Assessing Corporate Performance in Latin America : The Potency of Logistic Regression and Random Forest for Predicting Financial Distress

Latin America is a growing financial market. The performance of its private sector corporations is critical, as inadequate performance and financial distress can result in losses for many people. This study analyses the efficacy of Logistic Regression (LR) and Random Forest (RF) techniques for predicting corporate distress up to three years in advance. Results indicate that RF outperforms LR in terms of predictive power and error levels. The most effective predictors remained constant over the 20-year period but varied between the two models. The performance levels were unaffected by the COVID-19 pandemic