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

Burcu Hacibedel and Ritong Qu

Journal of Credit Risk

Abstract: 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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