Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
IMF Working Papers, June 7, 2024
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- Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
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Bibliographic details
- 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
Model design and methodology
- 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:
- High-frequency, market-based, aggregate distress signals.
- Low-frequency, firm-level financial ratios.
- Macroeconomic indicators.
- Uses transfer learning: insights from publicly-traded firms are transferred to privately-held firms when private firms' financial ratios are provided.
Data sources and transfer approach
- 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).
Performance and validation
- 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.
Key findings and implications
- 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.
Relevant subject areas and keywords
- 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.
Source: 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.
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