Monitoring Privately-held Firms' Default Risk in Real Time: A Signal-Knowledge Transfer Learning Model
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, a...
| Main Author: | |
|---|---|
| Other Authors: | , , |
| Format: | eBook |
| Language: | English |
| Published: |
Washington, D.C.
International Monetary Fund
2024
|
| Series: | IMF Working Papers
|
| Subjects: | |
| Online Access: | |
| Collection: | International Monetary Fund - Collection details see MPG.ReNa |
| 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 |
|---|---|
| Physical Description: | 45 pages |
| ISBN: | 9798400278396 |