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|a 1018-5941
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|a Hacibedel, Burcu.
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|a Understanding and Predicting Systemic Corporate Distress :
|b A Machine-Learning Approach /
|c Burcu Hacibedel, Ritong Qu.
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|a Washington, D.C. :
|b International Monetary Fund,
|c 2022.
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|a 1 online resource (48 pages)
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|a IMF Working Papers
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|a <strong>Off-Campus Access:</strong> No User ID or Password Required
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|a <strong>On-Campus Access:</strong> No User ID or Password Required
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|a Electronic access restricted to authorized BRAC University faculty, staff and students
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|a 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.
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|a Mode of access: Internet
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|a Econometric and Statistical Methods
|2 imf
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|a Financial Crises
|2 imf
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|a Financial Forecasting and Simulation
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|a Financial Markets and the Macroeconomy
|2 imf
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|a Qu, Ritong.
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|a IMF Working Papers; Working Paper ;
|v No. 2022/153
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|z Full text available on IMF
|u https://elibrary.imf.org/openurl?genre=journal&issn=1018-5941&volume=2022&issue=153
|z IMF e-Library
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