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

Author

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  • Ms. Burcu Hacibedel
  • Ritong Qu

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.

Suggested Citation

  • Ms. Burcu Hacibedel & Ritong Qu, 2022. "Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach," IMF Working Papers 2022/153, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2022/153
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