 1076-9307

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Abstract

We develop a Bayesian network (LASSO-BN) model for firm bankruptcy prediction. We select fnancial 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.

Item Type: Article
Keywords: Bayesian network; LASSO; Accounting ratios; Sensitivity analysis; Interpretability analysis
Schools/Departments: University of Nottingham Ningbo China > Faculty of Business > Nottingham University Business School China
Identification Number: 10.1002/ijfe.2162
Depositing User: Wu, Cocoa
Date Deposited: 28 Aug 2020 01:19
Last Modified: 28 Aug 2020 01:19
URI: https://eprints.nottingham.ac.uk/id/eprint/61457

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