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

Download or read book Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach written by Ms. Burcu Hacibedel and published by International Monetary Fund. This book was released on 2022-07-29 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.