Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

Burcu Hacibedel and Ritong Qu

No 2022/153, IMF Working Papers from International Monetary Fund

Abstract: In this paper, we study systemic non-financial corporate sector distress using firm-level probabilities of default (PD), covering 55 economies, and spanning the last three decades. Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy. A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time. Our results show that credit expansion, monetary policy tightening, overvalued stock prices, and debt-linked balance-sheet weaknesses predict corporate distress. We also find that systemic corporate distress events are associated with contractions in GDP and credit growth in advanced and emerging markets at different degrees and milder than financial crises.

Keywords: Nonfinancial sector; Probability of default; Early warning systems; Macroprudential policy; balance-sheet weakness; appendix B constructing predictor; distress events; appendix C machine learning model; PD indices; Corporate sector; Banking crises; Credit; Financial statements; Global (search for similar items in EconPapers)
Pages: 48
Date: 2022-07-29
New Economics Papers: this item is included in nep-big, nep-cmp, nep-fdg and nep-rmg
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