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

July 29, 2022

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Burcu Hacibedel, and Ritong Qu. Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach, (USA: International Monetary Fund, 2022) accessed December 21, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Summary

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.

Subject: Banking crises, Corporate sector, Credit, Economic sectors, Financial crises, Financial statements, Money, Public financial management (PFM)

Keywords: Appendix B constructing predictor, Appendix C machine learning model, Balance-sheet weakness, Banking crises, Corporate sector, Credit, Distress events, Early warning systems, Financial statements, Global, Macroprudential policy, Nonfinancial sector, PD indices, Probability of default

Publication Details

Pages:

48
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2022/153
Stock No:

WPIEA2022153
ISBN:

9798400216299
ISSN:

1018-5941

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