{
  "title": "Hungary’s Corporate Sector Risk: A Machine Learning Approach",
  "publication": "Selected Issues Papers, August 13, 2024",
  "sourceUrl": "https://www.imf.org/en/publications/selected-issues-papers/issues/2024/08/12/hungarys-corporate-sector-risk-a-machine-learning-approach-553508",
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  "summary": "In recent years, Hungary’s non-financial corporations were confronted with multiple shocks, ranging from the pandemic and rising geopolitical tensions to the historic tightening of domestic monetary policy.",
  "sections": [
    {
      "heading": "Scope and methodology",
      "content": "- Examines determinants of Hungarian listed firms’ credit risk evolution over recent shocks using machine learning techniques.\n- Focus on interplay of firm-specific and macroeconomic drivers during: the pandemic, rising geopolitical tensions, and historic tightening of domestic monetary policy."
    },
    {
      "heading": "Key findings",
      "content": "- Both firm-specific and macroeconomic factors helped explain the observed rise in firms’ default probability at onset of the pandemic.\n- Hungarian corporates proved broadly resilient, with risk indicators quickly improving a year after the pandemic onset.\n- Firms’ credit risk rose again in 2022 as both long-term interest rates and sovereign risk premia sharply increased, despite continued improvements in firms’ financial ratios.\n- A significant portion of corporate loans are set to mature within the next few years and could be repriced at higher interest rates — a development that merits continued monitoring."
    },
    {
      "heading": "Subjects and keywords (as reported)",
      "content": "- Subjects: Business enterprises, Central bank policy rate, Corporate sector, COVID-19, Credit default swap, Credit risk, Economic sectors, Financial regulation and supervision, Financial services, Health, Long term interest rates, Machine learning, Market risk, Monetary policy, Monetary tightening, Money, Technology\n- Keywords: Business enterprises, Central bank policy rate, corporate sector, Corporate sector, COVID-19, COVID-19 pandemic, Credit default swap, credit risk, Credit risk, geopolitical tension, Hungary, Liquidity requirements, Loans, Long term interest rates, machine learning, Machine learning, Market risk, monetary policy, Monetary tightening, probability of default, sovereign risk\n\n---\n\n Content in this bundle\n\n- Hungary’s Corporate Sector Risk: A Machine Learning Approach\n  - Hungary’s Corporate Sector Risk: A Machine Learning Approach (Markdown version){rel=\"alternate\" type=\"text/markdown\"}\n  - Hungary’s Corporate Sector Risk: A Machine Learning Approach (PDF){rel=\"external\" type=\"application/pdf\"}\n\n---\n\nSource: https://www.imf.org/en/publications/selected-issues-papers/issues/2024/08/12/hungarys-corporate-sector-risk-a-machine-learning-approach-553508"
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    "Authors: Jakree Koosakul, Xuege Zhang",
    "Published: August 13, 2024",
    "Series: Selected Issues Papers",
    "DOI: https://doi.org/10.5089/9798400287916.018",
    "Examines determinants of Hungarian listed firms’ credit risk evolution over recent shocks using machine learning techniques.",
    "Focus on interplay of firm-specific and macroeconomic drivers during: the pandemic, rising geopolitical tensions, and historic tightening of domestic monetary policy.",
    "Both firm-specific and macroeconomic factors helped explain the observed rise in firms’ default probability at onset of the pandemic.",
    "Hungarian corporates proved broadly resilient, with risk indicators quickly improving a year after the pandemic onset.",
    "Firms’ credit risk rose again in 2022 as both long-term interest rates and sovereign risk premia sharply increased, despite continued improvements in firms’ financial ratios.",
    "A significant portion of corporate loans are set to mature within the next few years and could be repriced at higher interest rates — a development that merits continued monitoring.",
    "Subjects: Business enterprises, Central bank policy rate, Corporate sector, COVID-19, Credit default swap, Credit risk, Economic sectors, Financial regulation and supervision, Financial services, Health, Long term interest rates, Machine learning, Market risk, Monetary policy, Monetary tightening, Money, Technology",
    "Keywords: Business enterprises, Central bank policy rate, corporate sector, Corporate sector, COVID-19, COVID-19 pandemic, Credit default swap, credit risk, Credit risk, geopolitical tension, Hungary, Liquidity requirements, Loans, Long term interest rates, machine learning, Machine learning, Market risk, monetary policy, Monetary tightening, probability of default, sovereign risk",
    "**Hungary’s Corporate Sector Risk: A Machine Learning Approach**"
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