{
  "title": "Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model",
  "publication": "IMF Working Papers, June 7, 2024",
  "sourceUrl": "https://www.imf.org/en/publications/wp/issues/2024/06/07/monitoring-privately-held-firms-default-risk-in-real-time-a-signal-knowledge-transfer-549982",
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  "summary": "We develop a mixed-frequency, tree-based, gradient-boosting model designed to assess the default risk of privately held firms in real time. The model uses data from publicly-traded companies to construct a probability of default (PD) function.",
  "sections": [
    {
      "heading": "Model design and methodology",
      "content": "- Developed a mixed-frequency, tree-based, gradient-boosting model named the signal-knowledge transfer learning model (SKTL).\n- Constructs a probability of default (PD) function by integrating:\n  - High-frequency, market-based, aggregate distress signals.\n  - Low-frequency, firm-level financial ratios.\n  - Macroeconomic indicators.\n- Uses transfer learning: insights from publicly-traded firms are transferred to privately-held firms when private firms' financial ratios are provided."
    },
    {
      "heading": "Data sources and transfer approach",
      "content": "- Trains on data drawn from publicly-traded companies.\n- Transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones.\n- Mixed-frequency design allows combining market-based signals (high-frequency) with accounting and macro indicators (low-frequency)."
    },
    {
      "heading": "Performance and validation",
      "content": "- The model performs well as an ordinal measure of privately-held firms' default risk.\n- SKTL provides real-time monitoring capability by leveraging high-frequency distress signals alongside traditional financial ratios and macro indicators."
    },
    {
      "heading": "Key findings and implications",
      "content": "- Transfer learning enables the extension of market-information-driven PD estimates to a much larger universe of privately-held firms.\n- The SKTL framework bridges information gaps between publicly-traded and privately-held firms, improving the ability to assess default risk in real time."
    },
    {
      "heading": "Relevant subject areas and keywords",
      "content": "- Subjects: Credit risk; Debt default; External debt; Financial regulation and supervision; Financial sector policy and analysis; Financial statements; Public financial management (PFM); Solvency.\n- Keywords include: Asia and Pacific; Corporate sector; Credit risk; Debt default; Default risk; Europe; Financial statements; Global; Gradient boosting; gradient-boosting model; held firm; North America; Privately-held firm; publicly-traded firm; signal-knowledge transfer learning model; Solvency; Transfer learning; transfers insight.\n\nSource: IMF Working Paper \"Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model\" by Jorge A Chan-Lau, Ruofei Hu, Luca Mungo, Ritong Qu, Weining Xin, and Cheng Zhong, June 7, 2024.\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/2024/06/07/monitoring-privately-held-firms-default-risk-in-real-time-a-signal-knowledge-transfer-549982"
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    "Authors: Jorge A Chan-Lau, Ruofei Hu, Luca Mungo, Ritong Qu, Weining Xin, Cheng Zhong",
    "Published: June 7, 2024",
    "Series: IMF Working Papers",
    "DOI: https://doi.org/10.5089/9798400278396.001",
    "Developed a mixed-frequency, tree-based, gradient-boosting model named the signal-knowledge transfer learning model (SKTL).",
    "Constructs a probability of default (PD) function by integrating:",
    "Uses transfer learning: insights from publicly-traded firms are transferred to privately-held firms when private firms' financial ratios are provided.",
    "Trains on data drawn from publicly-traded companies.",
    "Transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones.",
    "Mixed-frequency design allows combining market-based signals (high-frequency) with accounting and macro indicators (low-frequency).",
    "The model performs well as an ordinal measure of privately-held firms' default risk.",
    "SKTL provides real-time monitoring capability by leveraging high-frequency distress signals alongside traditional financial ratios and macro indicators.",
    "Transfer learning enables the extension of market-information-driven PD estimates to a much larger universe of privately-held firms.",
    "The SKTL framework bridges information gaps between publicly-traded and privately-held firms, improving the ability to assess default risk in real time.",
    "Subjects: Credit risk; Debt default; External debt; Financial regulation and supervision; Financial sector policy and analysis; Financial statements; Public financial management (PFM); Solvency.",
    "Keywords include: Asia and Pacific; Corporate sector; Credit risk; Debt default; Default risk; Europe; Financial statements; Global; Gradient boosting; gradient-boosting model; held firm; North America; Privately-held firm; publicly-traded firm; signal-knowledge transfer learning model; Solvency; Transfer learning; transfers insight.",
    "**Working Paper**"
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