A Machine Learning-based DSS for mid and long-term company crisis prediction
The paper presents a study that analyzes financial information on Italian companies from 2001 to 2018, using the AIDA database. The data set includes bankrupted companies that had revenues between 1 million to 40 million euros in at least one of the last 5 years of life before they went bankrupt and a company lifetime of at least 10 years, as well as active companies that had similar revenues in the last 5 years. The data set contains more than 8959 companies and 15 most important financial features. The study applies feature reduction, missing value imputation, and standard scaling to the data set. Finally, the study evaluates the performance of a machine learning classifier, adopting AUC as the metric used in previous works. The imbalanced nature of the data set is addressed by sampling active companies in a controlled way. The study aims to predict bankruptcies of companies, with the focus on the recall of the confusion matrix, as finding all companies that most likely will declare ...