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, Cheng Zhong

June 7, 2024

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Format: Chicago

Jorge A Chan-Lau, Ruofei Hu, Luca Mungo, Ritong Qu, Weining Xin, and Cheng Zhong. Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model, (USA: International Monetary Fund, 2024) accessed December 3, 2024

Disclaimer: IMF Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

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. 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.

Subject: Credit risk, Debt default, External debt, Financial regulation and supervision, Financial sector policy and analysis, Financial statements, Public financial management (PFM), Solvency

Keywords: 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

Publication Details

Pages:

45
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2024/115
Stock No:

WPIEA2024115
ISBN:

9798400278396
ISSN:

1018-5941

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