Journal of Credit Risk

Understanding and predicting systemic corporate distress: a machine-learning approach

Burcu Hacibedel and Ritong Qu

  • We propose a new definition for system-wide corporate distress events and provide a novel database of corporate distress events covering the last three decades and 55 advanced and emerging markets.
  • We find corporate distress generally is followed by slower economic growth. However, not all corporate distress events lead to systemic corporate nor financial crises.
  • We construct a machine-learning based model to forecast systemic corporate distress with a forecast horizon of 4-quarters. The early-warning system generates timely warnings and identifies sources of vulnerabilities using Shapley value decomposition.

evel probabilities of default, covering 55 economies and spanning the last three decades. Systemic corporate distress is identified by elevated probabilities of default across a large portion of the firms in an economy. A machine-learning-based earlywarning system is constructed to predict the risk of systemic 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 gross domestic product. Their impacts are milder than those of financial crises.

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