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Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model

Author

Listed:
  • Mr. Jorge A Chan-Lau
  • Ruofei Hu
  • Luca Mungo
  • Ritong Qu
  • Weining Xin
  • Cheng Zhong

Abstract

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. This function integrates high-frequency, market-based, aggregate distress signals with low-frequency, firm-level financial ratios, and macroeconomic indicators. When provided with private firms' financial ratios, the model, which we name signal-knowledge transfer learning model (SKTL), transfers insights gained from 35 thousand publicly-traded firms to more than 4 million private-held ones and performs well as an ordinal measure of privately-held firms' default risk.

Suggested Citation

  • Mr. Jorge A Chan-Lau & Ruofei Hu & Luca Mungo & Ritong Qu & Weining Xin & Cheng Zhong, 2024. "Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model," IMF Working Papers 2024/115, International Monetary Fund.
    • Handle: RePEc:imf:imfwpa:2024/115
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