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

Jorge Chan-Lau, Ruofei Hu, Luca Mungo, Ritong Qu, Weining Xin and Cheng Zhong

No 2024/115, IMF Working Papers from International Monetary Fund

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.

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 (search for similar items in EconPapers)
Pages: 45
Date: 2024-06-07
New Economics Papers: this item is included in nep-knm and nep-rmg
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.imf.org/external/pubs/cat/longres.aspx?sk=549982 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:imf:imfwpa:2024/115

Ordering information: This working paper can be ordered from
http://www.imf.org/external/pubs/pubs/ord_info.htm

Access Statistics for this paper

More papers in IMF Working Papers from International Monetary Fund International Monetary Fund, Washington, DC USA. Contact information at EDIRC.
Bibliographic data for series maintained by Akshay Modi ().