{
  "title": "Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach",
  "publication": "IMF Working Papers, July 29, 2022",
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  "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.",
  "sections": [
    {
      "heading": "Scope and Data",
      "content": "- Study covers firm-level probabilities of default (PD) across 55 economies.\n- Time span: the last three decades.\n- Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy.\n- A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time."
    },
    {
      "heading": "Key Findings",
      "content": "- Predictors of systemic corporate distress:\n  - Credit expansion.\n  - Monetary policy tightening.\n  - Overvalued stock prices.\n  - Debt-linked balance-sheet weaknesses.\n- Associations of systemic corporate distress events:\n  - Associated with contractions in GDP and credit growth in advanced and emerging markets.\n  - Contractions associated with distress are milder than those associated with financial crises."
    },
    {
      "heading": "Methodology and Components (as described)",
      "content": "- Use of firm-level PD indices to identify distress events.\n- Machine-learning model used for one-year-ahead early warning.\n- Appendices referenced in keywords: appendix B constructing predictor, appendix C machine learning model."
    },
    {
      "heading": "Publication and Metadata",
      "content": "- Authors: Burcu Hacibedel, Ritong Qu\n- Date: July 29, 2022\n- Pages: 48\n- Series: Working Paper No. 2022/153\n- Volume: 2022\n- Issue: 153\n- DOI: https://doi.org/10.5089/9798400216299.001\n- Stock No: WPIEA2022153\n- ISBN: 9798400216299\n- ISSN: 1018-5941\n\nSource: IMF Working Paper No. 2022/153 by Burcu Hacibedel and Ritong Qu, July 29, 2022.\n\n---\n\n Content in this bundle\n\n- Working Paper\n  - Working Paper (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Working Paper (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462"
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    "Authors: Burcu Hacibedel, Ritong Qu",
    "Published: July 29, 2022",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400216299.001",
    "Study covers firm-level probabilities of default (PD) across 55 economies.",
    "Time span: 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.",
    "Predictors of systemic corporate distress:",
    "Associations of systemic corporate distress events:",
    "Use of firm-level PD indices to identify distress events.",
    "Machine-learning model used for one-year-ahead early warning.",
    "Appendices referenced in keywords: appendix B constructing predictor, appendix C machine learning model.",
    "Authors: Burcu Hacibedel, Ritong Qu",
    "Date: July 29, 2022",
    "Pages: 48",
    "Series: Working Paper No. 2022/153",
    "Volume: 2022",
    "Issue: 153",
    "DOI: https://doi.org/10.5089/9798400216299.001",
    "Stock No: WPIEA2022153",
    "ISBN: 9798400216299",
    "ISSN: 1018-5941",
    "**Working Paper**"
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