Predicting financial distress in Latin American companies: A comparative analysis of logistic regression and random forest models

Flavio Barboza and Edward Altman

The North American Journal of Economics and Finance, 2024, vol. 72, issue C

Abstract: Latin America represents a growing financial market. The performance of its private sector corporations is critical, as inadequate performance and financial distress can lead to significant losses for many stakeholders. This study assesses the efficacy of Logistic Regression (LR) and Random Forest (RF) techniques in predicting corporate distress up to three years in advance. Additionally, we discuss relevant indicators and compare our findings in two different scenarios (pre versus pandemic period). The results indicate that RF outperforms LR in terms of predictive power and error levels. The most effective predictors remained consistent over the 20-year period but varied between the two models. Importantly, the performance levels remained unaffected by the COVID-19 pandemic.

Keywords: Distress prediction; Corporate default; Credit risk; Random forest; Logistic regression (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:72:y:2024:i:c:s1062940824000834

DOI: 10.1016/j.najef.2024.102158

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