## Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach

_IMF Working Papers, July 29, 2022_

## Source details

**Canonical URL:** [Understanding and Predicting Systemic Corporate Distress: A Machine-Learning Approach](https://www.imf.org/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462)

## Other formats

- [Markdown version](/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462/index.md)
- [Structured JSON version](/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462/index.json)
- [Bundle manifest](/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462/bundle-manifest.json)

## Bibliographic details
- Authors: Burcu Hacibedel, Ritong Qu
- Published: July 29, 2022
- Series: IMF Working Papers
- DOI: https://doi.org/10.5089/9798400216299.001

---

### Scope and Data
- Study covers firm-level probabilities of default (PD) across 55 economies.
- Time span: the last three decades.
- Systemic corporate distress is identified by elevated PDs across a large portion of the firms in an economy.
- A machine-learning based early warning system is constructed to predict the onset of distress in one year’s time.

### Key Findings
- Predictors of systemic corporate distress:
  - Credit expansion.
  - Monetary policy tightening.
  - Overvalued stock prices.
  - Debt-linked balance-sheet weaknesses.
- Associations of systemic corporate distress events:
  - Associated with contractions in GDP and credit growth in advanced and emerging markets.
  - Contractions associated with distress are milder than those associated with financial crises.

### Methodology and Components (as described)
- Use of firm-level PD indices to identify distress events.
- Machine-learning model used for one-year-ahead early warning.
- Appendices referenced in keywords: appendix B constructing predictor, appendix C machine learning model.

### Publication and Metadata
- Authors: Burcu Hacibedel, Ritong Qu
- Date: July 29, 2022
- Pages: 48
- Series: Working Paper No. 2022/153
- Volume: 2022
- Issue: 153
- DOI: https://doi.org/10.5089/9798400216299.001
- Stock No: WPIEA2022153
- ISBN: 9798400216299
- ISSN: 1018-5941

*Source: IMF Working Paper No. 2022/153 by Burcu Hacibedel and Ritong Qu, July 29, 2022.*

---

## Content in this bundle

- **Working Paper**
  - [Working Paper (Markdown version)](/-/media/files/publications/wp/2022/english/wpiea2022153-print-pdf.pdf.md){rel="alternate" type="text/markdown"}
  - [Working Paper (PDF)](/-/media/files/publications/wp/2022/english/wpiea2022153-print-pdf.pdf){rel="external" type="application/pdf"}

---

_Source: https://www.imf.org/en/publications/wp/issues/2022/07/28/understanding-and-predicting-systemic-corporate-distress-a-machine-learning-approach-521462_