02093cas a2200301 a 4500 AALejournalIMF023023 230101c9999 xx r poo 0 0eng d 20.00 USD 9798400216299 1018-5941 BD-DhAAL BD-DhAAL Hacibedel, Burcu. Understanding and Predicting Systemic Corporate Distress : A Machine-Learning Approach / Burcu Hacibedel, Ritong Qu. Washington, D.C. : International Monetary Fund, 2022. 1 online resource (48 pages) IMF Working Papers <strong>Off-Campus Access:</strong> No User ID or Password Required <strong>On-Campus Access:</strong> No User ID or Password Required Electronic access restricted to authorized BRAC University faculty, staff and students 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. Mode of access: Internet Econometric and Statistical Methods imf Financial Crises imf Financial Forecasting and Simulation imf Financial Markets and the Macroeconomy imf Qu, Ritong. IMF Working Papers; Working Paper ; No. 2022/153 Full text available on IMF https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=153 IMF e-Library