AEA Papers and Proceedings
ISSN 2574-0768 (Print) | ISSN 2574-0776 (Online)
Emerging Markets at Risk
AEA Papers and Proceedings
vol. 110,
May 2020
(pp. 493–98)
Abstract
Policymakers would like to predict and mitigate the risks associated with the post-global financial crisis rise in corporate leverage in emerging markets. However, long-standing advanced-economy bankruptcy models fail to capture the idiosyncrasies that impact the solvency of emerging market firms. We study how a machine learning technique for variable selection, LASSO, can improve corporate distress risk models in emerging markets. Exploring the trade-off between model fit and predictive power, we find that larger models forecast distress with more accuracy during periods of economic stress (when global factors gain relevance), while more parsimonious specifications outperform during normal times.Citation
Asis, Gonzalo, Anusha Chari, and Adam Haas. 2020. "Emerging Markets at Risk." AEA Papers and Proceedings, 110: 493–98. DOI: 10.1257/pandp.20201007Additional Materials
JEL Classification
- E32 Business Fluctuations; Cycles
- G01 Financial Crises
- G32 Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
- G33 Bankruptcy; Liquidation
- E32 Business Fluctuations; Cycles
- G01 Financial Crises
- G32 Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill