Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach Article Swipe
Burcu Hacıbedel
,
Ritong Qu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5089/9798400216299.001
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.5089/9798400216299.001
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 fina ncial crises.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5089/9798400216299.001
- https://www.elibrary.imf.org/downloadpdf/journals/001/2022/153/001.2022.issue-153-en.xml
- OA Status
- diamond
- Cited By
- 3
- References
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- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4290998497Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5089/9798400216299.001Digital Object Identifier
- Title
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Understanding and Predicting Systemic Corporate Distress: A Machine-Learning ApproachWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-07-01Full publication date if available
- Authors
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Burcu Hacıbedel, Ritong QuList of authors in order
- Landing page
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https://doi.org/10.5089/9798400216299.001Publisher landing page
- PDF URL
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https://www.elibrary.imf.org/downloadpdf/journals/001/2022/153/001.2022.issue-153-en.xmlDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://www.elibrary.imf.org/downloadpdf/journals/001/2022/153/001.2022.issue-153-en.xmlDirect OA link when available
- Concepts
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Systemic risk, Distress, Artificial intelligence, Computer science, Machine learning, Business, Psychology, Economics, Clinical psychology, Financial crisis, MacroeconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1, 2023: 1, 2022: 1Per-year citation counts (last 5 years)
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62Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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