A Machine Learning-based DSS for mid and long-term company crisis prediction

In the field of detection and prediction of company defaults and bankruptcy, significant effort has been devoted to evaluating financial ratios as predictors using statistical models and machine learning techniques. This problem becomes crucially important when financial decision-makers are provided with predictions on which to act, based on the output of prediction models. However, research has shown that such predictors are sufficiently accurate in the short-term, with the focus mainly directed towards large and medium-large companies. In contrast, in this paper, we focus on mid- and long-term bankruptcy prediction (up to 60 months) targeting small and/or medium enterprises. The key contribution of this study is a substantial improvement of the prediction accuracy in the short-term (12 months) using machine learning techniques, compared to the state-of-the-art, while also making accurate mid- and long-term predictions (measure of the area under the ROC curve of 0.88 with a 60 month prediction horizon). Extensive computational tests on the entire set of companies in Italy highlight the efficiency and accuracy of the developed method, as well as demonstrating the possibility of using it as a tool for the development of strategies and policies for entire economic systems. Considering the recent COVID-19 pandemic, we show how our method can be used as a viable tool for large-scale policy-making.

Automated summary

In this paper, the focus is on improving the accuracy of bankruptcy prediction for small and/or medium enterprises in the short term using machine learning techniques, while also making accurate mid- and long-term predictions. Extensive computational tests in Italy demonstrate the efficiency and accuracy of the developed method, showing its potential as a tool for developing strategies and policies for the entire economic system. Additionally, the study discusses how the method can be utilized for large-scale policy-making in the context of the recent COVID-19 pandemic.