Data-Driven Strategies for Early Detection of Corporates’ Financial Distress

Abstract

[EN] Scholars have taken a keen interest in predicting corporate crises in the past decades. However, most studies focused on classical parametric models that, by their nature, can consider few predictors and interactions and must respect numerous assumptions. Over the past few years, the economy has faced a severe structural crisis that has resulted in significantly lower income, cash, and capital levels than in the past. This crisis has led to insolvency and bankruptcy in many cases. Hence, there is a renewed interest in research for new models for forecasting business crises using novel advanced statistical learning techniques. The study shows that using tree-based methods and hyper-parameters optimization leads to excellent results in terms of accuracy. Moreover, this approach allows us to automatically consider all possible interactions and discover relevant aspects never considered in past studies. This line of research provides fascinating results that can bring new knowledge into the reference literature.