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Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
Author:
Jorge A Chan-Lau
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,
Ruofei Hu
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,
Luca Mungo
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https://orcid.org/0000-0002-5007-947X
,
Ritong Qu
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,
Weining Xin
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, and
Cheng Zhong
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Series:
IMF Working Papers
Volume/Issue:
Volume 2024: Issue 115
Language:
English
Publication Date:
07 Jun 2024
DOI:
https://doi.org/10.5089/9798400278396.001
ISBN:
9798400278396
ISSN:
1018-5941
Pages:
45
Keywords:
Default risk; Corporate sector; Privately-held firm; Gradient boosting; Transfer learning; signal-knowledge transfer learning model; publicly-traded firm; gradient-boosting model; transfers insight; held firm; Financial statements; Debt default; Solvency; Credit risk; Europe; North America; Global; Asia and Pacific
Legal-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.
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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.
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Cover IMF Working Papers
IMF Working Papers
ISSN:
1018-5941
Publisher:
International Monetary Fund