Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach
July 29, 2022
Preview Citation
Format: Chicago
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
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
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Pages:
48
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Volume:
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DOI:
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Issue:
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Series:
Working Paper No. 2022/153
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Stock No:
WPIEA2022153
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ISBN:
9798400216299
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ISSN:
1018-5941