Articlehybrid2020

A two‐stage Bayesian network model for corporate bankruptcy prediction

Yi Cao · Xiaoquan Liu · Jia Zhai · Shan Hua

50 citations0/0 files storedInternational Journal of Finance & Economics

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Financial Distress and Bankruptcy Prediction Corporate Insolvency and Governance Forecasting Techniques and Applications

Abstract

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.

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References

1968FINANCIAL RATIOS, DISCRIMINANT ANALYSIS AND THE PREDICTION OF CORPORATE BANKRUPTCY 1980Financial Ratios and the Probabilistic Prediction of Bankruptcy 1984Methodological Issues Related to the Estimation of Financial Distress Prediction Models 2001Forecasting Bankruptcy More Accurately: A Simple Hazard Model 2008In Search of Distress Risk 2008Forecasting Default with the Merton Distance to Default Model 2018How Valuable Is FinTech Innovation?2004Bankruptcy Prediction with Industry Effects

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